Load the libraries

library(utils)
library(knitr)
library(dplyr)

Data Preparation

Introduction

The Global Financial Inclusion (Global Findex) database, launched by the World Bank in 2011, provides comparable indicators showing how people around the world save, borrow, make payments, and manage risk. The 2014 edition of the database contain 142 economies with 146688 observations of 86 variables.

Data Dictionary

Following is sample of variables within data file micro_world.csv. Variables may contain the recorded results of a direct question asked, or be derived in some way. The number of variables and the number of data points (cases) are summarized for each variable.

# Load Data Dictionary
wd <- getwd()
ddf <- paste(wd,"//Global-Findex-Data-Dictionary.csv",sep = "")
findex.data.dictionary <- read.csv(ddf, header = T)
kable(tail(findex.data.dictionary,10))
Variable File Type Format Width Valid.cases Value.s. Question Comment
77 q40bc micro_world Discrete numeric 1 21459 Minimum: 1, Maximum: 3 If received government transfers: into an account or to a card 1-yes, 2-no, 3-don’t know(df) / refused(ref), NA
78 q40d micro_world Discrete numeric 1 21459 Minimum: 1, Maximum: 4 If received government transfers: through a mobile phone 1-yes, 2-no, 3-don’t know(df), 4-refused(ref), NA
79 q41 micro_world Discrete numeric 1 14259 Minimum: 1, Maximum: 4 If received cashless government transfers: account use 1-all of the money right away, 2-over time as needed, 3-don’t know(df), 4-refused(ref), NA
80 q42 micro_world Discrete numeric 1 14259 Minimum: 1, Maximum: 5 If received cashless government transfers: account type 1-you had this account before you began receiving payments from the government, 2-you had an account before, but this account was opened so you could receive payments from the government, 3-this was your first account, and it was opened so you could receive payments from the government, 4-don’t know(df), 5-refused(ref), NA
81 q43 micro_world Discrete numeric 1 115563 Minimum: 1, Maximum: 4 Received agricultural payments in past 12 months 1-yes, 2-no, 3-don’t know(df), 4-refused(ref), NA
82 q44a micro_world Discrete numeric 1 23956 Minimum: 1, Maximum: 4 If received agricultural payments: in cash 1-yes, 2-no, 3-don’t know(df), 4-refused(ref), NA
83 q44b micro_world Discrete numeric 1 23956 Minimum: 1, Maximum: 4 If received agricultural payments: into an account 1-yes, 2-no, 3-don’t know(df), 4-refused(ref), NA
84 q44c micro_world Discrete numeric 1 23956 Minimum: 1, Maximum: 4 If received agricultural payments: through a mobile phone 1-yes, 2-no, 3-don’t know(df), 4-refused(ref), NA
85 saved micro_world Discrete numeric 1 146688 Minimum: 1, Maximum: 3 Saved in the past year 1-yes, 2-no, 3-don’t know(df) / refused(ref)
86 borrowed micro_world Discrete numeric 1 146688 Minimum: 1, Maximum: 3 Borrowed in the past year 1-yes, 2-no, 3-don’t know(df) / refused(ref)
Sample Data
# Load Data

wd <- getwd()
df <- paste(wd,"//micro_world.csv",sep = "")
findex.raw.data <- read.csv(df, header = T)
kable(tail(findex.raw.data,10))
economy economycode regionwb pop_adult wpid_random wgt female age educ inc_q account account_fin account_mob q2 q3 q4 q5 q6 q8a q8b q8c q8d q8e q8f q8g q8h q8i q9 q10 q11 q12 q13 q14 q16 q17a q17b q17c q18a q18b q20 q21a q21b q21c q21d q22a q22b q22c q24 q25 q26 q27a q27b q27c q27d q28 q29a q29b q29c q29d q30 q31a q31b q31c q32 q33a q33b q33c q34 q35 q36a q36bc q36d q37 q38 q39 q40a q40bc q40d q41 q42 q43 q44a q44b q44c saved borrowed
146679 Kosovo KSV Europe & Central Asia 1323838 146964643 1.0732399 1 17 1 4 2 2 NA 2 NA NA 2 NA 2 2 1 2 2 2 2 1 2 NA NA NA NA NA NA 1 2 2 1 2 1 2 2 2 1 2 1 2 2 2 2 2 NA NA NA NA 1 1 2 2 2 2 NA NA NA 1 1 2 2 2 NA NA NA NA NA NA 2 NA NA NA NA NA 1 1 2 2 1 1
146680 Kosovo KSV Europe & Central Asia 1323838 155489096 0.8891532 2 23 2 4 1 1 NA 1 1 1 1 1 NA NA NA NA NA NA NA NA NA 1 1 1 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 NA NA NA NA 2 NA NA NA NA 2 NA NA NA 2 NA NA NA 1 2 2 2 2 NA NA 2 NA NA NA NA NA 2 NA NA NA 1 2
146681 Kosovo KSV Europe & Central Asia 1323838 209392015 0.4979076 2 52 2 4 1 1 NA 2 NA NA 2 NA NA NA NA NA NA NA NA NA NA 1 1 1 1 2 2 1 2 1 2 1 2 2 2 2 1 2 2 1 2 2 1 1 2 1 2 1 1 2 2 2 1 1 1 2 2 2 NA NA NA 2 NA NA NA NA NA NA 2 NA NA NA NA NA 1 1 2 2 1 1
146682 Kosovo KSV Europe & Central Asia 1323838 111923258 1.0057815 1 19 2 1 1 1 NA 2 NA NA 2 NA NA NA NA NA NA NA NA NA NA 2 NA 1 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 2 1 2 NA NA NA NA 2 NA NA NA NA 1 1 2 2 2 NA NA NA 2 NA NA NA NA NA NA 2 NA NA NA NA NA 2 NA NA NA 1 1
146683 Kosovo KSV Europe & Central Asia 1323838 203301882 0.4224128 1 52 2 5 1 1 NA 1 1 1 1 1 NA NA NA NA NA NA NA NA NA 1 1 1 1 1 2 1 2 1 1 1 2 2 2 1 2 2 2 2 2 1 4 1 2 1 2 1 2 NA NA NA NA 1 1 2 2 2 NA NA NA 1 1 2 1 2 2 3 2 NA NA NA NA NA 1 1 2 2 1 1
146684 Kosovo KSV Europe & Central Asia 1323838 191255055 0.3576257 2 23 2 5 1 1 NA 1 1 1 2 NA NA NA NA NA NA NA NA NA NA 1 1 1 1 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 1 1 3 2 NA NA NA NA 2 NA NA NA NA 1 1 2 2 2 NA NA NA 1 1 2 1 2 2 3 2 NA NA NA NA NA 1 1 2 2 1 1
146685 Kosovo KSV Europe & Central Asia 1323838 114164617 0.5364385 2 45 2 4 1 1 NA 1 1 1 1 1 NA NA NA NA NA NA NA NA NA 1 1 1 1 1 2 1 1 1 2 1 2 2 2 1 2 2 2 2 1 1 4 1 2 1 2 1 2 NA NA NA NA 1 1 2 2 2 NA NA NA 1 1 2 1 2 2 3 2 NA NA NA NA NA 1 1 2 2 1 1
146686 Kosovo KSV Europe & Central Asia 1323838 120046994 1.9080592 2 48 1 3 1 1 NA 2 NA NA 2 NA NA NA NA NA NA NA NA NA NA 1 3 1 1 2 2 2 2 1 2 1 2 2 2 1 2 2 2 1 2 2 2 2 NA NA NA NA 2 NA NA NA NA 1 1 2 2 1 1 2 2 2 NA NA NA NA NA NA 2 NA NA NA NA NA 1 1 2 2 1 1
146687 Kosovo KSV Europe & Central Asia 1323838 146340621 0.3576257 2 75 2 5 1 1 NA 1 2 1 1 1 NA NA NA NA NA NA NA NA NA 1 2 1 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 NA NA NA NA 1 1 1 2 4 NA NA NA 1 1 2 1 2 1 4 2 NA NA NA NA NA 2 NA NA NA 1 1
146688 Kosovo KSV Europe & Central Asia 1323838 151657570 0.7409610 2 27 2 2 2 2 NA 2 NA NA 2 NA 2 1 2 1 2 1 1 2 1 NA NA NA NA NA NA 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 NA NA NA NA 2 NA NA NA NA 1 1 2 2 1 1 2 2 2 NA NA NA NA NA NA 2 NA NA NA NA NA 2 NA NA NA 2 2 
# There are a lot of columns in this dataset. This will subset the dataset to include only the observations we are interested in. Rename column name accordingly.

data.filter1 <- findex.raw.data %>%
  filter(regionwb %in% c("Middle East","East Asia & Pacific","South Asia"))

data.filter1 <-  rename(data.filter1, gender = female)

Research question(s)

Research would be more specific to economies that fall under the regions Middle East, East Asia & Pacific and South Asia.

  1. How does account ownership vary by individual characteristics?
    1. Role of household income to have an account.
    2. Role of gender to have an account.
    3. Role of age to have an account.
    4. Account ownership by economy(country) in the region.
  2. How do people receive wage payments?
    1. Role of gender in receiving wage payments (in cash only and deposited into the account). Does female respondents receive wage in cash only more often than male respondents?
    2. Does gender has any effect on wages paid more often in cash only vs. deposited into the account?
    3. Does age has any effect on wages paid more often in cash only vs. deposited into the account?
    4. How often does high household income respondents get paid in cash only?
    5. How does economies(countries) in the region fair in wage payments (in cash only vs. deposited into the account)? Does one economy pay wages more often in cash only vs. deposited into the account?

Cases

Each case represents a person at least 15 years of age from the economy(country) and region. There are total of 146688 observations across the world and 27343 observation from the regions Middle East, East Asia & Pacific and South Asia.

Data collection

The Global Financial Inclusion (Global Findex) Database is the world’s most comprehensive gauge of how adults around the world save, borrow, make payments and manage risk. Launched in 2011 with the support of the Bill & Melinda Gates Foundation. Three years later, the 2014 Global Findex provides an update on the indicators collected in 2011. The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2014 calendar year. World - Global Financial Inclusion (Global Findex) Database 2014 is maintained by World Bank.

Type of study

This is an observational study.

Data source

Development Research Group, Finance and Private Sector Development Unit - World Bank. Additional information regarding World - Global Financial Inclusion (Global Findex) Database 2014 can be found at http://www.worldbank.org/en/programs/globalfindex.

The reference citation for the 2014 Global Findex data is Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, Peter Van Oudheusden The Global Findex Database 2014: Measuring Financial Inclusion around the World. Policy Research Working Paper 7255.

Response

The response variables are account ownership and wage payments. All the values are numerical.

Explanatory

The explanatory variables are count of populations based on age, gender, household income and economy. All the values are numerical.

Relevant summary statistics

data.filter1 %>% 
  group_by (gender) %>% 
  summarise(count = n())
## # A tibble: 2 × 2
##   gender count
##    <int> <int>
## 1      1 12792
## 2      2 14551