diff --git a/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.Rmd b/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.Rmd index 04cb8d0..c7a43a0 100644 --- a/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.Rmd +++ b/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.Rmd @@ -116,11 +116,11 @@ SHED$crypto.used.as.payment <- SHED$S16_b == "Yes" # Do you and/or your spouse or partner # currently have a checking, savings or money # market account? -SHED$does.not.have.bank.account <- SHED$BK1 == "No" +SHED$lacks.bank.account <- SHED$BK1 == "No" # C2A # Do you currently have at least one credit card? -SHED$does.not.have.credit.card <- SHED$C2A == "No" +SHED$lacks.credit.card <- SHED$C2A == "No" # ppgender # Gender [Ipsos source] @@ -308,7 +308,7 @@ SHED <- svydesign(ids = ~0, data = SHED, weights = SHED$weight_pop) svyvar.covariance <- svyvar(~ age + is.male + crypto.used.as.payment + - does.not.have.bank.account + does.not.have.credit.card, SHED, na.rm = TRUE) + lacks.bank.account + lacks.credit.card, SHED, na.rm = TRUE) attr(svyvar.covariance, "var") <- NULL svyvar.correlation <- cov2cor(as.matrix(svyvar.covariance)) @@ -316,29 +316,29 @@ print(round(svyvar.correlation, 3)) corrplot(svyvar.correlation, tl.col = "darkred", tl.srt = 35, method = "shade", number.digits = 2, addshade = "all", diag = FALSE, - title = "\n\n Correlation Plot", + title = "\n\n Correlation Matrix of Financial Marginalization\nand Use of Cryptocurrency as a Means of Payment", addCoef.col = "black", type = "lower") ``` # Main results -## does.not.have.bank.account +## lacks.bank.account -```{r main-results-does-not-have-bank-account} +```{r main-results-lacks-bank-account} -svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + does.not.have.bank.account, +svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lacks.bank.account, SHED, family = stats::quasibinomial(link = "logit")) summary(svyglm.fit) -or_svyglm(SHED$variables$variables, svyglm.fit, incr = list(age = 1)) +or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1)) ``` -## does.not.have.credit.card +## lacks.credit.card -```{r main-results-does-not-have-credit-card} +```{r main-results-lacks-credit-card} -svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + does.not.have.credit.card, +svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lacks.credit.card, SHED, family = stats::quasibinomial(link = "logit")) summary(svyglm.fit) or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1)) diff --git a/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.html b/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.html index 8105d66..888df15 100644 --- a/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.html +++ b/Financial-Marginalization-and-Cryptocurrency/Financial-Marginalization-and-Cryptocurrency-Payments.html @@ -423,11 +423,11 @@ SHED$crypto.used.as.payment <- SHED$S16_b == "Yes" # Do you and/or your spouse or partner # currently have a checking, savings or money # market account? -SHED$does.not.have.bank.account <- SHED$BK1 == "No" +SHED$lacks.bank.account <- SHED$BK1 == "No" # C2A # Do you currently have at least one credit card? -SHED$does.not.have.credit.card <- SHED$C2A == "No" +SHED$lacks.credit.card <- SHED$C2A == "No" # ppgender # Gender [Ipsos source] @@ -608,77 +608,77 @@ SHED <- svydesign(ids = ~0, data = SHED, weights = SHED$weight_pop)

Checking correlations between main variables

svyvar.covariance <- svyvar(~ age + is.male + crypto.used.as.payment +
-    does.not.have.bank.account + does.not.have.credit.card, SHED, na.rm = TRUE)
+    lacks.bank.account + lacks.credit.card, SHED, na.rm = TRUE)
 attr(svyvar.covariance, "var") <- NULL
 svyvar.correlation <- cov2cor(as.matrix(svyvar.covariance))
 
 print(round(svyvar.correlation, 3))
-
##                               age is.male crypto.used.as.payment does.not.have.bank.account does.not.have.credit.card
-## age                         1.000  -0.024                 -0.070                     -0.106                    -0.201
-## is.male                    -0.024   1.000                  0.033                      0.011                     0.030
-## crypto.used.as.payment     -0.070   0.033                  1.000                      0.035                     0.035
-## does.not.have.bank.account -0.106   0.011                  0.035                      1.000                     0.364
-## does.not.have.credit.card  -0.201   0.030                  0.035                      0.364                     1.000
+
##                           age is.male crypto.used.as.payment lacks.bank.account lacks.credit.card
+## age                     1.000  -0.024                 -0.070             -0.106            -0.201
+## is.male                -0.024   1.000                  0.033              0.011             0.030
+## crypto.used.as.payment -0.070   0.033                  1.000              0.035             0.035
+## lacks.bank.account     -0.106   0.011                  0.035              1.000             0.364
+## lacks.credit.card      -0.201   0.030                  0.035              0.364             1.000
 ## attr(,"statistic")
 ## [1] "variance"

 
corrplot(svyvar.correlation, tl.col = "darkred", tl.srt = 35, 
   method = "shade", number.digits = 2, addshade = "all", diag = FALSE,
-         title = "\n\n Correlation Plot",
+         title = "\n\n Correlation Matrix of Financial Marginalization\nand Use of Cryptocurrency as a Means of Payment",
          addCoef.col = "black", type = "lower")

-

+

Main results

-
-

does.not.have.bank.account

-
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + does.not.have.bank.account, 
+

+
lacks.bank.account

+
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lacks.bank.account, 
   SHED, family = stats::quasibinomial(link = "logit"))
 summary(svyglm.fit)

 
## 
 ## Call:
-## svyglm(formula = crypto.used.as.payment ~ age + is.male + does.not.have.bank.account, 
+## svyglm(formula = crypto.used.as.payment ~ age + is.male + lacks.bank.account, 
 ##     design = SHED, family = stats::quasibinomial(link = "logit"))
 ## 
 ## Survey design:
 ## svydesign(ids = ~0, data = SHED, weights = SHED$weight_pop)
 ## 
 ## Coefficients:
-##                                 Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                    -2.913447   0.226073 -12.887  < 2e-16 ***
-## age                            -0.031695   0.004137  -7.662 1.97e-14 ***
-## is.maleTRUE                     0.466379   0.168780   2.763  0.00573 ** 
-## does.not.have.bank.accountTRUE  0.581630   0.285850   2.035  0.04190 *  
+##                         Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)            -2.913447   0.226073 -12.887  < 2e-16 ***
+## age                    -0.031695   0.004137  -7.662 1.97e-14 ***
+## is.maleTRUE             0.466379   0.168780   2.763  0.00573 ** 
+## lacks.bank.accountTRUE  0.581630   0.285850   2.035  0.04190 *  
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## (Dispersion parameter for quasibinomial family taken to be 0.958345)
 ## 
 ## Number of Fisher Scoring iterations: 7

-
or_svyglm(SHED$variables$variables, svyglm.fit, incr = list(age = 1))

-
##                        predictor oddsratio ci_low (2.5) ci_high (97.5)          increment
-## 1                            age     0.969        0.961          0.977 Indicator variable
-## 2                    is.maleTRUE     1.594        1.145          2.219 Indicator variable
-## 3 does.not.have.bank.accountTRUE     1.789        1.022          3.133 Indicator variable

+
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))

+
##                predictor oddsratio ci_low (2.5) ci_high (97.5)          increment
+## 1                    age     0.969        0.961          0.977                  1
+## 2            is.maleTRUE     1.594        1.145          2.219 Indicator variable
+## 3 lacks.bank.accountTRUE     1.789        1.022          3.133 Indicator variable

 

-

-
does.not.have.credit.card

-
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + does.not.have.credit.card, 
+

+
lacks.credit.card

+
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lacks.credit.card, 
   SHED, family = stats::quasibinomial(link = "logit"))
 summary(svyglm.fit)

 
## 
 ## Call:
-## svyglm(formula = crypto.used.as.payment ~ age + is.male + does.not.have.credit.card, 
+## svyglm(formula = crypto.used.as.payment ~ age + is.male + lacks.credit.card, 
 ##     design = SHED, family = stats::quasibinomial(link = "logit"))
 ## 
 ## Survey design:
 ## svydesign(ids = ~0, data = SHED, weights = SHED$weight_pop)
 ## 
 ## Coefficients:
-##                                Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                   -2.952972   0.234628 -12.586  < 2e-16 ***
-## age                           -0.031084   0.004205  -7.392 1.54e-13 ***
-## is.maleTRUE                    0.466022   0.168684   2.763  0.00574 ** 
-## does.not.have.credit.cardTRUE  0.304096   0.214471   1.418  0.15625    
+##                        Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)           -2.952972   0.234628 -12.586  < 2e-16 ***
+## age                   -0.031084   0.004205  -7.392 1.54e-13 ***
+## is.maleTRUE            0.466022   0.168684   2.763  0.00574 ** 
+## lacks.credit.cardTRUE  0.304096   0.214471   1.418  0.15625    
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
@@ -686,10 +686,10 @@ summary(svyglm.fit)

 ## 
 ## Number of Fisher Scoring iterations: 7

 
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))

-
##                       predictor oddsratio ci_low (2.5) ci_high (97.5)          increment
-## 1                           age     0.969        0.961          0.977                  1
-## 2                   is.maleTRUE     1.594        1.145          2.218 Indicator variable
-## 3 does.not.have.credit.cardTRUE     1.355        0.890          2.064 Indicator variable

+
##               predictor oddsratio ci_low (2.5) ci_high (97.5)          increment
+## 1                   age     0.969        0.961          0.977                  1
+## 2           is.maleTRUE     1.594        1.145          2.218 Indicator variable
+## 3 lacks.credit.cardTRUE     1.355        0.890          2.064 Indicator variable