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690 lines
19 KiB
Text
690 lines
19 KiB
Text
---
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title: "Financial Marginalization and Cryptocurrency Payments"
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author: "Rucknium"
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date: '2022-05-26'
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output: html_document
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---
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```{css, echo=FALSE}
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body .main-container {
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max-width: 1480px !important;
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width: 1480px !important;
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}
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body {
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max-width: 1480px !important;
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}
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```
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE, size = "small", tidy = FALSE)
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options(width = 170)
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# or_glm function adapted from "oddsratio" package so it can
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# handle svyglm functions:
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or_svyglm <- function (data, model, incr, ci = 0.95)
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{
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if (any(class(model) %in% "glm")) {
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preds <- names(coefficients(model))[2:length(coefficients(model))]
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coef <- coefficients(model)[2:length(coefficients(model))]
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}
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increments <- list()
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odds_ratios <- list()
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ci_low <- list()
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ci_high <- list()
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for (i in preds) {
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if (any(class(model) %in% "glm")) {
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ci_list <- data.frame(suppressMessages(confint(model,
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level = ci)))[-1, ]
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}
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if (is.numeric(data[[i]]) | is.integer(data[[i]])) {
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odds_ratios[[i]] <- round(exp(as.numeric(coef[[i]]) *
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as.numeric(incr[[i]])), 3)
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if (!class(model)[1] == "glmmPQL") {
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ci_low[[i]] <- round(exp(ci_list[i, 1] * as.numeric(incr[[i]])),
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3)
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ci_high[[i]] <- round(exp(ci_list[i, 2] * as.numeric(incr[[i]])),
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3)
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}
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increments[[i]] <- as.numeric(incr[[i]])
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or <- odds_ratios[[i]]
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}
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else {
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odds_ratios[[i]] <- round(exp(as.numeric(coef[[i]])),
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3)
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if (!class(model)[1] == "glmmPQL") {
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ci_low[[i]] <- round(exp(ci_list[i, 1]), 3)
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ci_high[[i]] <- round(exp(ci_list[i, 2]), 3)
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}
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increments[[i]] <- "Indicator variable"
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or <- odds_ratios[[i]]
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}
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}
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result <- data.frame(predictor = names(odds_ratios), oddsratio = unlist(odds_ratios,
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use.names = FALSE), ci_low = unlist(ci_low, use.names = FALSE),
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ci_high = unlist(ci_high, use.names = FALSE), increment = unlist(increments,
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use.names = FALSE))
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if (any(class(model) %in% "glm")) {
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col_names <- gsub("\\.\\.", replacement = "", names(ci_list))
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col_names <- gsub("X", replacement = "", col_names)
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colnames(result)[3] <- paste0("ci_low (", col_names[1],
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")")
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colnames(result)[4] <- paste0("ci_high (", col_names[2],
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")")
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}
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return(result)
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}
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```
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## Load packages
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```{r load-packages}
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# install.packages("readstata13")
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# install.packages("survey")
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# install.packages("corrplot")
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library(readstata13)
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library(survey)
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library(corrplot)
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```
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## Download and import data
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```{r get-data}
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# download.file("https://www.federalreserve.gov/consumerscommunities/files/SHED_public_use_data_2021_(Stata).zip",
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# "data/SHED_public_use_data_2021_(Stata).zip")
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# unzip("data/SHED_public_use_data_2021_(Stata).zip", exdir = "data")
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SHED <- readstata13::read.dta13("data/public2021.dta", generate.factors = TRUE, nonint.factors = TRUE)
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# S16_b
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# In the past year, have you done the following
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# with cryptocurrency, such as Bitcoin or Ethereum?
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# - Used to buy something or make a payment
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SHED$crypto.used.as.payment <- SHED$S16_b == "Yes"
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# BK1
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# Do you and/or your spouse or partner
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# currently have a checking, savings or money
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# market account?
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SHED$lacks.bank.account <- SHED$BK1 == "No"
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# C2A
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# Do you currently have at least one credit card?
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SHED$lacks.credit.card <- SHED$C2A == "No"
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# ppgender
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# Gender [Ipsos source]
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SHED$is.male <- SHED$ppgender == "Male"
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# race_5cat Race 5 categories
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SHED$race <- SHED$race_5cat
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# ED0
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# What is the highest level of school you have
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# completed or the highest degree you have
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# received?
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SHED$education.level <- relevel(SHED$ED0, "High school degree or GED")
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# ED1
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# Which one of the following broad categories
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# best describes your (current/most recent)
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# educational program?
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SHED$education.subject <- relevel(SHED$ED1, "Business/management")
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# pppa_lgb [Ipsos source]
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# Q230: Which of the following best describes how you think of yourself?
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SHED$lgbtq <- relevel(SHED$pppa_lgb, "Straight, that is, not gay")
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# ppp20197 [Ipsos source]
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# QEG22: Are you a citizen of the United States?
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SHED$is.noncitizen <- SHED$ppp20197 == "No"
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# I40
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# Which of the following categories best
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# describes the total income that you and/or
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# your spouse or partner received from all
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# sources, before taxes and deductions, in the
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# past 12 months?
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SHED$income.category <- SHED$I40
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# B2
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# Overall, which one of the following best
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# describes how well you are managing
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# financially these days?
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SHED$overall.financial.wellbeing <- SHED$B2
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# I41_b
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# Supplemental Nutrition Assistance Program
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# (SNAP or food stamps) - In the past 12
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# months, have you received any of the
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# following?
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SHED$received.food.stamps <- SHED$I41_b
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# FL0
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# On a scale from zero to ten, where zero is
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# not at all willing to take risks and ten is
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# very willing to take risks, what number would
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# you be on the scale?
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SHED$risk.tolerance <- as.numeric(SHED$FL0) - 1
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# B10
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# Overall, on a scale from zero to ten, where
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# zero is not at all satisfied and ten is
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# completely satisfied, how satisfied are you
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# with life as a whole these days?
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SHED$life.satisfaction <- as.numeric(SHED$B10) - 1
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# ppage
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# Age [Ipsos source]
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# ppcmdate
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# Date member completed Core survey
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# Must correct age variable for time of initial Ipsos survey
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SHED$age <- SHED$ppage + (2021 - as.numeric(substr(SHED$ppcmdate, 1, 4)))
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# ind1
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# IND1: Industry (tight scale) in current or main job
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SHED$job.industry <- relevel(SHED$ind1, "Retail/Stores/Shopping (including Online Retail)")
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# ppcm0160 [Ipsos source]
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# Q26: Occupation (detailed) in current or main job
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SHED$job.occupation <- relevel(SHED$ppcm0160, "Retail Sales")
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# ppcm1301 [Ipsos source]
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# GOVEMP1: Employer type
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SHED$employer.type <- relevel(SHED$ppcm1301, "Private-for-profit company")
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# ppmsacat
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# MSA Status [Ipsos source]
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SHED$resides.in.metro.statistical.area <- SHED$ppmsacat == "Metro"
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# ppfs0596 [Ipsos source]
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# Q22: What is the approximate total amount of
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# your household's savings and investments?
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SHED$total.household.savings <- relevel(SHED$ppfs0596, "$100,000 - $249,999")
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# A1_a
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# In the past 12 months, has each of the following happened to you:
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# - Turned down for credit
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SHED$rejected.for.credit <- SHED$A1_a == "Yes"
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# BK2_a
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# In the past 12 months, did you and/or spouse or partner:
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# - Purchase a money order from a place other than a bank
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SHED$purchase.non.bank.money.order <- SHED$BK2_a == "Yes"
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# BK2_b
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# In the past 12 months, did you and/or spouse or partner:
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# - Cash a check at a place other than a bank
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SHED$cash.check.non.bank <- SHED$BK2_b == "Yes"
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# BK2_c
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# In the past 12 months, did you and/or spouse or partner:
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# - Take out a payday loan or payday advance
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SHED$take.payday.loan <- SHED$BK2_c == "Yes"
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# BK2_d
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# In the past 12 months, did you and/or spouse or partner:
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# - Take out a pawn shop loan or an auto title loan
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SHED$take.auto.or.pawn.shop.loan <- SHED$BK2_d == "Yes"
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# BK2_e
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# In the past 12 months, did you and/or spouse or partner:
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# - Obtain a tax refund advance to receive your refund faster
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SHED$take.tax.refund.advance <- SHED$BK2_e == "Yes"
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# BNPL1
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# In the past year, have you used a “Buy Now
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# Pay Later” service to buy something?
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SHED$used.buy.now.pay.later <- SHED$BNPL1 == "Yes"
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# ppfs1482 [Ipsos source]
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# Q108: Where do you think your credit score falls
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SHED$perceived.credit.score <- relevel(SHED$ppfs1482, "Fair")
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# GE2A
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# Some people earn money by selling items at
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# places like flea markets and garage sales or
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# through online marketplaces like eBay or
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# Etsy. In the past month, have you made money
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# by selling items in any of these ways?
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SHED$informal.selling.of.goods <- SHED$GE2A == "Yes"
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# GE1A
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# In the past month, have you done any
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# freelance or gig-work, either to supplement
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# your income or as your main job?
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SHED$freelance.or.gig.work <- SHED$GE1A == "Yes"
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# E7
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# During the past 12 months, have you
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# personally experienced discrimination or
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# unfair treatment because of your race,
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# ethnicity, age, religion, disability status,
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# sexual orientation, gender, or gender
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# identity?
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SHED$experienced.discrimination <- SHED$E7 == "Yes"
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# E8_b
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# In the past 12 months, did you personally experience
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# discrimination or unfair treatment while
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# doing any of the following?
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# - Banking or applying for a loan
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SHED$experienced.discrimination.in.banking <- SHED$E8_b %in% "Yes"
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# xlaptop
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# Is R a KP laptop user?
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SHED$is.kp.laptop.user <- SHED$xlaptop %in% "Yes"
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# devicetype2
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# DOV: Device Type - at the end of survey
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SHED$respondent.device.type <- relevel(SHED$devicetype2, "WinPC")
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SHED <- svydesign(ids = ~0, data = SHED, weights = SHED$weight_pop)
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# weight_pop used as survey weights, in accordance with suggestion by:
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# https://www.federalreserve.gov/consumerscommunities/files/SHED_2021codebook.pdf
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```
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## Checking correlations between main variables
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```{r initial-correlations}
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svyvar.covariance <- svyvar(~ age + is.male + crypto.used.as.payment +
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lacks.bank.account + lacks.credit.card, SHED, na.rm = TRUE)
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attr(svyvar.covariance, "var") <- NULL
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svyvar.correlation <- cov2cor(as.matrix(svyvar.covariance))
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print(round(svyvar.correlation, 3))
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corrplot(svyvar.correlation, tl.col = "darkred", tl.srt = 35,
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method = "shade", number.digits = 2, addshade = "all", diag = FALSE,
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title = "\n\n Correlation Matrix of Financial Marginalization\nand Use of Cryptocurrency as a Means of Payment",
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addCoef.col = "black", type = "lower")
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```
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# Main results
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## lacks.bank.account
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```{r main-results-lacks-bank-account}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lacks.bank.account,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## lacks.credit.card
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```{r main-results-lacks-credit-card}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lacks.credit.card,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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# Additional results on financial marginalization
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## rejected.for.credit
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```{r other-financial-marginalization-rejected-for-credit}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + rejected.for.credit,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## purchase.non.bank.money.order
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```{r other-financial-marginalization-purchase-non-bank-money-order}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + purchase.non.bank.money.order,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## cash.check.non.bank
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```{r other-financial-marginalization-cash-check-non-bank}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + cash.check.non.bank,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## take.payday.loan
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```{r other-financial-marginalization-take-payday-loan}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + take.payday.loan,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## take.auto.or.pawn.shop.loan
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```{r other-financial-marginalization-take-auto-or-pawn-shop-loan}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + take.auto.or.pawn.shop.loan,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## take.tax.refund.advance
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```{r other-financial-marginalization-take-tax-refund-advance}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + take.tax.refund.advance,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## used.buy.now.pay.later
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```{r other-financial-marginalization-used-buy-now-pay-later}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + used.buy.now.pay.later,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## experienced.discrimination.in.banking
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```{r other-financial-marginalization-experienced-discrimination-in-banking}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + experienced.discrimination.in.banking,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## perceived.credit.score
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```{r other-financial-marginalization-perceived-credit-score}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + perceived.credit.score,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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# General societal marginalization
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## experienced.discrimination
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```{r general-societal-marginalization-experienced-discrimination}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + experienced.discrimination,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## race
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```{r general-societal-marginalization-race}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + race,
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SHED, family = stats::quasibinomial(link = "logit"))
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summary(svyglm.fit)
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or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
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```
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## is.noncitizen
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```{r general-societal-marginalization-is.noncitizen}
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svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + is.noncitizen,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## lgbtq
|
|
|
|
```{r general-societal-marginalization-lgbtq}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lgbtq,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
# Data quality sanity checks
|
|
|
|
## education.subject
|
|
|
|
```{r data-quality-checks-education-subject}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + education.subject,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## job.industry
|
|
|
|
```{r data-quality-checks-job-industry}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + job.industry,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## job.occupation
|
|
|
|
```{r data-quality-checks-job-occupation}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + job.occupation,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## risk.tolerance
|
|
|
|
```{r data-quality-checks-risk-tolerance}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + risk.tolerance,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1, risk.tolerance = 1))
|
|
|
|
|
|
```
|
|
|
|
|
|
# Miscellaneous
|
|
|
|
## education.level
|
|
|
|
```{r miscellaneous-education-level}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + education.level,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## income.category
|
|
|
|
```{r miscellaneous-income-category}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + income.category,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## total.household.savings
|
|
|
|
```{r miscellaneous-total-household-savings}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + total.household.savings,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## overall.financial.wellbeing
|
|
|
|
```{r miscellaneous-overall-financial-wellbeing}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + overall.financial.wellbeing,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## received.food.stamps
|
|
|
|
```{r miscellaneous-received-food-stamps}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + received.food.stamps,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## life.satisfaction
|
|
|
|
```{r miscellaneous-life-satisfaction}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + life.satisfaction,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1, life.satisfaction = 1))
|
|
|
|
```
|
|
|
|
## employer.type
|
|
|
|
```{r miscellaneous-employer-type}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + employer.type,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## employer.type = Self-employed & job.occupation = Computer and Mathematical
|
|
|
|
```{r miscellaneous-self-employed-computer-industry}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male +
|
|
I(employer.type == "Self-employed") * I(job.occupation == "Computer and Mathematical"),
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
print(or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1)))
|
|
|
|
```
|
|
|
|
## informal.selling.of.goods
|
|
|
|
```{r miscellaneous-informal-selling-of-goods}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + informal.selling.of.goods,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## freelance.or.gig.work
|
|
|
|
```{r miscellaneous-freelance-or-gig-work}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + freelance.or.gig.work,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## resides.in.metro.statistical.area
|
|
|
|
```{r miscellaneous-resides-in-metro-statistical-area}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + resides.in.metro.statistical.area,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## respondent.device.type
|
|
|
|
```{r miscellaneous-respondent-device-type}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + respondent.device.type,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
```
|
|
|
|
## is.kp.laptop.user
|
|
|
|
```{r miscellaneous-is-kp-laptop-user}
|
|
|
|
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + is.kp.laptop.user,
|
|
SHED, family = stats::quasibinomial(link = "logit"))
|
|
summary(svyglm.fit)
|
|
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
|
|
|
|
|
```
|
|
|