# Calculate 2 + 2
2 + 2 # This line tells R to add 2 and 2 together.[1] 4# The comment after the code explains what the code does.Data Wrangling and Visualization in R for International Affairs Research
Introduction
In this tutorial, we’ll introduce you to coding in R in a gentle, step-by-step manner. Remember, every detail matters when writing code—every capital letter, comma, and punctuation mark must be in the correct place, or the code may not run as expected.
We’ll learn by exploring examples and then modifying them. This hands-on approach will help you understand not only the basics of R but also how to work with the kinds of datasets that are often used in international affairs.
R scripts let you record every step of your analysis, so if you make a mistake, you can simply fix it and run the code again instead of redoing your work from scratch.
In this tutorial, we’ll use Quarto Documents, which allow you to mix text and code. This tutorial itself is a Quarto Document. If you are viewing this tutorial in RStudio, then you can click on the Source tab in the upper-left-hand corner of this window to see the actual text that RStudio uses to render the tutorial you can follow in the visual editor, which you can see by clicking on the Visual tab.
When you render a Quarto document, all code blocks will run in order, and their results will appear right below each block. This makes it easy to see how the code works and compare your output to the provided examples.
The Objectives
By the end of this tutorial, you will be able to: - Understand the basics of the R language. - Edit and run R code using RStudio. - Install and use packages designed for international affairs research. - Import, clean, and combine datasets from different sources. - Create a variety of visualizations to explore international affairs data.
First Lines of Code
Let’s start with a very simple example. In this section, you’ll run a basic arithmetic calculation. Follow along carefully!
Running a Simple Calculation
The gray box below is a code block, which is where you can type and run R code in a Quarto Document. If you look at the block below in the Source tab, you can see that it starts with a special line that tells RStudio that you are starting a block of R code. If you are in the Visual tab, you can also add a code block by clicking Insert -> Executable Cell -> R.
Quarto Documents all have a run button, which looks like a play button, in the upper right-hand corner. When you click on the run button, RStudio will execute the R code in the block and output the results immediately below.
Below, we add two numbers together. Notice the use of the # symbol—everything following it on the line is a comment, which means R ignores that text when running the code. To run the code, just click on the run button in the upper right-hand corner of the code block.
Explanation:
- The number 2 is a numeric value. - The + symbol instructs R to perform addition. - The result of 2 + 2 will be displayed as 4.
Congratulations! You just ran your first line of R code!
More Examples with Basic Arithmetic
Now, try running these examples to see how R follows the standard order of operations (i.e., the rules that determine which calculations are performed first).
# Example: Multiplication inside parentheses is done first.
2 + (3 * 2) # Multiplies 3 and 2, then adds 2.[1] 8# Parentheses change the order of operations.
(2 + 3) * 2 # Adds 2 and 3 first, then multiplies the result by 2.[1] 10# Division example
4 / 2 # Divides 4 by 2.[1] 2# Combination of addition and division inside parentheses.
(4 + 2) / 2 # First adds 4 and 2, then divides by 2.[1] 3# Another combination example.
4 + (2 / 2) # Divides 2 by 2 first, then adds 4.[1] 5Explanation:
- Parentheses tell R which calculations to perform first. - Changing the order in which operations are done will change the result.
Object-Oriented Programming in R
R stores data in objects. Think of an object as a little container where you can keep a number, a list of numbers, a function, or even a whole dataset. You can create and then manipulate these objects using R code.
Creating Your First Object
Let’s create an object named x that holds the number 5.
# Assign the number 5 to the object x
x <- 5 # The '<-' operator assigns the value on the right to the object on the left.Explanation:
- x is the name of the object. - <- is used to assign values in R. - After this code runs, x holds the value 5.
Notice that this time R didn’t output anything below the code block. Instead, it took that value of 5 and assigned it to an object called x. If you are in RStudio, you can click on the Environment tab in the panel immediately to the right of the one in which you are currently looking at your code to see a list of all the objects that R currently has in memory. There, you should see that RStudio now displays the object x, which has a value of 5.
The environment tab is very helpful for keeping track of what objects R has in memory. If you are using code that tries to access an object, whether it is a function, a value, a list of values, or so on, but that object is not in memory, the code will not function properly. We’ll come back to this point later on in this tutorial.
Displaying an Object’s Value
Because we now have x in memory, we can ask R to show us the value stored in x.
# Display the value of x
x # Typing the object name prints its value.[1] 5Explanation:
- Simply typing x tells R to output the content of the object x.
Understanding Case Sensitivity
R distinguishes between uppercase and lowercase letters. This means x and X are considered different.
# Trying to print X (uppercase) when only x (lowercase) exists
X # This will result in an error because 'X' has not been defined.Error: object 'X' not foundExplanation:
- R will show an error message like Error: object 'X' not found because only x (lowercase) has been created.
Question 1. Correct the previous code block so that it will print out the object x without causing an error.
Creating a Vector
A vector is an object that is composed simply of a series data points of the same data type. We can use the c() function (which stands for “combine” or “concatenate”) to create a vector.
# Create a vector containing the numbers 1, 5, and 10
x <- c(1, 5, 10) # The c() function combines the numbers into a single vector.Explanation:
- Inside the c() function, numbers are separated by commas. - The vector x now contains three numbers: 1, 5, and 10.
Have a look at the Environment tab, and you’ll see that the value of x has now changed to list all the entries in the vector we just created.
Question 1. Create a code block below and then write code that will create a vector that contains the letters A, B, and C and print it out. Keep in mind that, in R, you have to put all text values in quotation marks, like this: "A", or else R will think that they refer to objects that are already in memory and try to find those objects.
Displaying a Vector
Print the vector x to see its contents.
# Print the vector x to the console
x # R will display the elements of the vector.[1] 1 5 10Explanation:
- The console shows [1] 1 5 10, which means these are the values stored in the vector.
Basic Vector Operations
Once you have a vector, you can perform operations on every element at once.
Performing Arithmetic on Vectors
Let’s do some basic arithmetic with the vector x. The cool thing is that we can generally treat the entire vector as if it were just one number. R will just apply whatever mathematical operations we tell it to to every entry in the vector.
# Re-create the vector for clarity
x <- c(1, 5, 10)
# Add 2 to every element of x
x + 2 # R adds 2 to each element in the vector.[1] 3 7 12# Multiply every element of x by 2
x * 2 # Each element is multiplied by 2.[1] 2 10 20# Raise every element of x to the power of 2 (square each element)
x ^ 2 # Each element is squared.[1] 1 25 100# Divide every element of x by 2
x / 2 # Each element is divided by 2.[1] 0.5 2.5 5.0Explanation:
- R automatically applies arithmetic operations to each element in the vector. - The same operator is used for each element in the vector.
Using Statistical Functions on a Vector
Functions are a special kind of R object. Functions are basically chunks of R code with a name. Generally, they take other objects, which we call arguments, as inputs, and then produce some output from them. In code, functions are always written followed by ( ), and all the function’s arguments are written in the parentheses.
For example, you can apply functions like min(), mean(), median(), max(), sd(), and summary() to understand the properties of a numerical vector. The main argument of all these functions is just the vector of numbers that you want to perform the function on.
# Calculate the minimum value in the vector
min(x) # Finds the smallest number.[1] 1# Calculate the mean (average) of the vector
mean(x) # Adds all numbers together and divides by the number of elements.[1] 5.333333# Calculate the median of the vector
median(x) # Finds the middle value when the numbers are sorted.[1] 5# Calculate the maximum value in the vector
max(x) # Finds the largest number.[1] 10# Calculate the standard deviation, which measures the spread of the numbers
sd(x) # Standard deviation of the numbers in the vector.[1] 4.50925# Provide a summary of the vector's statistical properties
summary(x) # Gives min, 1st quartile, median, mean, 3rd quartile, and max. Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 3.000 5.000 5.333 7.500 10.000 Every function in R has a brief built-in description that outlines its arguments, what types of values you can set for the arguments, and some examples of how the function can be used. To see these descriptions, you can use the help() function, as in the following code block:
help("min") #notice that the name of the function is in quotation marksstarting httpd help server ... doneRather than printing out the function description, if you are in RStudio, the description will appear in the bottom-right-hand corner of the screen. At first, these descriptions can be very difficult to parse, but the more you use R, the more you will be able to understand how they work. The good thing is that they all have the same format. They start with a brief overview of what the function does, followed by a description of all its arguments. Then most functions will list a few extra details and, finally, they will have some example code that you can try out to get a better sense of how the function works.
Creating New Objects
We can also use the <- operator to take the output of one bit of code and turn it into a new object. This is where the power of R really starts to show up, because you can use chains of objects to analyze and visualize data. Here’s an example:
x <- c(1, 2, 3, 4, 5)
y <- x*2
z <- mean(y)
x[1] 1 2 3 4 5y[1] 2 4 6 8 10z[1] 6Now that we have some understanding of how some of the basic building blocks of the R coding language work, let’s look at how we can use packages to really take advantage of R’s sophisticated capabilities for data analysis and visualization.
Question 2. Create a code block below and add in code that will create a vector consisting of all the even numbers between 1 and 11, multiply all those numbers by 3, and then find the median value. Assign this value to an object called z and then print out the results.
Installing and Loading Packages
Remember how we said that functions are objects and that R can’t use objects that aren’t in its memory? Well, that goes for functions just as much as any other object. Because R is an open-source language, people are writing new code for it all the time. All this new code is in the form of functions, and you have to bring those functions into memory for R to be able to use them.
Packages are collections of functions and data that you can download and bring into R’s memory for your own use. The vast majority of packages in R are maintained by the Comprehensive R Archive Network (CRAN), a website that hosts a structured repository of R packages, along with their documentation.
In this tutorial, we’ll introduce several packages that are particularly useful for international affairs research. Before you can use a package, however, you must install it (if you haven’t already) and then load it into your R session.
To install a package, you need to know the package’s name (generally, you find this sort of thing by looking at websites where people have posted analyses they have done that you want to replicate or apply to something else). Once you know the name, you can use the install.packages() function to tell R to go to find the package on CRAN, download it, and install it.
Once you have installed a package, you still need to tell R to load the functions in it when you want to use them. To load a package into memory, you use the library() function. While you only need to install a package once, you need to run the library() function to load its contents into memory every time you restart R.
The following code block shows you how to install and activate the packages that we’ll be using in this tutorial.
# Uncomment the lines below if you haven't installed these packages before.
# install.packages("tidyverse") # A suite of packages for data manipulation and visualization.
# install.packages("peacesciencer") # Access peace and conflict data.
# install.packages("WDI") # Access World Bank development indicators.
# install.packages("countrycode") # Standardize country names and codes.
# Load the packages so that their functions become available.
library(tidyverse) # Loads packages like ggplot2, dplyr, tidyr, etc.── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.2 ✔ tibble 3.3.0
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.1.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(peacesciencer) # Provides functions to retrieve conflict data.{peacesciencer} includes additional remote data for separate download. Please type ?download_extdata() for more information.
This message disappears on load when these data are downloaded and in the package's `extdata` directory.library(WDI) # Allows access to World Bank data.
library(countrycode) # Helps convert and match country codes.Explanation:
- install.packages("packageName") downloads and installs the package. - library(packageName) loads the package into the current session. - The comments explain each package’s purpose.
Working with International Affairs Data
Most of the packages we just installed and activated are designed to help us source data from major data providers like the World Bank, the Varieties of Democracy Project, and the Uppsala Conflict Data Program. In addition, we loaded the tidyverse package, which is actually a cluster of different packages that streamline data processing and visualization.
We’re going to look at how we can use these packages to source data efficiently and then use tidyverse to make interesting data visualizations with them. We will walk through each step, explaining the functions and operations in detail.
Before we start, though, a quick word about how R represents data tables. While R has an absolutely huge number of data object types, there are really just a few that are the most commonly used for basic data visualization and analysis. Here they are:
Vector: We’ve already seen these - they’re just a sequence of data values of the same type
Matrix: This is a table with rows and columns, filled with data values that are all of the same type
Data Frame: This is a table in which the columns can be different data types, so you can combine numerical data with text data, which
Rcalls stringsTibble: This is basically a special type of data frame that is used a lot in
tidyversefunctions. Generally, you can use them interchangeably with data frames
Note: I will sometimes just refer to data frames and tibbles interchangeably as data tables.
The code blocks below show you the differences between these different types of data objects:
# ======================= VECTORS =======================
# Vectors are one-dimensional arrays that can hold data of a single type
# Creating numeric vectors
numeric_vector <- c(1, 2, 3, 4, 5)
# Creating vectors with mixed types (note how R coerces to a single type)
mixed_vector <- c(1, "apple", TRUE) # All elements become characters
print(mixed_vector) # [1] "1" "apple" "TRUE"[1] "1" "apple" "TRUE" print(typeof(mixed_vector)) # "character"[1] "character"# ======================= MATRICES =======================
# Matrices are two-dimensional arrays that contain data of a single type
# Creating matrices
matrix_by_row <- matrix(1:12, nrow = 3, ncol = 4, byrow = TRUE) # creates a
#matrix and fills it with the values 1 through 12 by listing these values
#across the rows. nrow and ncol determine the number of rows and columns in
#the matrix to be created
# Matrix operations
matrix_addition <- matrix_by_row + 5 # Add 5 to each element
matrix_multiplication <- matrix_by_row * 2 # Multiply each element by 2
# Matrix subsetting
print(matrix_by_row[2, 3]) # Element at row 2, column 3[1] 7print(matrix_by_row[1, ]) # First row[1] 1 2 3 4print(matrix_by_row[, 2]) # Second column[1] 2 6 10# ======================= DATA FRAMES =======================
# Data frames are table-like structures that can contain different types of data in different columns
# Creating a data frame - notice how we're combining different types of
# variables in this example!
df <- data.frame(
id = 1:4,
name = c("Alice", "Bob", "Charlie", "David"),
score = c(85.2, 92.5, 78.3, 88.7),
pass = c(TRUE, TRUE, FALSE, TRUE)
)
# Data frame operations
df$bonus <- df$score * 0.1 # Add a new column called "bonus" that is 0.1 times
# score
# Structure of a data frame
head(df) id name score pass bonus
1 1 Alice 85.2 TRUE 8.52
2 2 Bob 92.5 TRUE 9.25
3 3 Charlie 78.3 FALSE 7.83
4 4 David 88.7 TRUE 8.87# ======================= TIBBLES =======================
# Tibbles are a modern reimagining of data frames from the tidyverse package
# Creating a tibble - notice this is basically the same as data frames
tbl <- tibble(
id = 1:4,
name = c("Alice", "Bob", "Charlie", "David"),
score = c(85.2, 92.5, 78.3, 88.7),
pass = c(TRUE, TRUE, FALSE, TRUE)
)
# You can also convert a tibble to a data frame
tbl_from_df <- as_tibble(df)
# Tibbles default to just printing a few rows, so you don't need to use
# head() when inspecting them:
tbl# A tibble: 4 × 4
id name score pass
<int> <chr> <dbl> <lgl>
1 1 Alice 85.2 TRUE
2 2 Bob 92.5 TRUE
3 3 Charlie 78.3 FALSE
4 4 David 88.7 TRUE tbl_from_df# A tibble: 4 × 5
id name score pass bonus
<int> <chr> <dbl> <lgl> <dbl>
1 1 Alice 85.2 TRUE 8.52
2 2 Bob 92.5 TRUE 9.25
3 3 Charlie 78.3 FALSE 7.83
4 4 David 88.7 TRUE 8.87Using peacesciencer to Access Peace and Conflict Data
The peacesciencer package simplifies accessing state-level conflict data. It is based on the concept of the state-year (or, if you want to be really precise, the state-day). That is, every country in the data set has one entry for each year. To access the data, you use the create_stateyears() function, which has two main arguments: system, which tells R which of two country name coding systems to use (we’ll worry more about this later), and subset_years, which tells R what years of data you want. The result will be a tibble with all available country-years.
In the code block below, you’ll also see a new tool used, the : operator. This is a shorthand that generates a numeric vector of all the whole numbers starting with the number before the : and ending with the number after it.
# Create a dataset of state-year observations from 1990 to 2020.
# - system = "gw": Uses the Gleditsch-Ward country codes.
# - subset_years = 1990:2020: Generates data for each year from 1990 to 2020.
state_data <- create_stateyears(system = "gw",
subset_years = 1990:2020)
# Preview the first few rows of the dataset.
state_data# A tibble: 5,976 × 4
gwcode gw_name microstate year
* <dbl> <chr> <dbl> <int>
1 2 United States of America 0 1990
2 2 United States of America 0 1991
3 2 United States of America 0 1992
4 2 United States of America 0 1993
5 2 United States of America 0 1994
6 2 United States of America 0 1995
7 2 United States of America 0 1996
8 2 United States of America 0 1997
9 2 United States of America 0 1998
10 2 United States of America 0 1999
# ℹ 5,966 more rowsExplanation:
- create_stateyears() creates a data frame where each row represents a country in a specific year. - head() lets you quickly inspect the dataset.
You can see that create_stateyears() just creates a list of countries and years. There’s not much we can to with that, but we can use this table of countries and years as a foundation to build an entire dataset. To do this, we need to join data from other tables. Joining refers to the process of taking data from different tables and combining them based on a common identifier.
Because the peacesciencer package is designed to help us interface with a bunch of other datasets, once we have our country-year table, we can then use the special code denoting the country, combined with the year, to join additional data to the table we started with.
To conduct these joins, we’ll be using %>%, which is known as the pipe operator. The pipe operator basically tells R to take the output of the object that comes before the pipe and add it in as the first argument for the function that comes after the pipe. Let’s have a look at a quick example:
x <- 1:10 #creating a new vector to use as an example
x %>%
mean() #taking the mean of x[1] 5.5x %>%
exp() %>%
mean() #exponentiating x and then taking the mean[1] 3484.377Question 3. Create a code block below and write code that uses the %>% operator to first create a vector of all the numbers from 1 to 5, then take the mean, and then exponentiate exp() the mean and print out the result.
Now let’s see how we can use the pipe operator to stitch together data from peacesciencer. The package has some special built-in functions for joining specific types of data to country-year tables. Here’re some examples?
# Enhance state_data by adding:
# - Democracy scores using the add_democracy() function.
# - Civil war data using the add_ucdp_acd() function.
state_data <- state_data %>%
add_democracy() %>% # Adds Polity scores and related democracy measures.
add_ucdp_acd() # Adds data on civil wars from the UCDP dataset.Joining with `by = join_by(gwcode, year)`
Joining with `by = join_by(gwcode, year)`# List the names of the variables in the updated dataset.
names(state_data) # This displays all column names in state_data. [1] "gwcode" "gw_name" "microstate" "year"
[5] "euds" "aeuds" "polity2" "v2x_polyarchy"
[9] "ucdpongoing" "ucdponset" "maxintensity" "conflict_ids" # Preview the updated dataset.
head(state_data)# A tibble: 6 × 12
gwcode gw_name microstate year euds aeuds polity2 v2x_polyarchy ucdpongoing
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 United … 0 1990 1.76 0.948 10 0.866 0
2 2 United … 0 1991 1.76 0.948 10 0.865 0
3 2 United … 0 1992 1.94 1.13 10 0.869 0
4 2 United … 0 1993 1.95 1.15 10 0.868 0
5 2 United … 0 1994 1.89 1.08 10 0.868 0
6 2 United … 0 1995 1.89 1.08 10 0.868 0
# ℹ 3 more variables: ucdponset <dbl>, maxintensity <dbl>, conflict_ids <chr>Explanation:
- The %>% operator (called the “pipe”) passes the result of one function to the next. - add_democracy() and add_ucdp_acd() enrich the data with new variables. - names() lists all variable names so you know what data is available.
Now we have added two measures of democracy: v2x_polyarchy and polity2. The polyarchy score is a composite score maintained by the Varieties of Democracy project, while Polity 2 is maintained by the Polity project. Both are intended to provide an overall view of a country’s “democraticness”.
We have also added several conflict variables. ucdpongoing is a binary variable that is a 1 when there is an ongoing conflict and a 0 otherwise. ucdponset, similarly, is a 1 in the first year of a conflict and 0 otherwise. maxintensity designates the highest intensity of the conflict. 1 designates less than 1,000 battle deaths, while 2 designates 1,000 or more battle deaths. The Uppsala Conflict Data Project designates conflicts with less than 1,000 battle deaths “minor”, while those with 1,000 or more count as “war”.
Question 4. Create a code block below and write code that will create a new dataset, state_data_2, that will include the same data as state_data, but for the years 1970 to 1990.
Preparing Data for Visualization
By this point in your career as an IA major, you have probably heard something about Democratic Peace Theory, which is the idea that democracies don’t go to war with each other (these depends heavily, we should note, on what countries count as democracies in any given analysis). Even if that’s true, though, it doesn’t mean that democracies actually go to war less than countries with other forms of government. In the code block below, we’re going to have a look at the conflict propensity of states with different types of governments, as defined by their Polity IV scores (where higher scores are more democratic) between 1990 and 2020.
Like before, we’re going to be using the %>% operator, but we’re also introducing some new functions. First, we’ll be using is.na(). One thing that you’ll learn as you work more with data is that it is very common for datasets to have gaps where they simply do not have a measure for a given observation on a given variable. We call these gaps missing values. Statistical software packages have different conventions for representing missing values. In R, missing values are represented as NA.
R is very conservative in dealing with missing values - that is, if it is performing any kind of calculation and encounters a missing value, it will return an NA as the output for the whole calculation. The logic here, while strict, is pretty reasonable. This is the equivalent of R saying, “I can’t give you an answer, because there is a value in this calculation that could literally be anything, so the result could be anything.”
While logically fair, most of the time, we still want to do whatever calculation we were trying to do, and, in practice, we generally proceed by dropping the observations with missing values and then proceeding with the calculation using the values that we do have. We use the is.na() function to determine whether or not a value is NA (we can’t just use ==, the equals function, because R will just return NA to that). Here’s an example:
is.na(NA)[1] TRUEis.na(1)[1] FALSE1 == 1[1] TRUE1 == NA[1] NANA == NA[1] NANext, we use the filter() function. filter() takes a vector of TRUE/FALSE entries, which we call Booleans, for each row of a table and then returns only the rows of the table for which the value of the vector is TRUE. Here’s an example:
x <- c(1:5,NA,6:10)
y <- 1:11
cbind(x,y) %>%
as_tibble() %>%
filter(!is.na(x)) #the ! operator is "NOT" - so it negates the TRUE/FALSE vector# A tibble: 10 × 2
x y
<int> <int>
1 1 1
2 2 2
3 3 3
4 4 4
5 5 5
6 6 7
7 7 8
8 8 9
9 9 10
10 10 11There are two other new functions in the code block below, group_by() and summarize(), which are best understood together. group_by() assigns the rows of a table to groups if they have the same value of a particular variable. summarize() allows you to calculate statistics for columns in the table based on group membership. Here’s an example:
tibble(x = 1:10,
y = c("group_A","group_A","group_A","group_A","group_A",
"group_B","group_B","group_B","group_B","group_B")) %>%
group_by(y) %>%
summarize(mean_x = mean(x),
min_x = min(x),
max_x = max(x))# A tibble: 2 × 4
y mean_x min_x max_x
<chr> <dbl> <int> <int>
1 group_A 3 1 5
2 group_B 8 6 10Question 5. Create a code block below and write code that will recreate the tibble in the previous code block, but this time will also compute the sum (using the sum() function) and standard deviation of x across groups.
Okay, now let’s have a look at how we can put this all together to reshape a data table.
# Create a summary dataset that groups data by democracy score.
dem_conflict <- state_data %>%
filter(!is.na(polity2)) %>% # Remove rows with missing democracy scores.
group_by(polity2) %>% # Group data by the democracy score.
summarize(
n_obs = n(), # Count the number of observations per group.
prop_conflict = mean(ucdpongoing, na.rm = TRUE) # Calculate the proportion of
# country-years with conflict. The na.rm = TRUE part tells R to ignore NAs when
# computing the mean
)
# Display the first few rows of the summarized data.
head(dem_conflict)# A tibble: 6 × 3
polity2 n_obs prop_conflict
<dbl> <int> <dbl>
1 -10 109 0
2 -9 109 0.156
3 -8 66 0.0606
4 -7 317 0.186
5 -6 207 0.290
6 -5 118 0.254 Explanation:
- filter(!is.na(polity2)) ensures we only include complete data. - group_by(polity2) groups data by democracy score. - summarize() calculates summary statistics for each group. - n() counts the number of rows per group, and mean(any_conflict, na.rm = TRUE) calculates the average (proportion) of conflict occurrences.
Question 6. Make a code block below and add code that will create a new data table called democracy_compare. This object should be constructed similarly to dem_conflict, but it should have a variable called mean_polyarchy, which will be computed as the mean of the v2x_polyarchy column using the summarize() function. Print out the resulting data table and discuss how similar the two democracy measures seem to be to each other.
Visualizing the Relationship Between Democracy and Conflict
Now we have got the dataset ready to create our first plot! We’re going to use the ggplot() function, which is part of the tidyverse family of packages. ggplot() is kind of a miniature programming language unto itself, designed to break the components of data visualizations into some basic elements that can be combined to describe any of a wide range of chart types. These are the basic elements from which ggplot() builds a graphic:
data: The data object in which the variables you would like to plot are found.aes(): Aesthetics: A function that defines how columns in thedatawill be mapped to visual variables like spatial location (x-axis and y-axis), size, shape, color, or transparency. All variables you want to visualize in a plot have to be input as arguments in anaes()function.geom_(): Geometries: Functions that take data and anaes()function and use them to draw a specific graphic, like points, lines, bars, boxplots, and other more exotic graphics.scale_(): Scales: Functions that control how variables are mapped to the plot axes, sizes, color, and so on.labs(): Labels: Labels for various plot features.theme_(): Themes: Settings that adjust the plot’s overall appearance.
Putting these elements together can get a bit complex, so we’ll take it step by step.
# Create a scatterplot to visualize the relationship.
# First, we use the ggplot() function to start the plot object. We can use
# the function to set the default data object and aes() for the whole plot
ggplot(data = dem_conflict, aes(x = polity2, y = prop_conflict)) +
# Now we will define our first geometry, which is a layer of points.
# We already established that their locations on the x- and y-axes will be
# defined by polity2 and prop_conflict, respectively, and we'll also use
# a new aes() function, which we'll use to set the size of each point
# according to the number of observations
geom_point(aes(size = n_obs), alpha = 0.7) +
labs(
title = "Relationship Between Democracy and Armed Conflict (1990-2020)",
subtitle = "Point size indicates number of country-years at each democracy score",
x = "Democracy Score (Polity, v. 2: -10 = Autocracy, 10 = Democracy)",
y = "Proportion of Country-Years with Active Conflict",
size = "Number of\nObservations"
) +
theme_minimal() # A clean, minimal theme for the plot.
Explanation:
- ggplot() initializes the plot with data and mappings (aes). - geom_point() creates the scatterplot points. - geom_smooth() adds a trend line. - labs() customizes titles and axis labels. - theme_minimal() applies a simple visual theme.
That’s a pretty interesting pattern! It looks like conflict is most common for countries in the middle of the authoritarian - democratic spectrum! We can add another geometry to the plot, geom_smooth(), to plot a trendline over the scatterplots:
ggplot(data = dem_conflict, aes(x = polity2, y = prop_conflict)) +
geom_point(aes(size = n_obs), alpha = 0.7) +
geom_smooth(method = "loess", se = TRUE, color = "red") +
# The smooth line (LOESS) helps visualize the trend. The se argument tells R
# whether or not to show confidence bounds for the estimated mean. method tells
# R what model to use to estimate the local mean.
labs(
title = "Relationship Between Democracy and Armed Conflict (1990-2020)",
subtitle = "Point size indicates number of country-years at each democracy score",
x = "Democracy Score (Polity2: -10 = Autocracy, 10 = Democracy)",
y = "Proportion of Country-Years with Active Conflict",
size = "Number of\nObservations"
) +
theme_bw() # Another plot theme`geom_smooth()` using formula = 'y ~ x'
We can also adjust our aes() function to change the way we are mapping variables from the data table to visual variables in our plot. Here’s another example:
ggplot(data = dem_conflict, aes(y = polity2, x = prop_conflict)) +
# Notice that we switched the x and y axes in the above line!
geom_point(aes(size = n_obs, color = n_obs), alpha = 0.7) +
# Now we're encodeing n_obs by both size and color!
labs(
title = "Relationship Between Democracy and Armed Conflict (1990-2020)",
subtitle = "Point size indicates number of country-years at each democracy score",
y = "Democracy Score (Polity2: -10 = Autocracy, 10 = Democracy)",
x = "Proportion of Country-Years with Active Conflict",
size = "Number of\nObservations",
color = "Number of\nObservations"
) +
theme_classic() # Another plot theme
Finally, we can use scales_ functions to control how we draw the data columns that are mapped to the visual variables. For example:
ggplot(data = dem_conflict, aes(x = polity2, y = prop_conflict)) +
geom_smooth(method = "loess", se = TRUE, color = "darkblue", fill="lightblue") +
#Notice that we moved the smooth line up in the code - that will make R
#draw it first and then draw the points on top of it. I also changed the
#color for the line and the fill color for the confidence envelope.
geom_point(aes(size = n_obs, color = n_obs)) +
scale_color_viridis_c() + # Applying a fancy color-blind-friendly color scale
labs(
title = "Relationship Between Democracy and Armed Conflict (1990-2020)",
subtitle = "Point size indicates number of country-years at each democracy score",
x = "Democracy Score (Polity2: -10 = Autocracy, 10 = Democracy)",
y = "Proportion of Country-Years with Active Conflict",
size = "Number of\nObservations",
color = "Number of\nObservations"
) +
theme_linedraw() # Another plot theme`geom_smooth()` using formula = 'y ~ x'
In addition to being able to map data table columns to specific colors, you can see in the example above that we can also tell R to use certain colors for specific things, like the trend line and its confidence bands. R has a ton of built-in color names that we can use. You can find them all at this link.
Question 7. Add a code block below and then write code that will create your own version of the plot above. Change the color scheme and any other features you like.
Using WDI to Access World Bank Development Data
So far, we have focused primarily on datasets with political variables, but there is an even greater wealth of economic data available for analysis. Probably the most comprehensive set of country-level economic data is maintained in the World Banks’ World Development Indicators (WDI) database. The WDI package lets us access the database directly in R. Because the WDI database is even bigger than V-Dem, there are some tools to help us select which variables (which the WDI calls “indicators”) we want to use.
First, we can use the WDIsearch() function to search for a particular text in the indicators’ names. Here’s an example:
WDIsearch("education") indicator
191 3.1_LOW.SEC.NEW.TEACHERS
192 3.1_PRI.NEW.ENTRANTS
194 3.11_LOW.SEC.CLASSROOMS
195 3.12_LOW.SEC.NEW.CLASSROOMS
196 3.13_PRI.MATH.BOOK.PER.PUPIL
197 3.14_PRI.LANGU.BOOK.PER.PUPIL
202 3.2_PRI.STUDENTS
203 3.3_PRI.TEACHERS
204 3.4_PRI.NEW.TEACHERS
205 3.5_PRI.CLASSROOMS
206 3.6_PRI.NEW.CLASSROOMS
207 3.7_LOW.SEC.NEW.ENTRANTS
208 3.8_LOW.SEC.STUDENTS
209 3.9_LOW.SEC.TEACHERS
237 4.1_TOTAL.EDU.SPENDING
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564 5.2.3_KHM.BAS.AID.GPE
565 5.2.3_LAO.BAS.AID.EC
566 5.2.3_LBR.BAS.AID.WB
567 5.2.3_MDG.BAS.AID.FR
568 5.2.3_MRT.BAS.AID.ISDB
569 5.2.3_MWI.BAS.AID.DFID
570 5.2.3_NER.BAS.AID.FR
571 5.2.3_RWA.BAS.AID.UNICEF
572 5.2.3_SEN.BAS.AID.GPE
573 5.2.3_SLE.BAS.AID.GIZ
574 5.2.3_TJK.BAS.AID.EC
575 5.2.3_TLS.TOT.AID.AUS
576 5.2.3_VNM.BAS.AID.JICA
577 5.2.3_ZMB.BAS.AID.ILO
578 5.2.4_AFG.BAS.AID.DEU
579 5.2.4_BFA.BAS.AID.DNK
580 5.2.4_CIV.BAS.AID.ISDB
581 5.2.4_CMR.BAS.AID.JICA
582 5.2.4_DJI.BAS.AID.AFDB
583 5.2.4_ETH.BAS.AID.DVV
584 5.2.4_GEO.BAS.AID.WB
585 5.2.4_GHA.BAS.AID.UNICEF
586 5.2.4_GIN.BAS.AID.ADPP.GPE
587 5.2.4_GNB.BAS.AID.EU
588 5.2.4_KGZ.BAS.AID.ADPP.WB
589 5.2.4_KHM.BAS.AID.EC
590 5.2.4_LAO.BAS.AID.DEU
591 5.2.4_MDG.BAS.AID.JICA
592 5.2.4_MRT.BAS.AID.SP
593 5.2.4_MWI.BAS.AID.GIZ
594 5.2.4_NER.BAS.AID.JAPAN
595 5.2.4_RWA.BAS.AID.USAID
596 5.2.4_SEN.BAS.AID.IT
597 5.2.4_SLE.BAS.AID.JICA
598 5.2.4_TJK.BAS.AID.GIZ
599 5.2.4_TLS.TOT.AID.WB
600 5.2.4_VNM.BAS.AID.UNESCO
601 5.2.4_ZMB.BAS.AID.JPN
602 5.2.5_AFG.BAS.AID.IND
603 5.2.5_BFA.BAS.AID.JICA
604 5.2.5_CIV.BAS.AID.FSD
605 5.2.5_CMR.BAS.AID.UNESCO
606 5.2.5_DJI.BAS.AID.ISDB
607 5.2.5_ETH.BAS.AID.EC
608 5.2.5_GHA.BAS.AID.USAID
609 5.2.5_GIN.BAS.AID.ADPP.GIZ
610 5.2.5_GNB.BAS.AID.FR
611 5.2.5_KHM.BAS.AID.JPN
612 5.2.5_LAO.BAS.AID.GPE
613 5.2.5_MDG.BAS.AID.NOR
614 5.2.5_MRT.BAS.AID.UNESCO
615 5.2.5_MWI.BAS.AID.GPE
616 5.2.5_NER.BAS.AID.KFW
617 5.2.5_RWA.BAS.AID.WB
618 5.2.5_SEN.BAS.AID.UNICEF
619 5.2.5_SLE.BAS.AID.SIDA
620 5.2.5_TJK.BAS.AID.GPE
621 5.2.5_TLS.TOT.AID.JPN
622 5.2.5_VNM.BAS.AID.UNICEF
623 5.2.5_ZMB.BAS.AID.ZMB
624 5.2.6_AFG.BAS.AID.JPN
625 5.2.6_BFA.BAS.AID.NLD
626 5.2.6_CIV.BAS.AID.KFW
627 5.2.6_CMR.BAS.AID.UNICEF
628 5.2.6_DJI.BAS.AID.IMOA
629 5.2.6_ETH.BAS.AID.FIN
630 5.2.6_GHA.BAS.AID.WFP
631 5.2.6_GIN.BAS.AID.ADPP.KFW
632 5.2.6_GNB.BAS.AID.PORT
633 5.2.6_KHM.BAS.AID.SWE
634 5.2.6_LAO.BAS.AID.JICA
635 5.2.6_MDG.BAS.AID.WFP
636 5.2.6_MRT.BAS.AID.UNICEF
637 5.2.6_MWI.BAS.AID.JICA
638 5.2.6_NER.BAS.AID.WFP
639 5.2.6_SEN.BAS.AID.USAID
640 5.2.6_SLE.BAS.AID.UNICEF
641 5.2.6_TJK.BAS.AID.UNICEF
642 5.2.6_TLS.TOT.AID.KOR
643 5.2.6_VNM.BAS.AID.USAID
644 5.2.6_ZMB.BAS.AID.UNICEF
645 5.2.7_AFG.BAS.AID.JICA
646 5.2.7_BFA.BAS.AID.UNICEF
647 5.2.7_CIV.BAS.AID.UNICEF
648 5.2.7_ETH.BAS.AID.GIZ
649 5.2.7_GHA.BAS.AID.WB
650 5.2.7_GIN.BAS.AID.ADPP.UNICEF
651 5.2.7_GNB.BAS.AID.UNICEF
652 5.2.7_KHM.BAS.AID.UNESCO
653 5.2.7_LAO.BAS.AID.UNESCO
654 5.2.7_MDG.BAS.AID.UNESCO
655 5.2.7_MRT.BAS.AID.WFP
656 5.2.7_MWI.BAS.AID.KFW
657 5.2.7_NER.BAS.AID.DFID
658 5.2.7_SLE.BAS.AID.WB
659 5.2.7_TJK.BAS.AID.USAID
660 5.2.7_TLS.TOT.AID.NZL
661 5.2.7_VNM.BAS.AID.WB
662 5.2.7_ZMB.BAS.AID.USAID
663 5.2.8_AFG.BAS.AID.NLD
664 5.2.8_BFA.BAS.AID.EC
665 5.2.8_CIV.BAS.AID.USAID
666 5.2.8_ETH.BAS.AID.GPE
667 5.2.8_GNB.BAS.AID.JAP
668 5.2.8_KHM.BAS.AID.UNICEF
669 5.2.8_LAO.BAS.AID.UNICEF
670 5.2.8_MDG.BAS.AID.UNICEF
671 5.2.8_MWI.BAS.AID.UNICEF
672 5.2.8_NER.BAS.AID.CHE
673 5.2.8_SLE.BAS.AID.WFP
674 5.2.8_TJK.BAS.AID.WFP
675 5.2.8_TLS.TOT.AID.CFNZL
676 5.2.9_AFG.BAS.AID.NZL
677 5.2.9_ETH.BAS.AID.ITA
678 5.2.9_KHM.BAS.AID.USAID
679 5.2.9_LAO.BAS.AID.WFP
680 5.2.9_MDG.BAS.AID.GPE
681 5.2.9_MWI.BAS.AID.USAID
682 5.2.9_NER.BAS.AID.LUX
683 5.2.9_TJK.BAS.AID.WB
684 5.2.9_TLS.TOT.AID.PRT
687 5.2_BASIC.EDU.AID
694 6.1_LEG.CA
696 6.2_LEG.OTHER.DONORS
697 6.3_LEG.CSO
698 6.4_LAST.JSR
699 6.5_NEXT.JSR
700 7.1.1_ESP.PERIOD.START
701 7.1.2_ESP.PERIOD.END
706 7.2_ESP.ENDORSEMENT
785 8.3.2_CAF.BAC.SUCC
947 9.1_AID.ALIGNMENT
966 9.2_COORDINATED.TECH.COOP
967 9.3_PFM.COUNTRY.SYSTEMS
968 9.4_PROCUREMENT.COUNTRY.SYSTEMS
969 9.5_PIU
970 9.6_PBA
976 9120000
1029 account.t.d.5
1030 account.t.d.6
1449 al_prim_al_alot_dfcl_1529
1450 al_prim_al_alot_dfcl_3044
1451 al_prim_al_alot_dfcl_4564
1452 al_prim_al_alot_dfcl_65up
1453 al_prim_al_alot_dfcl_all
1454 al_prim_al_alot_dfcl_fem
1455 al_prim_al_alot_dfcl_male
1456 al_prim_al_alot_dfcl_rur
1457 al_prim_al_alot_dfcl_urb
1458 al_prim_any_dfcl_1529
1459 al_prim_any_dfcl_3044
1460 al_prim_any_dfcl_4564
1461 al_prim_any_dfcl_65up
1462 al_prim_any_dfcl_all
1463 al_prim_any_dfcl_cogn_all
1464 al_prim_any_dfcl_comm_all
1465 al_prim_any_dfcl_fem
1466 al_prim_any_dfcl_hearing_all
1467 al_prim_any_dfcl_male
1468 al_prim_any_dfcl_mobile_all
1469 al_prim_any_dfcl_rur
1470 al_prim_any_dfcl_seeing_all
1471 al_prim_any_dfcl_selfcare_all
1472 al_prim_any_dfcl_urb
1473 al_prim_none_dfcl_1529
1474 al_prim_none_dfcl_3044
1475 al_prim_none_dfcl_4564
1476 al_prim_none_dfcl_65up
1477 al_prim_none_dfcl_all
1478 al_prim_none_dfcl_fem
1479 al_prim_none_dfcl_male
1480 al_prim_none_dfcl_rur
1481 al_prim_none_dfcl_urb
1482 al_prim_nosome_dfcl_1529
1483 al_prim_nosome_dfcl_3044
1484 al_prim_nosome_dfcl_4564
1485 al_prim_nosome_dfcl_65up
1486 al_prim_nosome_dfcl_all
1487 al_prim_nosome_dfcl_fem
1488 al_prim_nosome_dfcl_male
1489 al_prim_nosome_dfcl_rur
1490 al_prim_nosome_dfcl_urb
1491 al_prim_some_dfcl_1529
1492 al_prim_some_dfcl_3044
1493 al_prim_some_dfcl_4564
1494 al_prim_some_dfcl_65up
1495 al_prim_some_dfcl_all
1496 al_prim_some_dfcl_fem
1497 al_prim_some_dfcl_male
1498 al_prim_some_dfcl_rur
1499 al_prim_some_dfcl_urb
1500 al_seco_al_alot_dfcl_1529
1501 al_seco_al_alot_dfcl_3044
1502 al_seco_al_alot_dfcl_4564
1503 al_seco_al_alot_dfcl_65up
1504 al_seco_al_alot_dfcl_all
1505 al_seco_al_alot_dfcl_fem
1506 al_seco_al_alot_dfcl_male
1507 al_seco_al_alot_dfcl_rur
1508 al_seco_al_alot_dfcl_urb
1509 al_seco_any_dfcl_1529
1510 al_seco_any_dfcl_3044
1511 al_seco_any_dfcl_4564
1512 al_seco_any_dfcl_65up
1513 al_seco_any_dfcl_all
1514 al_seco_any_dfcl_cogn_all
1515 al_seco_any_dfcl_comm_all
1516 al_seco_any_dfcl_fem
1517 al_seco_any_dfcl_hearing_all
1518 al_seco_any_dfcl_male
1519 al_seco_any_dfcl_mobile_all
1520 al_seco_any_dfcl_rur
1521 al_seco_any_dfcl_seeing_all
1522 al_seco_any_dfcl_selfcare_all
1523 al_seco_any_dfcl_urb
1524 al_seco_none_dfcl_1529
1525 al_seco_none_dfcl_3044
1526 al_seco_none_dfcl_4564
1527 al_seco_none_dfcl_65up
1528 al_seco_none_dfcl_all
1529 al_seco_none_dfcl_fem
1530 al_seco_none_dfcl_male
1531 al_seco_none_dfcl_rur
1532 al_seco_none_dfcl_urb
1533 al_seco_nosome_dfcl_1529
1534 al_seco_nosome_dfcl_3044
1535 al_seco_nosome_dfcl_4564
1536 al_seco_nosome_dfcl_65up
1537 al_seco_nosome_dfcl_all
1538 al_seco_nosome_dfcl_fem
1539 al_seco_nosome_dfcl_male
1540 al_seco_nosome_dfcl_rur
1541 al_seco_nosome_dfcl_urb
1542 al_seco_some_dfcl_1529
1543 al_seco_some_dfcl_3044
1544 al_seco_some_dfcl_4564
1545 al_seco_some_dfcl_65up
1546 al_seco_some_dfcl_all
1547 al_seco_some_dfcl_fem
1548 al_seco_some_dfcl_male
1549 al_seco_some_dfcl_rur
1550 al_seco_some_dfcl_urb
1670 BAR.NOED.1519.FE.ZS
1671 BAR.NOED.1519.ZS
1672 BAR.NOED.15UP.FE.ZS
1673 BAR.NOED.15UP.ZS
1674 BAR.NOED.2024.FE.ZS
1675 BAR.NOED.2024.ZS
1676 BAR.NOED.2529.FE.ZS
1677 BAR.NOED.2529.ZS
1678 BAR.NOED.25UP.FE.ZS
1679 BAR.NOED.25UP.ZS
1680 BAR.NOED.3034.FE.ZS
1681 BAR.NOED.3034.ZS
1682 BAR.NOED.3539.FE.ZS
1683 BAR.NOED.3539.ZS
1684 BAR.NOED.4044.FE.ZS
1685 BAR.NOED.4044.ZS
1686 BAR.NOED.4549.FE.ZS
1687 BAR.NOED.4549.ZS
1688 BAR.NOED.5054.FE.ZS
1689 BAR.NOED.5054.ZS
1690 BAR.NOED.5559.FE.ZS
1691 BAR.NOED.5559.ZS
1692 BAR.NOED.6064.FE.ZS
1693 BAR.NOED.6064.ZS
1694 BAR.NOED.6569.FE.ZS
1695 BAR.NOED.6569.ZS
1696 BAR.NOED.7074.FE.ZS
1697 BAR.NOED.7074.ZS
1698 BAR.NOED.75UP.FE.ZS
1699 BAR.NOED.75UP.ZS
2102 BI.EMP.FRML.ED.PB.ZS
2105 BI.EMP.FRML.PB.ED.ZS
2115 BI.EMP.PUBS.FE.ED.ZS
2124 BI.EMP.PWRK.ED.PB.ZS
2128 BI.EMP.PWRK.PB.ED.ZS
2146 BI.EMP.TOTL.ED.PB.ZS
2151 BI.EMP.TOTL.NO.ED
2159 BI.EMP.TOTL.PB.ED.ZS
2172 BI.EMP.TOTL.PB.TT.ZS
2180 BI.PWK.AGES.PB.ED.MD
2181 BI.PWK.AGES.PB.ED.SM
2196 BI.PWK.AGES.PV.ED.MD
2197 BI.PWK.AGES.PV.ED.SM
2234 BI.PWK.PRVS.NN.ZS
2236 BI.PWK.PRVS.PR.ZS
2238 BI.PWK.PRVS.SG.ZS
2242 BI.PWK.PRVS.TT.ED.ZS
2243 BI.PWK.PRVS.TT.HE.ZS
2244 BI.PWK.PRVS.TT.MW.ZS
2245 BI.PWK.PRVS.TT.TS.ZS
2246 BI.PWK.PRVS.TT.ZS
2251 BI.PWK.PUBS.ED.FE.ZS
2262 BI.PWK.PUBS.NN.ZS
2265 BI.PWK.PUBS.NO.ED
2274 BI.PWK.PUBS.PR.ZS
2277 BI.PWK.PUBS.RU.ED.ZS
2285 BI.PWK.PUBS.SG.ZS
2291 BI.PWK.PUBS.TT.CA.ZS
2292 BI.PWK.PUBS.TT.ED.ZS
2293 BI.PWK.PUBS.TT.HE.ZS
2294 BI.PWK.PUBS.TT.MW.ZS
2295 BI.PWK.PUBS.TT.PA.ZS
2296 BI.PWK.PUBS.TT.PS.ZS
2297 BI.PWK.PUBS.TT.SS.ZS
2298 BI.PWK.PUBS.TT.TS.ZS
2299 BI.PWK.PUBS.TT.ZS
2303 BI.PWK.TOTL.NO.ED
2321 BI.WAG.PREM.ED
2322 BI.WAG.PREM.ED.GP
2323 BI.WAG.PREM.FE.ED
2329 BI.WAG.PREM.MA.ED
2337 BI.WAG.PREM.PB.ED
2338 BI.WAG.PREM.PB.ED.PE
2339 BI.WAG.PREM.PB.ED.WE
2342 BI.WAG.PREM.PB.FE.ED
2349 BI.WAG.PREM.PB.FM.ED
2358 BI.WAG.PREM.PB.MA.ED
2364 BI.WAG.PREM.PB.NN
2367 BI.WAG.PREM.PB.PR
2369 BI.WAG.PREM.PB.SG
2374 BI.WAG.PREM.PB.TT
2375 BI.WAG.PREM.PV.FM.ED
2381 BI.WAG.PRVS.ED.FM
2391 BI.WAG.PUBS.ED.FM
2507 borrow.any.5
2508 borrow.any.6
3031 CC.SE.CAT1.ZS
3032 CC.SE.CAT2.ZS
3033 CC.SE.CAT3.ZS
3034 CC.SE.CAT4.ZS
3035 CC.SE.NYRS.AVG
3292 DAK.EDU.CR
6241 DT.ODA.DACD.EDU.BAS.CD
6242 DT.ODA.DACD.EDU.CD
6243 DT.ODA.DACD.EDU.PSEC.CD
6244 DT.ODA.DACD.EDU.SEC.CD
6245 DT.ODA.DACD.EDU.UNKN.CD
7228 FB.CAP.INST.ST.DM
7229 FB.CAP.INST.ST.MS.AL
7230 FB.CAP.INST.ST.MS.GI
7231 FB.CAP.INST.ST.MS.GP
7232 FB.CAP.INST.ST.MS.IO
7233 FB.CAP.INST.ST.MS.WB
7234 FB.CAP.INST.ST.MU
7235 FB.CAP.INST.ST.SG
7236 FB.CAP.LEGL.DF.FE
7237 FB.CAP.POLI.FE.CTR.WS
7238 FB.CAP.POLI.G2P.FE
7239 FB.CAP.POLI.GL.AP
7240 FB.CAP.POLI.GL.SP
7241 FB.CAP.POLI.GL.WP
7242 FB.CAP.POLI.NM.5Y
7245 FB.CAP.POLI.PSC.CD.2Y
7246 FB.CAP.POLI.PSC.DT.FE
7247 FB.CAP.POLI.PSC.IP.2Y
7248 FB.CAP.POLI.PSC.JS.FE
7249 FB.CAP.POLI.PSC.NI
7250 FB.CAP.POLI.PSC.PR.FE
7251 FB.CAP.POLI.PSC.SS.FE
7252 FB.CAP.POLI.PSC.ST.FE
7253 FB.CAP.POLI.PSC.UN.FE
7255 FB.CAP.POLI.RE.AP
7256 FB.CAP.POLI.RE.SP
7257 FB.CAP.POLI.RE.WP
7258 FB.CAP.POLI.RG.DC.AP
7259 FB.CAP.POLI.RG.DC.SP
7260 FB.CAP.POLI.RG.DC.WP
7379 FB.FCP.INST.SA.MA.FE
7582 FB.INC.NSTR.FC.DV
7583 FB.INC.NSTR.FC.LN
7608 FC.XPD.EDU.CR
7690 fin1.t.d.5
7691 fin1.t.d.6
7758 fin14.1.d.5
7759 fin14.1.d.6
7773 fin14a.t.d.5
7774 fin14a.t.d.6
7786 fin14a1.d.5
7787 fin14a1.d.6
7799 fin14b.t.d.5
7800 fin14b.t.d.6
7820 fin15.t.d.2017.5
7821 fin15.t.d.2017.6
7835 fin16.t.d.5
7836 fin16.t.d.6
7856 fin17a.17a1.d.5
7857 fin17a.17a1.d.6
7869 fin17a.t.d.5
7870 fin17a.t.d.6
7890 fin17a1.d.5
7891 fin17a1.d.6
7903 fin17b.t.d.5
7904 fin17b.t.d.6
7908 fin17c.d.2014
7909 fin17c.d.2014.1
7910 fin17c.d.2014.10
7911 fin17c.d.2014.11
7912 fin17c.d.2014.12
7913 fin17c.d.2014.2
7914 fin17c.d.2014.3
7915 fin17c.d.2014.4
7916 fin17c.d.2014.5
7917 fin17c.d.2014.6
7918 fin17c.d.2014.7
7919 fin17c.d.2014.8
7920 fin17c.d.2014.9
7930 fin19.t.2017.5
7931 fin19.t.2017.6
7943 fin2.7.t.d.5
7944 fin2.7.t.d.6
7956 fin2.t.d.5
7957 fin2.t.d.6
7969 fin20.t.d.5
7970 fin20.t.d.6
7982 fin21.t.d.2017.5
7983 fin21.t.d.2017.6
7995 fin21b.2014.5
7996 fin21b.2014.6
8000 fin22a.2014
8001 fin22a.2014.1
8002 fin22a.2014.10
8003 fin22a.2014.11
8004 fin22a.2014.12
8005 fin22a.2014.2
8006 fin22a.2014.3
8007 fin22a.2014.4
8008 fin22a.2014.5
8009 fin22a.2014.6
8010 fin22a.2014.7
8011 fin22a.2014.8
8012 fin22a.2014.9
8021 fin22a.c.MM.d.5
8022 fin22a.c.MM.d.6
8034 fin22a.c.t.d.5
8035 fin22a.c.t.d.6
8055 fin22b.t.d.5
8056 fin22b.t.d.6
8068 fin22c.t.d.5
8069 fin22c.t.d.6
8093 fin24a.1.d.5
8094 fin24a.1.d.6
8106 fin24a.2.d.5
8107 fin24a.2.d.6
8119 fin24a.3.d.5
8120 fin24a.3.d.6
8132 fin24a.32.d.5
8133 fin24a.32.d.6
8145 fin24a.321.d.5
8146 fin24a.321.d.6
8158 fin24a.N.d.5
8159 fin24a.N.d.6
8171 fin24b.1.d.5
8172 fin24b.1.d.6
8184 fin24b.2.d.5
8185 fin24b.2.d.6
8197 fin24b.3.d.5
8198 fin24b.3.d.6
8210 fin24b.32.d.5
8211 fin24b.32.d.6
8223 fin24b.321.d.5
8224 fin24b.321.d.6
8244 fin26.28.t.d.5
8245 fin26.28.t.d.6
8257 fin26.t.d.5
8258 fin26.t.d.6
8282 fin28.t.d.5
8283 fin28.t.d.6
8303 fin30.t.d.5
8304 fin30.t.d.6
8335 fin32.33.t.d.5
8336 fin32.33.t.d.6
8360 fin32.n33.t.d.5
8361 fin32.n33.t.d.6
8373 fin32.t.d.5
8374 fin32.t.d.6
8386 fin33.2014.d.5
8387 fin33.2014.d.6
8429 fin37.38.t.d.5
8430 fin37.38.t.d.6
8452 fin37.t.d.5
8453 fin37.t.d.6
8475 fin38.t.d.5
8476 fin38.t.d.6
8491 fin42.t.d.5
8492 fin42.t.d.6
8514 fin44a1.d.5
8515 fin44a1.d.6
8527 fin44a2.d.5
8528 fin44a2.d.6
8540 fin44a3.d.5
8541 fin44a3.d.6
8553 fin44b1.d.5
8554 fin44b1.d.6
8566 fin44b2.d.5
8567 fin44b2.d.6
8579 fin44b3.d.5
8580 fin44b3.d.6
8592 fin44c1.d.5
8593 fin44c1.d.6
8605 fin44c2.d.5
8606 fin44c2.d.6
8618 fin44c3.d.5
8619 fin44c3.d.6
8623 fin44d1.d
8624 fin44d1.d.1
8625 fin44d1.d.10
8626 fin44d1.d.11
8627 fin44d1.d.12
8628 fin44d1.d.2
8629 fin44d1.d.3
8630 fin44d1.d.4
8631 fin44d1.d.5
8632 fin44d1.d.6
8633 fin44d1.d.7
8634 fin44d1.d.8
8635 fin44d1.d.9
8636 fin44d2.d
8637 fin44d2.d.1
8638 fin44d2.d.10
8639 fin44d2.d.11
8640 fin44d2.d.12
8641 fin44d2.d.2
8642 fin44d2.d.3
8643 fin44d2.d.4
8644 fin44d2.d.5
8645 fin44d2.d.6
8646 fin44d2.d.7
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14198 PER.HH.Q3.SHARE
14199 PER.HH.Q4.SHARE
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14220 PER.JR.QLT.Q1
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14280 PER.OC.GEO.RRL.PP
14282 PER.OC.GEO.RRL.PRM
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14435 PER.PRM.QLT.Q1
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14437 PER.PRM.QLT.Q3
14438 PER.PRM.QLT.Q4
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14462 PER.PRM.TOT.CPL
14463 PER.PRM.TOTEXP
14466 PER.PRM.TTL.GOV
14468 PER.PVT.PRM.TOT
14469 PER.PVT.SEC.TOT
14470 PER.PVT.TER.TOT
14471 PER.RRL.HH.SHARE
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14475 PER.SAL.MOE.MONTH
14490 PER.SEC.CPL.TOT
14497 PER.SEC.EXE.CPL
14498 PER.SEC.EXE.RCT
14500 PER.SEC.EXE.TOT
14501 PER.SEC.GDP
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14505 PER.SEC.HH.TRNSPT
14506 PER.SEC.HH.TUIT
14507 PER.SEC.HH.UNIF
14508 PER.SEC.ITL.SHARE
14517 PER.SEC.PS.GDP
14521 PER.SEC.PS.RCT.RATIO
14530 PER.SEC.QTL.NP
14531 PER.SEC.QTL.P40
14532 PER.SEC.QTL.POOR
14533 PER.SEC.QTL.Q1
14534 PER.SEC.QTL.Q2
14535 PER.SEC.QTL.Q3
14536 PER.SEC.QTL.Q4
14537 PER.SEC.QTL.Q5
14538 PER.SEC.QTL.R40
14542 PER.SEC.RCT.TOT
14543 PER.SEC.SAL
14547 PER.SEC.SAL.RCT
14550 PER.SEC.SAL.TOT
14556 PER.SEC.TOT
14557 PER.SEC.TOT.CPL
14558 PER.SEC.TOTEXP
14561 PER.SOR.BAS.CTL
14562 PER.SOR.BAS.DIST
14563 PER.SOR.BAS.PROV
14564 PER.SOR.CTL.BAS
14565 PER.SOR.CTL.CAP
14566 PER.SOR.CTL.CPL
14567 PER.SOR.CTL.JR
14568 PER.SOR.CTL.PP
14569 PER.SOR.CTL.PRM
14570 PER.SOR.CTL.RCT
14571 PER.SOR.CTL.RCT.SHARE
14572 PER.SOR.CTL.SAL
14573 PER.SOR.CTL.SAL.SHARE
14574 PER.SOR.CTL.SEC
14575 PER.SOR.CTL.SR
14576 PER.SOR.CTL.TER
14577 PER.SOR.CTL.TOT
14578 PER.SOR.CTL.TOT.BAS
14579 PER.SOR.CTL.TOT.PP
14580 PER.SOR.CTL.TOT.PRM
14581 PER.SOR.CTL.TOT.SEC
14582 PER.SOR.CTL.TOT.TER
14583 PER.SOR.CTL.TOT.VOC
14584 PER.SOR.CTL.VOC
14585 PER.SOR.DIST
14586 PER.SOR.DIST.BAS
14587 PER.SOR.DIST.CTL
14588 PER.SOR.DIST.JR
14589 PER.SOR.DIST.LOC
14590 PER.SOR.DIST.PP
14591 PER.SOR.DIST.PRM
14592 PER.SOR.DIST.REG
14593 PER.SOR.DIST.REV
14594 PER.SOR.DIST.SEC
14595 PER.SOR.DIST.SR
14596 PER.SOR.DIST.SUB
14597 PER.SOR.DIST.TER
14598 PER.SOR.DIST.VOC
14600 PER.SOR.FEE.JR.SHARE
14601 PER.SOR.FEE.PP.SHARE
14603 PER.SOR.FEE.PRM.SHARE
14605 PER.SOR.FEE.SEC.SHARE
14607 PER.SOR.FEE.SR.SHARE
14609 PER.SOR.FEE.TER.SHARE
14610 PER.SOR.FEE.TOT
14612 PER.SOR.GOV.GDP
14617 PER.SOR.GOV.PS.VOC
14620 PER.SOR.GOV.TER
14621 PER.SOR.GOV.TOT
14622 PER.SOR.GOV.TOT.CPL
14628 PER.SOR.HH.LOC
14629 PER.SOR.HH.P20.PRM
14630 PER.SOR.HH.P20.SEC
14631 PER.SOR.HH.P20.TER
14632 PER.SOR.HH.P40
14634 PER.SOR.HH.Q1
14635 PER.SOR.HH.Q1.PRM
14636 PER.SOR.HH.Q1.SEC
14637 PER.SOR.HH.Q1.TER
14638 PER.SOR.HH.Q2
14639 PER.SOR.HH.Q2.PRM
14640 PER.SOR.HH.Q2.SEC
14641 PER.SOR.HH.Q2.TER
14642 PER.SOR.HH.Q3
14643 PER.SOR.HH.Q3.PRM
14644 PER.SOR.HH.Q3.SEC
14645 PER.SOR.HH.Q3.TER
14646 PER.SOR.HH.Q4
14647 PER.SOR.HH.Q4.PRM
14648 PER.SOR.HH.Q4.SEC
14649 PER.SOR.HH.Q4.TER
14650 PER.SOR.HH.Q5
14651 PER.SOR.HH.Q5.PRM
14652 PER.SOR.HH.Q5.SEC
14653 PER.SOR.HH.Q5.TER
14654 PER.SOR.HH.R20.PRM
14655 PER.SOR.HH.R20.SEC
14656 PER.SOR.HH.R20.TER
14657 PER.SOR.HH.R40
14661 PER.SOR.HH.TOT
14662 PER.SOR.HH.USD
14663 PER.SOR.HH.USD.BAS
14664 PER.SOR.HH.USD.JR
14665 PER.SOR.HH.USD.PP
14666 PER.SOR.HH.USD.PRM
14667 PER.SOR.HH.USD.SEC
14668 PER.SOR.HH.USD.SR
14669 PER.SOR.HH.USD.TER
14670 PER.SOR.ITL.BAS
14671 PER.SOR.ITL.CPL
14672 PER.SOR.ITL.GDP
14674 PER.SOR.ITL.OFF
14681 PER.SOR.ITL.TOT
14682 PER.SOR.ITL.USD
14684 PER.SOR.PP.CTL
14685 PER.SOR.PP.DIST
14686 PER.SOR.PP.PROV
14687 PER.SOR.PRM.CTL
14688 PER.SOR.PRM.DIST
14689 PER.SOR.PRM.PROV
14690 PER.SOR.PROV
14691 PER.SOR.PROV.REV
14692 PER.SOR.PRT.SEC
14693 PER.SOR.PVT.GDP
14694 PER.SOR.PVT.GDP.PP
14695 PER.SOR.PVT.GDP.PRM
14696 PER.SOR.PVT.GDP.SEC
14697 PER.SOR.PVT.GDP.TER
14698 PER.SOR.PVT.JR
14699 PER.SOR.PVT.PCT
14700 PER.SOR.PVT.PCT.PRM
14701 PER.SOR.PVT.PCT.Q1
14702 PER.SOR.PVT.PCT.Q2
14703 PER.SOR.PVT.PCT.Q3
14704 PER.SOR.PVT.PCT.Q4
14705 PER.SOR.PVT.PCT.Q5
14706 PER.SOR.PVT.PCT.SEC
14707 PER.SOR.PVT.PCT.TER
14708 PER.SOR.PVT.PP
14709 PER.SOR.PVT.PRM
14710 PER.SOR.PVT.SR
14711 PER.SOR.PVT.TER
14712 PER.SOR.PVT.TOT
14713 PER.SOR.PVT.VOC
14714 PER.SOR.SEC.CTL
14715 PER.SOR.SEC.DIST
14716 PER.SOR.SEC.PROV
14717 PER.SOR.SUB.BAS
14718 PER.SOR.SUB.CAP
14719 PER.SOR.SUB.CPL
14720 PER.SOR.SUB.PP
14721 PER.SOR.SUB.PRM
14722 PER.SOR.SUB.RCT
14723 PER.SOR.SUB.RCT.SHARE
14724 PER.SOR.SUB.REV
14726 PER.SOR.SUB.SAL.SHARE
14727 PER.SOR.SUB.SEC
14728 PER.SOR.SUB.STAFF
14729 PER.SOR.SUB.TER
14730 PER.SOR.SUB.VOC
14731 PER.SOR.TER.CTL
14732 PER.SOR.TER.DIST
14733 PER.SOR.TER.PROV
14734 PER.SOR.TOT.CTL
14735 PER.SOR.TOT.SUB
14736 PER.SOR.VOC.CTL
14737 PER.SOR.VOC.DIST
14738 PER.SOR.VOC.PROV
14739 PER.SP.ITL.SHARE
14740 PER.SP.SAL
14741 PER.SP.SOR.GOV
14742 PER.SP.SOR.ITL
14745 PER.SR.CPL.TOT
14749 PER.SR.ITL.SHARE
14752 PER.SR.PS.GDP
14759 PER.SR.QLT.Q1
14760 PER.SR.QLT.Q2
14761 PER.SR.QLT.Q3
14762 PER.SR.QLT.Q4
14763 PER.SR.QLT.Q5
14764 PER.SR.QTL.P40
14765 PER.SR.QTL.R40
14767 PER.SR.RCT.TOT
14770 PER.SR.SAL.RCT
14772 PER.SR.SAL.TOT
14774 PER.SR.SOR.GOV
14776 PER.SR.TEACH.SHARE
14778 PER.SR.TOT
14779 PER.SR.TOTEXP
14780 PER.SR.TRANSFER.PS
14782 PER.SUB.DIST.CAP
14783 PER.SUB.DIST.RCT
14784 PER.SUB.DIST.SAL
14785 PER.SUB.PROV.CAP
14786 PER.SUB.PROV.RCT
14787 PER.SUB.PROV.SAL
14788 PER.TEACH.RCT.PS
14789 PER.TEACH.SAL.PS
14792 PER.TER.CPL.TOT
14795 PER.TER.EXE.CPL
14796 PER.TER.EXE.RCT
14798 PER.TER.EXE.TOT
14801 PER.TER.GDP
14803 PER.TER.HH.BOARD
14804 PER.TER.HH.TEXT
14805 PER.TER.HH.TRNSPT
14806 PER.TER.HH.UNIF
14807 PER.TER.ITL.SHARE
14809 PER.TER.PS.GDP
14810 PER.TER.PS.NONSAL
14812 PER.TER.PS.RCT.RATIO
14819 PER.TER.QTL.NP
14820 PER.TER.QTL.P40
14821 PER.TER.QTL.POOR
14822 PER.TER.QTL.Q1
14823 PER.TER.QTL.Q2
14824 PER.TER.QTL.Q3
14825 PER.TER.QTL.Q4
14826 PER.TER.QTL.Q5
14827 PER.TER.QTL.R40
14831 PER.TER.RCT.TOT
14832 PER.TER.SAL
14835 PER.TER.SAL.RCT
14838 PER.TER.SAL.TOT
14840 PER.TER.SHARE.PRIV
14841 PER.TER.TEACH.SHARE
14845 PER.TER.TOT
14846 PER.TER.TOT.CPL
14847 PER.TER.TOTEXP
14851 PER.TT.CAP.SHARE
14853 PER.TT.ITL.SHARE
14856 PER.TT.PS.GDP
14859 PER.TT.SOR.GOV
14860 PER.TT.SOR.ITL
14861 PER.URB.HH.SHARE
14862 PER.URB.HH.TOTSHARE
14865 PER.VOC.CPL.TOT
14866 PER.VOC.EXE.CPL
14867 PER.VOC.EXE.RCT
14868 PER.VOC.EXE.TOT
14871 PER.VOC.GDP
14872 PER.VOC.GS.RCT
14873 PER.VOC.HH.PS
14874 PER.VOC.PCR
14875 PER.VOC.PPC
14876 PER.VOC.PS.GDP
14877 PER.VOC.PS.NONSAL
14879 PER.VOC.PS.USD
14880 PER.VOC.PSTF
14881 PER.VOC.PTR
14883 PER.VOC.QTL.P40
14884 PER.VOC.QTL.Q1
14885 PER.VOC.QTL.Q2
14886 PER.VOC.QTL.Q3
14887 PER.VOC.QTL.Q4
14888 PER.VOC.QTL.Q5
14889 PER.VOC.QTL.R40
14892 PER.VOC.RCT.TOT
14893 PER.VOC.SAL
14894 PER.VOC.SAL.RCT
14897 PER.VOC.SAL.USDLOC
14898 PER.VOC.TEACH.SHARE
14899 PER.VOC.TNR
14900 PER.VOC.TOT
14901 PER.VOC.TOT.CPL
14902 PER.VOC.TOTEXP
14903 PER.VOC.TRANSFER.RCT
17727 PRJ.ATT.1519.1.FE
17728 PRJ.ATT.1519.1.MA
17729 PRJ.ATT.1519.1.MF
17730 PRJ.ATT.1519.2.FE
17731 PRJ.ATT.1519.2.MA
17732 PRJ.ATT.1519.2.MF
17733 PRJ.ATT.1519.3.FE
17734 PRJ.ATT.1519.3.MA
17735 PRJ.ATT.1519.3.MF
17736 PRJ.ATT.1519.4.FE
17737 PRJ.ATT.1519.4.MA
17738 PRJ.ATT.1519.4.MF
17739 PRJ.ATT.1519.NED.FE
17740 PRJ.ATT.1519.NED.MA
17741 PRJ.ATT.1519.NED.MF
17742 PRJ.ATT.1519.S1.FE
17743 PRJ.ATT.1519.S1.MA
17744 PRJ.ATT.1519.S1.MF
17745 PRJ.ATT.15UP.1.FE
17746 PRJ.ATT.15UP.1.MA
17747 PRJ.ATT.15UP.1.MF
17748 PRJ.ATT.15UP.2.FE
17749 PRJ.ATT.15UP.2.MA
17750 PRJ.ATT.15UP.2.MF
17751 PRJ.ATT.15UP.3.FE
17752 PRJ.ATT.15UP.3.MA
17753 PRJ.ATT.15UP.3.MF
17754 PRJ.ATT.15UP.4.FE
17755 PRJ.ATT.15UP.4.MA
17756 PRJ.ATT.15UP.4.MF
17757 PRJ.ATT.15UP.NED.FE
17758 PRJ.ATT.15UP.NED.MA
17759 PRJ.ATT.15UP.NED.MF
17760 PRJ.ATT.15UP.S1.FE
17761 PRJ.ATT.15UP.S1.MA
17762 PRJ.ATT.15UP.S1.MF
17763 PRJ.ATT.2024.1.FE
17764 PRJ.ATT.2024.1.MA
17765 PRJ.ATT.2024.1.MF
17766 PRJ.ATT.2024.2.FE
17767 PRJ.ATT.2024.2.MA
17768 PRJ.ATT.2024.2.MF
17769 PRJ.ATT.2024.3.FE
17770 PRJ.ATT.2024.3.MA
17771 PRJ.ATT.2024.3.MF
17772 PRJ.ATT.2024.4.FE
17773 PRJ.ATT.2024.4.MA
17774 PRJ.ATT.2024.4.MF
17775 PRJ.ATT.2024.NED.FE
17776 PRJ.ATT.2024.NED.MA
17777 PRJ.ATT.2024.NED.MF
17778 PRJ.ATT.2024.S1.FE
17779 PRJ.ATT.2024.S1.MA
17780 PRJ.ATT.2024.S1.MF
17781 PRJ.ATT.2039.1.FE
17782 PRJ.ATT.2039.1.MA
17783 PRJ.ATT.2039.1.MF
17784 PRJ.ATT.2039.2.FE
17785 PRJ.ATT.2039.2.MA
17786 PRJ.ATT.2039.2.MF
17787 PRJ.ATT.2039.3.FE
17788 PRJ.ATT.2039.3.MA
17789 PRJ.ATT.2039.3.MF
17790 PRJ.ATT.2039.4.FE
17791 PRJ.ATT.2039.4.MA
17792 PRJ.ATT.2039.4.MF
17793 PRJ.ATT.2039.NED.FE
17794 PRJ.ATT.2039.NED.MA
17795 PRJ.ATT.2039.NED.MF
17796 PRJ.ATT.2039.S1.FE
17797 PRJ.ATT.2039.S1.MA
17798 PRJ.ATT.2039.S1.MF
17799 PRJ.ATT.2064.1.FE
17800 PRJ.ATT.2064.1.MA
17801 PRJ.ATT.2064.1.MF
17802 PRJ.ATT.2064.2.FE
17803 PRJ.ATT.2064.2.MA
17804 PRJ.ATT.2064.2.MF
17805 PRJ.ATT.2064.3.FE
17806 PRJ.ATT.2064.3.MA
17807 PRJ.ATT.2064.3.MF
17808 PRJ.ATT.2064.4.FE
17809 PRJ.ATT.2064.4.MA
17810 PRJ.ATT.2064.4.MF
17811 PRJ.ATT.2064.NED.FE
17812 PRJ.ATT.2064.NED.MA
17813 PRJ.ATT.2064.NED.MF
17814 PRJ.ATT.2064.S1.FE
17815 PRJ.ATT.2064.S1.MA
17816 PRJ.ATT.2064.S1.MF
17817 PRJ.ATT.2529.1.FE
17818 PRJ.ATT.2529.1.MA
17819 PRJ.ATT.2529.1.MF
17820 PRJ.ATT.2529.2.FE
17821 PRJ.ATT.2529.2.MA
17822 PRJ.ATT.2529.2.MF
17823 PRJ.ATT.2529.3.FE
17824 PRJ.ATT.2529.3.MA
17825 PRJ.ATT.2529.3.MF
17826 PRJ.ATT.2529.4.FE
17827 PRJ.ATT.2529.4.MA
17828 PRJ.ATT.2529.4.MF
17829 PRJ.ATT.2529.NED.FE
17830 PRJ.ATT.2529.NED.MA
17831 PRJ.ATT.2529.NED.MF
17832 PRJ.ATT.2529.S1.FE
17833 PRJ.ATT.2529.S1.MA
17834 PRJ.ATT.2529.S1.MF
17835 PRJ.ATT.25UP.1.FE
17836 PRJ.ATT.25UP.1.MA
17837 PRJ.ATT.25UP.1.MF
17838 PRJ.ATT.25UP.2.FE
17839 PRJ.ATT.25UP.2.MA
17840 PRJ.ATT.25UP.2.MF
17841 PRJ.ATT.25UP.3.FE
17842 PRJ.ATT.25UP.3.MA
17843 PRJ.ATT.25UP.3.MF
17844 PRJ.ATT.25UP.4.FE
17845 PRJ.ATT.25UP.4.MA
17846 PRJ.ATT.25UP.4.MF
17847 PRJ.ATT.25UP.NED.FE
17848 PRJ.ATT.25UP.NED.MA
17849 PRJ.ATT.25UP.NED.MF
17850 PRJ.ATT.25UP.S1.FE
17851 PRJ.ATT.25UP.S1.MA
17852 PRJ.ATT.25UP.S1.MF
17853 PRJ.ATT.4064.1.FE
17854 PRJ.ATT.4064.1.MA
17855 PRJ.ATT.4064.1.MF
17856 PRJ.ATT.4064.2.FE
17857 PRJ.ATT.4064.2.MA
17858 PRJ.ATT.4064.2.MF
17859 PRJ.ATT.4064.3.FE
17860 PRJ.ATT.4064.3.MA
17861 PRJ.ATT.4064.3.MF
17862 PRJ.ATT.4064.4.FE
17863 PRJ.ATT.4064.4.MA
17864 PRJ.ATT.4064.4.MF
17865 PRJ.ATT.4064.NED.FE
17866 PRJ.ATT.4064.NED.MA
17867 PRJ.ATT.4064.NED.MF
17868 PRJ.ATT.4064.S1.FE
17869 PRJ.ATT.4064.S1.MA
17870 PRJ.ATT.4064.S1.MF
17871 PRJ.ATT.60UP.1.FE
17872 PRJ.ATT.60UP.1.MA
17873 PRJ.ATT.60UP.1.MF
17874 PRJ.ATT.60UP.2.FE
17875 PRJ.ATT.60UP.2.MA
17876 PRJ.ATT.60UP.2.MF
17877 PRJ.ATT.60UP.3.FE
17878 PRJ.ATT.60UP.3.MA
17879 PRJ.ATT.60UP.3.MF
17880 PRJ.ATT.60UP.4.FE
17881 PRJ.ATT.60UP.4.MA
17882 PRJ.ATT.60UP.4.MF
17883 PRJ.ATT.60UP.NED.FE
17884 PRJ.ATT.60UP.NED.MA
17885 PRJ.ATT.60UP.NED.MF
17886 PRJ.ATT.60UP.S1.FE
17887 PRJ.ATT.60UP.S1.MA
17888 PRJ.ATT.60UP.S1.MF
17889 PRJ.ATT.80UP.1.FE
17890 PRJ.ATT.80UP.1.MA
17891 PRJ.ATT.80UP.1.MF
17892 PRJ.ATT.80UP.2.FE
17893 PRJ.ATT.80UP.2.MA
17894 PRJ.ATT.80UP.2.MF
17895 PRJ.ATT.80UP.3.FE
17896 PRJ.ATT.80UP.3.MA
17897 PRJ.ATT.80UP.3.MF
17898 PRJ.ATT.80UP.4.FE
17899 PRJ.ATT.80UP.4.MA
17900 PRJ.ATT.80UP.4.MF
17901 PRJ.ATT.80UP.NED.FE
17902 PRJ.ATT.80UP.NED.MA
17903 PRJ.ATT.80UP.NED.MF
17904 PRJ.ATT.80UP.S1.FE
17905 PRJ.ATT.80UP.S1.MA
17906 PRJ.ATT.80UP.S1.MF
17907 PRJ.ATT.ALL.1.FE
17908 PRJ.ATT.ALL.1.MA
17909 PRJ.ATT.ALL.1.MF
17910 PRJ.ATT.ALL.2.FE
17911 PRJ.ATT.ALL.2.MA
17912 PRJ.ATT.ALL.2.MF
17913 PRJ.ATT.ALL.3.FE
17914 PRJ.ATT.ALL.3.MA
17915 PRJ.ATT.ALL.3.MF
17916 PRJ.ATT.ALL.4.FE
17917 PRJ.ATT.ALL.4.MA
17918 PRJ.ATT.ALL.4.MF
17919 PRJ.ATT.ALL.NED.FE
17920 PRJ.ATT.ALL.NED.MA
17921 PRJ.ATT.ALL.NED.MF
17922 PRJ.ATT.ALL.S1.FE
17923 PRJ.ATT.ALL.S1.MA
17924 PRJ.ATT.ALL.S1.MF
17963 PRJ.POP.1519.1.FE
17964 PRJ.POP.1519.1.MA
17965 PRJ.POP.1519.1.MF
17966 PRJ.POP.1519.2.FE
17967 PRJ.POP.1519.2.MA
17968 PRJ.POP.1519.2.MF
17969 PRJ.POP.1519.3.FE
17970 PRJ.POP.1519.3.MA
17971 PRJ.POP.1519.3.MF
17972 PRJ.POP.1519.4.FE
17973 PRJ.POP.1519.4.MA
17974 PRJ.POP.1519.4.MF
17975 PRJ.POP.1519.NED.FE
17976 PRJ.POP.1519.NED.MA
17977 PRJ.POP.1519.NED.MF
17978 PRJ.POP.1519.S1.FE
17979 PRJ.POP.1519.S1.MA
17980 PRJ.POP.1519.S1.MF
17981 PRJ.POP.2024.1.FE
17982 PRJ.POP.2024.1.MA
17983 PRJ.POP.2024.1.MF
17984 PRJ.POP.2024.2.FE
17985 PRJ.POP.2024.2.MA
17986 PRJ.POP.2024.2.MF
17987 PRJ.POP.2024.3.FE
17988 PRJ.POP.2024.3.MA
17989 PRJ.POP.2024.3.MF
17990 PRJ.POP.2024.4.FE
17991 PRJ.POP.2024.4.MA
17992 PRJ.POP.2024.4.MF
17993 PRJ.POP.2024.NED.FE
17994 PRJ.POP.2024.NED.MA
17995 PRJ.POP.2024.NED.MF
17996 PRJ.POP.2024.S1.FE
17997 PRJ.POP.2024.S1.MA
17998 PRJ.POP.2024.S1.MF
17999 PRJ.POP.2529.1.FE
18000 PRJ.POP.2529.1.MA
18001 PRJ.POP.2529.1.MF
18002 PRJ.POP.2529.2.FE
18003 PRJ.POP.2529.2.MA
18004 PRJ.POP.2529.2.MF
18005 PRJ.POP.2529.3.FE
18006 PRJ.POP.2529.3.MA
18007 PRJ.POP.2529.3.MF
18008 PRJ.POP.2529.4.FE
18009 PRJ.POP.2529.4.MA
18010 PRJ.POP.2529.4.MF
18011 PRJ.POP.2529.NED.FE
18012 PRJ.POP.2529.NED.MA
18013 PRJ.POP.2529.NED.MF
18014 PRJ.POP.2529.S1.FE
18015 PRJ.POP.2529.S1.MA
18016 PRJ.POP.2529.S1.MF
18017 PRJ.POP.ALL.1.FE
18018 PRJ.POP.ALL.1.MA
18019 PRJ.POP.ALL.1.MF
18020 PRJ.POP.ALL.2.FE
18021 PRJ.POP.ALL.2.MA
18022 PRJ.POP.ALL.2.MF
18023 PRJ.POP.ALL.3.FE
18024 PRJ.POP.ALL.3.MA
18025 PRJ.POP.ALL.3.MF
18026 PRJ.POP.ALL.4.FE
18027 PRJ.POP.ALL.4.MA
18028 PRJ.POP.ALL.4.MF
18029 PRJ.POP.ALL.NED.FE
18030 PRJ.POP.ALL.NED.MA
18031 PRJ.POP.ALL.NED.MF
18032 PRJ.POP.ALL.S1.FE
18033 PRJ.POP.ALL.S1.MA
18034 PRJ.POP.ALL.S1.MF
18229 SABER.EMIS.GOAL1
18230 SABER.EMIS.GOAL1.LVL1
18231 SABER.EMIS.GOAL1.LVL2
18232 SABER.EMIS.GOAL1.LVL3
18233 SABER.EMIS.GOAL1.LVL4
18234 SABER.EMIS.GOAL1.LVL5
18235 SABER.EMIS.GOAL1.LVL6
18236 SABER.EMIS.GOAL2
18237 SABER.EMIS.GOAL2.LVL1
18238 SABER.EMIS.GOAL2.LVL2
18239 SABER.EMIS.GOAL2.LVL3
18240 SABER.EMIS.GOAL2.LVL4
18241 SABER.EMIS.GOAL2.LVL5
18242 SABER.EMIS.GOAL3
18243 SABER.EMIS.GOAL3.LVL1
18244 SABER.EMIS.GOAL3.LVL2
18245 SABER.EMIS.GOAL3.LVL3
18246 SABER.EMIS.GOAL3.LVL4
18247 SABER.EMIS.GOAL4
18248 SABER.EMIS.GOAL4.LVL1
18249 SABER.EMIS.GOAL4.LVL2
18250 SABER.EMIS.GOAL4.LVL3
18251 SABER.EMIS.GOAL4.LVL4
18300 SABER.HLTH.GOAL9
18354 SABER.SCH.FNNC.GOAL1.LVL1
18355 SABER.SCH.FNNC.GOAL1.LVL2
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18432 SABER.TER.GOAL6.LVL3
18555 save.any.5
18556 save.any.6
20348 SE.COM.DURS
20371 SE.PRE.DURS
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20381 SE.PRM.BFIN.6
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20399 SE.PRM.ENRL
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21972 SL.UEM.NEET.ZS
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21975 SL.UEM.PRIM.ZS
21976 SL.UEM.SECO.FE.ZS
21977 SL.UEM.SECO.MA.ZS
21978 SL.UEM.SECO.ZS
21979 SL.UEM.TERT.FE.ZS
21980 SL.UEM.TERT.MA.ZS
21981 SL.UEM.TERT.ZS
22345 SPI.D3.4.EDUC
22693 UIS.FEP.2.V
22694 UIS.FEP.3.V
22695 UIS.FEP.4.V
22699 UIS.GTVP.2.V.F
22700 UIS.GTVP.2.V.M
22701 UIS.GTVP.3.V.F
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22760 WP_time_10.6
22761 WP_time_10.7
name
191 Lower secondary education, new teachers, national source
192 Primary education, new entrants, national source
194 Lower secondary education, classrooms, national source
195 Lower secondary education, new classrooms, national source
196 Ratio of textbooks per pupil, primary education, mathematics
197 Ratio of textbooks per pupil, primary education, language
202 Primary education, pupils, national source
203 Primary education, teachers, national source
204 Primary education, new teachers, national source
205 Primary education, classrooms, national source
206 Primary education, new classrooms, national source
207 Lower Secondary education, new entrants, national source
208 Lower secondary education, pupils, national source
209 Lower secondary education, teachers, national source
237 Public spending on total education (% of total public spending)
250 Public spending on basic education (% of public spending on total education)
251 Public recurrent spending on total education (% of total public recurrent spending)
252 Public recurrent spending on basic education (% of public recurrent spending on total education)
255 International aid disbursed to total education, CIDA to Afghanistan (USD million)
256 International aid disbursed to total education, World Bank to Albania (USD million)
257 International aid disbursed to total education, CIDA to Burkina Faso (USD million)
258 International aid disbursed to total education, Global Partnership for Education to Central African Republic (USD million)
259 International aid disbursed to total education, AfDB to Côte d'Ivoire (USD million)
260 International aid disbursed to total education, AfDB to Cameroun (USD million)
261 International aid disbursed to total education, World Bank (IDA) to Djibouti (USD million)
262 International aid disbursed to total education, ADB to Ethiopia (USD million)
263 International aid disbursed to total education, European Commission to Georgia (USD million)
264 International aid disbursed to total education, DFID to Djibouti (USD million)
265 International aid disbursed to total education, AFD to Guinea (USD million)
266 International aid disbursed to total education, ADPP (European Union) to Guinea Bissau (USD million)
267 International aid disbursed to total education, European Commission to Kyrgyzstan (USD million)
268 International aid disbursed to total education, AfDB to Cambodia (USD million)
269 International aid disbursed to total education, ADB to Laos (USD million)
270 International aid disbursed to total education, UNICEF to Liberia (USD million)
271 International aid disbursed to total education, UNICEF to Moldova (USD million)
272 International aid to total education executed by World Bank (including GPE funds) in Madagascar (USD million)
273 International aid disbursed to total education, Canada to Mozambique (USD million)
274 International aid disbursed to total education, AFD to Mauritania (USD million)
275 International aid disbursed to total education, AfDB to Malawi (USD million)
276 International aid disbursed to total education, AFD to Niger (USD million)
277 International aid disbursed to total education, DFID to Rwanda (USD million)
278 International aid disbursed to total education, CIDA to Senegal (USD million)
279 International aid disbursed to total education, DFID to Sierra Leone (USD million)
280 International aid disbursed to total education, Belgium to Vietnam (USD million)
281 International aid disbursed to total education, Denmark to Zambia (USD million)
282 International aid disbursed to total education, Sida to Afghanistan (USD million)
283 International aid disbursed to total education, Japan Government to Ethiopia (USD million)
284 International aid disbursed to total education, WFP to Cambodia (USD million)
285 International aid disbursed to total education, World Bank to Laos (USD million)
286 International aid to total education executed by the European Commission in Madagascar (USD million)
287 International aid disbursed to total education, Japan to Mozambique (USD million)
288 International aid disbursed to total education, WFP to Malawi (USD million)
289 International aid disbursed to total education, UNICEF to Niger (USD million)
290 International aid disbursed to total education, World Bank to Tajikistan (USD million)
291 International aid disbursed to total education, UNESCO to Afghanistan (USD million)
292 International aid disbursed to total education, JICA to Ethiopia (USD million)
293 International aid disbursed to total education, World Bank to Cambodia (USD million)
294 International aid disbursed to total education, International NGOs to Laos (USD million)
295 International aid disbursed to total education, Netherlands to Mozambique (USD million)
296 International aid disbursed to total education, World Bank to Malawi (USD million)
297 International aid disbursed to total education, USAID to Afghanistan (USD million)
298 International aid disbursed to total education, KfW to Ethiopia (USD million)
299 International aid disbursed to total education, Portugal to Mozambique (USD million)
300 International aid disbursed to total education, World Bank to Afghanistan (USD million)
301 International aid disbursed to total education, Netherlands to Ethiopia (USD million)
302 International aid disbursed to total education, Spain to Mozambique (USD million)
303 International aid disbursed to total education, Sida to Ethiopia (USD million)
304 International aid disbursed to total education, UNICEF to Mozambique (USD million)
305 International aid disbursed to total education, UNICEF to Ethiopia (USD million)
306 International aid disbursed to total education, USAID to Mozambique (USD million)
307 International aid disbursed to total education, USAID to Ethiopia (USD million)
308 International aid disbursed to total education, World Bank to Mozambique (USD million)
309 International aid disbursed to total education, WFP to Ethiopia (USD million)
310 International aid disbursed to total education, World Bank to Ethiopia (USD million)
311 International aid disbursed to total education, DANIDA to Afghanistan (USD million)
312 International aid disbursed to total education, BEI to Albania (USD million)
313 International aid disbursed to total education, AFD to Burkina Faso (USD million)
314 International aid disbursed to total education, BADEA to Côte d'Ivoire (USD million)
315 International aid disbursed to total education, World Bank to Cameroun (USD million)
316 International aid disbursed to total education, FSD to Djibouti (USD million)
317 International aid disbursed to total education, Belgium (VLIR USO) to Ethiopia (USD million)
318 International aid disbursed to total education, UNICEF to Georgia (USD million)
319 International aid disbursed to total education, Global Partnership for Education to Djibouti (USD million)
320 International aid disbursed to total education, AfDB to Guinea (USD million)
321 International aid disbursed to total education, ADPP (Humana People to People) to Guinea Bissau (USD million)
322 International aid disbursed to total education, GIZ to Kyrgyzstan (USD million)
323 International aid disbursed to total education, Belgium to Cambodia (USD million)
324 International aid disbursed to total education, AusAID to Laos (USD million)
325 International aid disbursed to total education, USAID to Liberia (USD million)
326 International aid disbursed to total education, World Bank to Moldova (USD million)
327 International aid to total education executed by ILO in Madagascar (USD million)
328 International aid disbursed to total education, DANIDA to Mozambique (USD million)
329 International aid disbursed to total education, IsDB to Mauritania (USD million)
330 International aid disbursed to total education, CIDA to Malawi (USD million)
331 International aid disbursed to total education, Belgium to Niger (USD million)
332 International aid disbursed to total education, Global Partnership for Education to Rwanda (USD million)
333 International aid disbursed to total education, AFD and French Embassy to Senegal (USD million)
334 International aid disbursed to total education, European Commission to Sierra Leone (USD million)
335 International aid disbursed to total education, Aga Khan to Tajikistan (USD million)
336 International aid disbursed to total education, CIDA to Vietnam (USD million)
337 International aid disbursed to total education, Ireland to Zambia (USD million)
338 International aid disbursed to total education, France to Afghanistan (USD million)
339 International aid disbursed to total education, CEIB to Albania (USD million)
340 International aid disbursed to total education, Switzerland to Burkina Faso (USD million)
341 International aid disbursed to total education, World Bank to Côte d'Ivoire (USD million)
342 International aid disbursed to total education, AFD and French Embassy to Cameroun (USD million)
343 International aid disbursed to total education, AFD to Djibouti (USD million)
344 International aid disbursed to total education, DFID to Ethiopia (USD million)
345 International aid disbursed to total education, USAID to Georgia (USD million)
346 International aid disbursed to total education, JICA to Djibouti (USD million)
347 International aid disbursed to total education, World Bank to Guinea (USD million)
348 International aid disbursed to total education, ADPP (other donors) to Guinea Bissau (USD million)
349 International aid disbursed to total education, UNICEF to Kyrgyzstan (USD million)
350 International aid disbursed to total education, Global Partnership for Education to Cambodia (USD million)
351 International aid disbursed to total education, European Commission to Laos (USD million)
352 International aid disbursed to total education, World Bank to Liberia (USD million)
353 International aid to total education executed by AFD and French Embassy in Madagascar (USD million)
354 International aid disbursed to total education, DFID to Mozambique (USD million)
355 International aid disbursed to total education, Spanish Cooperation to Mauritania (USD million)
356 International aid disbursed to total education, DFID to Malawi (USD million)
357 International aid disbursed to total education, French Embassy to Niger (USD million)
358 International aid disbursed to total education, UNICEF to Rwanda (USD million)
359 International aid disbursed to total education, Global Partnership for Education to Senegal (USD million)
360 International aid disbursed to total education, GIZ to Sierra Leone (USD million)
361 International aid disbursed to total education, Open Society Foundations to Tajikistan (USD million)
362 International aid disbursed to total education, DFID to Vietnam (USD million)
363 International aid disbursed to total education, ILO to Zambia (USD million)
364 International aid disbursed to total education, Germany to Afghanistan (USD million)
365 International aid disbursed to total education, Denmark to Burkina Faso (USD million)
366 International aid disbursed to total education, IsDB to Côte d'Ivoire (USD million)
367 International aid disbursed to total education, JICA to Cameroun (USD million)
368 International aid disbursed to total education, AfDB to Djibouti (USD million)
369 International aid disbursed to total education, DVV international to Ethiopia (USD million)
370 International aid disbursed to total education, World Bank to Georgia (USD million)
371 International aid disbursed to total education, UNICEF to Djibouti (USD million)
372 International aid disbursed to total education, Global Partnership for Education to Guinea (USD million)
373 International aid disbursed to total education, European Commission to Guinea Bissau (USD million)
374 International aid disbursed to total education, World Bank to Kyrgyzstan (USD million)
375 International aid disbursed to total education, European Commission to Cambodia (USD million)
376 International aid disbursed to total education, Germany (GIZ and KfW) to Laos (USD million)
377 International aid to total education executed by JICA in Madagascar (USD million)
378 International aid disbursed to total education, Finland to Mozambique (USD million)
379 International aid disbursed to total education, UNESCO to Mauritania (USD million)
380 International aid disbursed to total education, GIZ to Malawi (USD million)
381 International aid disbursed to total education, Japan to Niger (USD million)
382 International aid disbursed to total education, USAID to Rwanda (USD million)
383 International aid disbursed to total education, Italy to Senegal (USD million)
384 International aid disbursed to total education, JICA to Sierra Leone (USD million)
385 International aid disbursed to total education, European Commission to Tajikistan (USD million)
386 International aid disbursed to total education, JICA to Vietnam (USD million)
387 International aid disbursed to total education, Japan to Zambia (USD million)
388 International aid disbursed to total education, India to Afghanistan (USD million)
389 International aid disbursed to total education, JICA to Burkina Faso (USD million)
390 International aid disbursed to total education, FSD to Côte d'Ivoire (USD million)
391 International aid disbursed to total education, UNESCO to Cameroun (USD million)
392 International aid disbursed to total education, IsDB to Djibouti (USD million)
393 International aid disbursed to total education, European Commission to Ethiopia (USD million)
394 International aid disbursed to total education, USAID to Djibouti (USD million)
395 International aid disbursed to total education, GIZ to Guinea (USD million)
396 International aid disbursed to total education, AFD and French Embassy to Guinea Bissau (USD million)
397 International aid disbursed to total education, Japan to Cambodia (USD million)
398 International aid disbursed to total education, Global Partnership for Education to Laos (USD million)
399 International aid to total education executed by Norway in Madagascar (USD million)
400 International aid disbursed to total education, Flanders to Mozambique (USD million)
401 International aid disbursed to total education, UNICEF to Mauritania (USD million)
402 International aid disbursed to total education, Global Partnership for Education to Malawi (USD million)
403 International aid disbursed to total education, KfW to Niger (USD million)
404 International aid disbursed to total education, World Bank to Rwanda (USD million)
405 International aid disbursed to total education, UNICEF to Senegal (USD million)
406 International aid disbursed to total education, Sida to Sierra Leone (USD million)
407 International aid disbursed to total education, GIZ to Tajikistan (USD million)
408 International aid disbursed to total education, UNESCO to Vietnam (USD million)
409 International aid disbursed to total education, Netherlands to Zambia (USD million)
410 International aid disbursed to total education, Japan's MoFA to Afghanistan (USD million)
411 International aid disbursed to total education, Netherlands to Burkina Faso (USD million)
412 International aid disbursed to total education, KfW to Côte d'Ivoire (USD million)
413 International aid disbursed to total education, UNICEF to Cameroun (USD million)
414 International aid disbursed to total education, IMOA to Djibouti (USD million)
415 International aid disbursed to total education, Finland to Ethiopia (USD million)
416 International aid disbursed to total education, WFP to Djibouti (USD million)
417 International aid disbursed to total education, KfW to Guinea (USD million)
418 International aid disbursed to total education, Portuguese Cooperation to Guinea Bissau (USD million)
419 International aid disbursed to total education, Sweden to Cambodia (USD million)
420 International aid disbursed to total education, JICA to Laos (USD million)
421 International aid to total education executed by WFP in Madagascar (USD million)
422 International aid disbursed to total education, Germany (GIZ and KfW) to Mozambique (USD million)
423 International aid disbursed to total education, JICA to Malawi (USD million)
424 International aid disbursed to total education, WFP to Niger (USD million)
425 International aid disbursed to total education, USAID to Senegal (USD million)
426 International aid disbursed to total education, UNICEF to Sierra Leone (USD million)
427 International aid disbursed to total education, Global Partnership for Education (CF and EPDF) to Tajikistan (USD million)
428 International aid disbursed to total education, UNICEF to Vietnam (USD million)
429 International aid disbursed to total education, UNICEF to Zambia (USD million)
430 International aid disbursed to total education, JICA to Afghanistan (USD million)
431 International aid disbursed to total education, UNICEF to Burkina Faso (USD million)
432 International aid disbursed to total education, UNICEF to Côte d'Ivoire (USD million)
433 International aid disbursed to total education, GIZ/BMZ to Ethiopia (USD million)
434 International aid disbursed to total education, World Bank to Djibouti (USD million)
435 International aid disbursed to total education, UNICEF to Guinea (USD million)
436 International aid disbursed to total education, UNICEF (excluding Japan funds) to Guinea Bissau (USD million)
437 International aid disbursed to total education, UNESCO to Cambodia (USD million)
438 International aid disbursed to total education, UNESCO to Laos (USD million)
439 International aid to total education executed by UNESCO in Madagascar (USD million)
440 International aid disbursed to total education, GPE to Mozambique (USD million)
441 International aid disbursed to total education, KfW to Malawi (USD million)
442 International aid disbursed to total education, DFID to Niger (USD million)
443 International aid disbursed to total education, World Bank (including the Global Partnership for Education) to Sierra Leone (USD million)
444 International aid disbursed to total education, UNICEF to Tajikistan (USD million)
445 International aid disbursed to total education, USAID to Vietnam (USD million)
446 International aid disbursed to total education, USAID to Zambia (USD million)
447 International aid disbursed to total education, Netherlands to Afghanistan (USD million)
448 International aid disbursed to total education, European Commission to Burkina Faso (USD million)
449 International aid disbursed to total education, USAID to Côte d'Ivoire (USD million)
450 International aid disbursed to total education, GPE Catalytic Fund to Ethiopia (USD million)
451 International aid disbursed to total education, Japan (via UNICEF) to Guinea Bissau (USD million)
452 International aid disbursed to total education, UNICEF to Cambodia (USD million)
453 International aid disbursed to total education, UNICEF to Laos (USD million)
454 International aid to total education executed by UNICEF (excluding GPE funds) in Madagascar (USD million)
455 International aid disbursed to total education, Ireland to Mozambique (USD million)
456 International aid disbursed to total education, UNICEF to Malawi (USD million)
457 International aid disbursed to total education, Switzerland to Niger (USD million)
458 International aid disbursed to total education from WFP to Sierra Leone (USD million)
459 International aid disbursed to total education, USAID to Tajikistan (USD million)
460 International aid disbursed to total education, World Bank to Vietnam (USD million)
461 International aid disbursed to total education, New Zealand to Afghanistan (USD million)
462 International aid disbursed to total education, Italian Cooperation to Ethiopia (USD million)
463 International aid disbursed to total education, USAID to Cambodia (USD million)
464 International aid disbursed to total education, WFP to Laos (USD million)
465 International aid disbursed to total education from the Global Partnership for Education (via UNICEF and WB) to Madagascar (USD million)
466 International aid disbursed to total education, Italy to Mozambique (USD million)
467 International aid disbursed to total education, USAID to Malawi (USD million)
468 International aid disbursed to total education, Luxembourg to Niger (USD million)
469 International aid disbursed to total education, WFP to Tajikistan (USD million)
472 International aid for total education, disbursed (up to present year) and scheduled (next years), aggregation of reporting donors (USD million)
473 International aid disbursed to basic education, CIDA to Afghanistan (USD million)
474 International aid disbursed to basic education, World Bank to Albania (USD million)
475 International aid disbursed to basic education, CIDA to Burkina Faso (USD million)
476 International aid disbursed to total education, Global Partnership for Education to Central African Republic (USD million)
477 International aid disbursed to basic education, AfDB to Côte d'Ivoire (USD million)
478 International aid disbursed to basic education, AfDB to Cameroun (USD million)
479 International aid disbursed to basic education, World Bank (IDA) to Djibouti (USD million)
480 International aid disbursed to basic education, ADB to Ethiopia (USD million)
481 International aid disbursed to basic education, European Commission to Georgia (USD million)
482 International aid disbursed to basic education, DFID to Djibouti (USD million)
483 International aid disbursed to basic education, AFD to Guinea (USD million)
484 International aid disbursed to basic education, ADPP (European Union) to Guinea Bissau (USD million)
485 International aid disbursed to basic education, European Commission to Kyrgyzstan (USD million)
486 International aid disbursed to basic education, AfDB to Cambodia (USD million)
487 International aid disbursed to basic education, ADB to Laos (USD million)
488 International aid disbursed to basic education, UNICEF to Liberia (USD million)
489 International aid disbursed to basic education, UNICEF to Moldova (USD million)
490 International aid to basic education executed by World Bank (GPE funds) in Madagascar (USD million)
491 International aid disbursed to total education, WFP to Mauritania (USD million)
492 International aid disbursed to basic education, AfDB to Malawi (USD million)
493 International aid disbursed to basic education, AFD to Niger (USD million)
494 International aid disbursed to basic education, DFID to Rwanda (USD million)
495 International aid disbursed to basic education, CIDA to Senegal (USD million)
496 International aid disbursed to basic education, DFID to Sierra Leone (USD million)
497 International aid disbursed to basic education, Aga Khan to Tajikistan (USD million)
498 International aid disbursed to total education, AusAID and ChildFund Australia to Tajikistan (USD million)
499 International aid disbursed to basic education, CIDA to Vietnam (USD million)
500 International aid disbursed to basic education, Denmark to Zambia (USD million)
501 International aid disbursed to basic education, Sida to Afghanistan (USD million)
502 International aid disbursed to basic education, Japan Government to Ethiopia (USD million)
503 International aid disbursed to basic education, WFP to Cambodia (USD million)
504 International aid disbursed to basic education, World Bank to Laos (USD million)
505 International aid to basic education executed by the European Commission in Madagascar (USD million)
506 International aid disbursed to basic education, WFP to Malawi (USD million)
507 International aid disbursed to basic education, UNICEF to Niger (USD million)
508 International aid disbursed to total education, private donors to Timor-Leste (USD million)
509 International aid disbursed to basic education, UNESCO to Afghanistan (USD million)
510 International aid disbursed to basic education, JICA to Ethiopia (USD million)
511 International aid disbursed to basic education, World Bank to Cambodia (USD million)
512 International aid disbursed to basic education, International NGOs to Laos (USD million)
513 International aid disbursed to basic education, World Bank to Malawi (USD million)
514 International aid disbursed to total education, UNICEF to Timor-Leste (USD million)
515 International aid disbursed to basic education, USAID to Afghanistan (USD million)
516 International aid disbursed to basic education, KfW to Ethiopia (USD million)
517 International aid disbursed to total education, USAID to Timor-Leste (USD million)
518 International aid disbursed to basic education, World Bank to Afghanistan (USD million)
519 International aid disbursed to basic education, Netherlands to Ethiopia (USD million)
520 International aid disbursed to basic education, Sida to Ethiopia (USD million)
521 International aid disbursed to basic education, UNICEF to Ethiopia (USD million)
522 International aid disbursed to basic education, USAID to Ethiopia (USD million)
523 International aid disbursed to basic education, WFP to Ethiopia (USD million)
524 International aid disbursed to basic education, World Bank to Ethiopia (USD million)
525 International aid disbursed to basic education, DANIDA to Afghanistan (USD million)
526 International aid disbursed to basic education, BEI to Albania (USD million)
527 International aid disbursed to basic education, AFD to Burkina Faso (USD million)
528 International aid disbursed to basic education, BADEA to Côte d'Ivoire (USD million)
529 International aid disbursed to basic education, World Bank to Cameroun (USD million)
530 International aid disbursed to basic education, FSD to Djibouti (USD million)
531 International aid disbursed to basic education, Belgium (VLIR USO) to Ethiopia (USD million)
532 International aid disbursed to basic education, UNICEF to Georgia (USD million)
533 International aid disbursed to basic education, Global Partnership for Education to Djibouti (USD million)
534 International aid disbursed to basic education, AfDB to Guinea (USD million)
535 International aid disbursed to basic education, ADPP (Humana People to People) to Guinea Bissau (USD million)
536 International aid disbursed to basic education, GIZ to Kyrgyzstan (USD million)
537 International aid disbursed to basic education, Belgium to Cambodia (USD million)
538 International aid disbursed to basic education, AusAID to Laos (USD million)
539 International aid disbursed to basic education, USAID to Liberia (USD million)
540 International aid disbursed to basic education, World Bank to Moldova (USD million)
541 International aid to basic education executed by ILO in Madagascar (USD million)
542 International aid disbursed to basic education, AFD to Mauritania (USD million)
543 International aid disbursed to basic education, CIDA to Malawi (USD million)
544 International aid disbursed to basic education, Belgium to Niger (USD million)
545 International aid disbursed to basic education, Global Partnership for Education to Rwanda (USD million)
546 International aid disbursed to basic education, AFD and French Embassy to Senegal (USD million)
547 International aid disbursed to basic education, European Commission to Sierra Leone (USD million)
548 International aid disbursed to basic education, Open Society Foundations to Tajikistan (USD million)
549 International aid disbursed to total education, AusAID (World Bank) to Timor-Leste (USD million)
550 International aid disbursed to basic education, DFID to Vietnam (USD million)
551 International aid disbursed to basic education, Ireland to Zambia (USD million)
552 International aid disbursed to basic education, France to Afghanistan (USD million)
553 International aid disbursed to basic education, CEIB to Albania (USD million)
554 International aid disbursed to basic education, Switzerland to Burkina Faso (USD million)
555 International aid disbursed to basic education, World Bank to Côte d'Ivoire (USD million)
556 International aid disbursed to basic education, AFD and French Embassy to Cameroun (USD million)
557 International aid disbursed to basic education, AFD to Djibouti (USD million)
558 International aid disbursed to basic education, DFID to Ethiopia (USD million)
559 International aid disbursed to basic education, USAID to Georgia (USD million)
560 International aid disbursed to basic education, JICA to Djibouti (USD million)
561 International aid disbursed to basic education, World Bank to Guinea (USD million)
562 International aid disbursed to basic education, ADPP (other donors) to Guinea Bissau (USD million)
563 International aid disbursed to basic education, UNICEF to Kyrgyzstan (USD million)
564 International aid disbursed to basic education, Global Partnership for Education to Cambodia (USD million)
565 International aid disbursed to basic education, European Commission to Laos (USD million)
566 International aid disbursed to basic education, World Bank to Liberia (USD million)
567 International aid to basic education executed by AFD and French Embassy in Madagascar (USD million)
568 International aid disbursed to basic education, IsDB to Mauritania (USD million)
569 International aid disbursed to basic education, DFID to Malawi (USD million)
570 International aid disbursed to basic education, French Embassy to Niger (USD million)
571 International aid disbursed to basic education, UNICEF to Rwanda (USD million)
572 International aid disbursed to basic education, Global Partnership for Education to Senegal (USD million)
573 International aid disbursed to basic education, GIZ to Sierra Leone (USD million)
574 International aid disbursed to basic education, European Commission to Tajikistan (USD million)
575 International aid disbursed to total education, Australia to Timor-Leste (USD million)
576 International aid disbursed to basic education, JICA to Vietnam (USD million)
577 International aid disbursed to basic education, ILO to Zambia (USD million)
578 International aid disbursed to basic education, Germany to Afghanistan (USD million)
579 International aid disbursed to basic education, Denmark to Burkina Faso (USD million)
580 International aid disbursed to basic education, IsDB to Côte d'Ivoire (USD million)
581 International aid disbursed to basic education, JICA to Cameroun (USD million)
582 International aid disbursed to basic education, AfDB to Djibouti (USD million)
583 International aid disbursed to basic education, DVV international to Ethiopia (USD million)
584 International aid disbursed to basic education, World Bank to Georgia (USD million)
585 International aid disbursed to basic education, UNICEF to Djibouti (USD million)
586 International aid disbursed to basic education, Global Partnership for Education to Guinea (USD million)
587 International aid disbursed to basic education, European Commission to Guinea Bissau (USD million)
588 International aid disbursed to basic education, World Bank to Kyrgyzstan (USD million)
589 International aid disbursed to basic education, European Commission to Cambodia (USD million)
590 International aid disbursed to basic education, Germany (GIZ and KfW) to Laos (USD million)
591 International aid to basic education executed by JICA in Madagascar (USD million)
592 International aid disbursed to basic education, Spanish Cooperation to Mauritania (USD million)
593 International aid disbursed to basic education, GIZ to Malawi (USD million)
594 International aid disbursed to basic education, Japan to Niger (USD million)
595 International aid disbursed to basic education, USAID to Rwanda (USD million)
596 International aid disbursed to basic education, Italy to Senegal (USD million)
597 International aid disbursed to basic education, JICA to Sierra Leone (USD million)
598 International aid disbursed to basic education, GIZ to Tajikistan (USD million)
599 International aid disbursed to total education, World Bank (IDA) to Timor-Leste (USD million)
600 International aid disbursed to basic education, UNESCO to Vietnam (USD million)
601 International aid disbursed to basic education, Japan to Zambia (USD million)
602 International aid disbursed to basic education, India to Afghanistan (USD million)
603 International aid disbursed to basic education, JICA to Burkina Faso (USD million)
604 International aid disbursed to basic education, FSD to Côte d'Ivoire (USD million)
605 International aid disbursed to basic education, UNESCO to Cameroun (USD million)
606 International aid disbursed to basic education, IsDB to Djibouti (USD million)
607 International aid disbursed to basic education, European Commission to Ethiopia (USD million)
608 International aid disbursed to basic education, USAID to Djibouti (USD million)
609 International aid disbursed to basic education, GIZ to Guinea (USD million)
610 International aid disbursed to basic education, AFD and French Embassy to Guinea Bissau (USD million)
611 International aid disbursed to basic education, Japan to Cambodia (USD million)
612 International aid disbursed to basic education, Global Partnership for Education to Laos (USD million)
613 International aid to basic education executed by Norway in Madagascar (USD million)
614 International aid disbursed to basic education, UNESCO to Mauritania (USD million)
615 International aid disbursed to basic education, Global Partnership for Education to Malawi (USD million)
616 International aid disbursed to basic education, KfW to Niger (USD million)
617 International aid disbursed to basic education, World Bank to Rwanda (USD million)
618 International aid disbursed to basic education, UNICEF to Senegal (USD million)
619 International aid disbursed to basic education, Sida to Sierra Leone (USD million)
620 International aid disbursed to basic education, Global Partnership for Education (CF and EPDF) to Tajikistan (USD million)
621 International aid disbursed to total education, Japan to Timor-Leste (USD million)
622 International aid disbursed to basic education, UNICEF to Vietnam (USD million)
623 International aid disbursed to basic education, Netherlands to Zambia (USD million)
624 International aid disbursed to basic education, Japan's MoFA to Afghanistan (USD million)
625 International aid disbursed to basic education, Netherlands to Burkina Faso (USD million)
626 International aid disbursed to basic education, KfW to Côte d'Ivoire (USD million)
627 International aid disbursed to basic education, UNICEF to Cameroun (USD million)
628 International aid disbursed to basic education, IMOA to Djibouti (USD million)
629 International aid disbursed to basic education, Finland to Ethiopia (USD million)
630 International aid disbursed to basic education, WFP to Djibouti (USD million)
631 International aid disbursed to basic education, KfW to Guinea (USD million)
632 International aid disbursed to basic education, Portuguese Cooperation to Guinea Bissau (USD million)
633 International aid disbursed to basic education, Sweden to Cambodia (USD million)
634 International aid disbursed to basic education, JICA to Laos (USD million)
635 International aid to basic education executed by WFP in Madagascar (USD million)
636 International aid disbursed to basic education, UNICEF to Mauritania (USD million)
637 International aid disbursed to basic education, JICA to Malawi (USD million)
638 International aid disbursed to basic education, WFP to Niger (USD million)
639 International aid disbursed to basic education, USAID to Senegal (USD million)
640 International aid disbursed to basic education, UNICEF to Sierra Leone (USD million)
641 International aid disbursed to basic education, UNICEF to Tajikistan (USD million)
642 International aid disbursed to total education, South Korea to Timor-Leste (USD million)
643 International aid disbursed to basic education, USAID to Vietnam (USD million)
644 International aid disbursed to basic education, UNICEF to Zambia (USD million)
645 International aid disbursed to basic education, JICA to Afghanistan (USD million)
646 International aid disbursed to basic education, UNICEF to Burkina Faso (USD million)
647 International aid disbursed to basic education, UNICEF to Côte d'Ivoire (USD million)
648 International aid disbursed to basic education, GIZ/BMZ to Ethiopia (USD million)
649 International aid disbursed to basic education, World Bank to Djibouti (USD million)
650 International aid disbursed to basic education, UNICEF to Guinea (USD million)
651 International aid disbursed to basic education, UNICEF (excluding Japan funds) to Guinea Bissau (USD million)
652 International aid disbursed to basic education, UNESCO to Cambodia (USD million)
653 International aid disbursed to basic education, UNESCO to Laos (USD million)
654 International aid to basic education executed by UNESCO in Madagascar (USD million)
655 International aid disbursed to basic education, WFP to Mauritania (USD million)
656 International aid disbursed to basic education, KfW to Malawi (USD million)
657 International aid disbursed to basic education, DFID to Niger (USD million)
658 International aid disbursed to basic education, World Bank (including the Global Partnership for Education) to Sierra Leone (USD million)
659 International aid disbursed to basic education, USAID to Tajikistan (USD million)
660 International aid disbursed to total education, New Zealand to Timor-Leste (USD million)
661 International aid disbursed to basic education, World Bank to Vietnam (USD million)
662 International aid disbursed to basic education, USAID to Zambia (USD million)
663 International aid disbursed to basic education, Netherlands to Afghanistan (USD million)
664 International aid disbursed to basic education, European Commission to Burkina Faso (USD million)
665 International aid disbursed to basic education, USAID to Côte d'Ivoire (USD million)
666 International aid disbursed to basic education, GPE Catalytic Fund to Ethiopia (USD million)
667 International aid disbursed to basic education, Japan (via UNICEF) to Guinea Bissau (USD million)
668 International aid disbursed to basic education, UNICEF to Cambodia (USD million)
669 International aid disbursed to basic education, UNICEF to Laos (USD million)
670 International aid to basic education executed by UNICEF in Madagascar (USD million)
671 International aid disbursed to basic education, UNICEF to Malawi (USD million)
672 International aid disbursed to basic education, Switzerland to Niger (USD million)
673 International aid disbursed to basic education, WFP to Sierra Leone (USD million)
674 International aid disbursed to basic education, WFP to Tajikistan (USD million)
675 International aid disbursed to total education, ChildFund NZAID and UNICEF to Timor-Leste (USD million)
676 International aid disbursed to basic education, New Zealand to Afghanistan (USD million)
677 International aid disbursed to basic education, Italian Cooperation to Ethiopia (USD million)
678 International aid disbursed to basic education, AUSAID to Cambodia (USD million)
679 International aid disbursed to basic education, WFP to Laos (USD million)
680 International aid disbursed to basic education, Global Partnership for Education to Madagascar (USD million)
681 International aid disbursed to basic education, USAID to Malawi (USD million)
682 International aid disbursed to basic education, Luxembourg to Niger (USD million)
683 International aid disbursed to basic education, World Bank to Tajikistan (USD million)
684 International aid disbursed to total education, Portugal to Timor-Leste (USD million)
687 International aid for basic education, disbursed (up to present year) and scheduled (next years), aggregation of reporting donors (USD million)
694 Coordinating agency of Local Education Group (1=text in notes)
696 Donor members in Local Education Group (1=text in notes)
697 Civil society organizations in Local Education Group (1=text in notes)
698 Date of last Joint Education Sector Review (year=full date in notes)
699 Date of next Joint Education Sector Review (year=full date in notes)
700 Starting year of current Education Sector Plan period (year=full period in notes)
701 Ending year of current Education Sector Plan period (year=full period in notes)
706 Endorsement of Education Sector Plan (year)
785 Baccalaureate in Central African Republic, exam at the end of secondary education, success rate (%)
947 Alignment of aid to education (% of total international aid to education)
966 Coordinated technical cooperation (% of total cooperation to education)
967 Use of public financial management country systems (% of total international aid to education)
968 Use of procurement country systems (% of total international aid to education)
969 Number of parallel implementation units, education sector
970 Aid provided through program based approaches (% of international aid to education)
976 9120000:ACTUAL EDUCATION
1029 Account, primary education or less (% ages 15+)
1030 Account, secondary education or more (% ages 15+)
1449 Adults aged 15 to 29 years with at least primary education (% of adults aged 15 to 29 years with severe disability)
1450 Adults aged 30 to 44 years with at least primary education (% of adults aged 30 to 44 years with severe disability)
1451 Adults aged 45 to 64 years with at least primary education (% of adults aged 45 to 64 years with severe disability)
1452 Adults aged 65 or older with at least primary education (% of adults aged 65 or older with severe disability)
1453 Adults with at least primary education (% of adults with severe disability)
1454 Female adults with at least primary education (% of female adults with severe disability)
1455 Male adults with at least primary education (% of male adults with severe disability)
1456 Adults living in rural areas with at least primary education (% of adults living in rural areas with severe disability)
1457 Adults living in urban areas with at least primary education (% of adults living in urban areas with severe disability)
1458 Adults aged 15 to 29 years with at least primary education (% of adults aged 15 to 29 years with any disability)
1459 Adults aged 30 to 44 years with at least primary education (% of adults aged 30 to 44 years with any disability)
1460 Adults aged 45 to 64 years with at least primary education (% of adults aged 45 to 64 years with any disability)
1461 Adults aged 65 or older with at least primary education (% of adults aged 65 or older with any disability)
1462 Adults with at least primary education (% of adults with any disability)
1463 Adults with at least primary education (% of adults with cognition disability)
1464 Adults with at least primary education (% of adults with communicating disability)
1465 Female adults with at least primary education (% of female adults with any disability)
1466 Adults with at least primary education (% of adults with hearing disability)
1467 Male adults with at least primary education (% of male adults with any disability)
1468 Adults with at least primary education (% of adults with mobile disability)
1469 Adults living in rural areas with at least primary education (% of adults living in rural areas with any disability)
1470 Adults with at least primary education (% of adults with seeing disability)
1471 Adults with at least primary education (% of adults with selfcare disability)
1472 Adults living in urban areas with at least primary education (% of adults living in urban areas with any disability)
1473 Adults aged 15 to 29 years with at least primary education (% of adults aged 15 to 29 years with no disability)
1474 Adults aged 30 to 44 years with at least primary education (% of adults aged 30 to 44 years with no disability)
1475 Adults aged 45 to 64 years with at least primary education (% of adults aged 45 to 64 years with no disability)
1476 Adults aged 65 or older with at least primary education (% of adults aged 65 or older with no disability)
1477 Adults with at least primary education (% of adults with no disability)
1478 Female adults with at least primary education (% of female adults with no disability)
1479 Male adults with at least primary education (% of male adults with no disability)
1480 Adults living in rural areas with at least primary education (% of adults living in rural areas with no disability)
1481 Adults living in urban areas with at least primary education (% of adults living in urban areas with no disability)
1482 Adults aged 15 to 29 years with at least primary education (% of adults aged 15 to 29 years with none or moderate disability)
1483 Adults aged 30 to 44 years with at least primary education (% of adults aged 30 to 44 years with none or moderate disability)
1484 Adults aged 45 to 64 years with at least primary education (% of adults aged 45 to 64 years with none or moderate disability)
1485 Adults aged 65 or older with at least primary education (% of adults aged 65 or older with none or moderate disability)
1486 Adults with at least primary education (% of adults with none or moderate disability)
1487 Female adults with at least primary education (% of female adults with none or moderate disability)
1488 Male adults with at least primary education (% of male adults with none or moderate disability)
1489 Adults living in rural areas with at least primary education (% of adults living in rural areas with none or moderate disability)
1490 Adults living in urban areas with at least primary education (% of adults living in urban areas with none or moderate disability)
1491 Adults aged 15 to 29 years with at least primary education (% of adults aged 15 to 29 years with moderate disability)
1492 Adults aged 30 to 44 years with at least primary education (% of adults aged 30 to 44 years with moderate disability)
1493 Adults aged 45 to 64 years with at least primary education (% of adults aged 45 to 64 years with moderate disability)
1494 Adults aged 65 or older with at least primary education (% of adults aged 65 or older with moderate disability)
1495 Adults with at least primary education (% of adults with moderate disability)
1496 Female adults with at least primary education (% of female adults with moderate disability)
1497 Male adults with at least primary education (% of male adults with moderate disability)
1498 Adults living in rural areas with at least primary education (% of adults living in rural areas with moderate disability)
1499 Adults living in urban areas with at least primary education (% of adults living in urban areas with moderate disability)
1500 Adults aged 15 to 29 years with at least secondary education (% of adults aged 15 to 29 years with severe disability)
1501 Adults aged 30 to 44 years with at least secondary education (% of adults aged 30 to 44 years with severe disability)
1502 Adults aged 45 to 64 years with at least secondary education (% of adults aged 45 to 64 years with severe disability)
1503 Adults aged 65 or older with at least secondary education (% of adults aged 65 or older with severe disability)
1504 Adults with at least secondary education (% of adults with severe disability)
1505 Female adults with at least secondary education (% of female adults with severe disability)
1506 Male adults with at least secondary education (% of male adults with severe disability)
1507 Adults living in rural areas with at least secondary education (% of adults living in rural areas with severe disability)
1508 Adults living in urban areas with at least secondary education (% of adults living in urban areas with severe disability)
1509 Adults aged 15 to 29 years with at least secondary education (% of adults aged 15 to 29 years with any disability)
1510 Adults aged 30 to 44 years with at least secondary education (% of adults aged 30 to 44 years with any disability)
1511 Adults aged 45 to 64 years with at least secondary education (% of adults aged 45 to 64 years with any disability)
1512 Adults aged 65 or older with at least secondary education (% of adults aged 65 or older with any disability)
1513 Adults with at least secondary education (% of adults with any disability)
1514 Adults with at least secondary education (% of adults with cognition disability)
1515 Adults with at least secondary education (% of adults with communicating disability)
1516 Female adults with at least secondary education (% of female adults with any disability)
1517 Adults with at least secondary education (% of adults with hearing disability)
1518 Male adults with at least secondary education (% of male adults with any disability)
1519 Adults with at least secondary education (% of adults with mobile disability)
1520 Adults living in rural areas with at least secondary education (% of adults living in rural areas with any disability)
1521 Adults with at least secondary education (% of adults with seeing disability)
1522 Adults with at least secondary education (% of adults with selfcare disability)
1523 Adults living in urban areas with at least secondary education (% of adults living in urban areas with any disability)
1524 Adults aged 15 to 29 years with at least secondary education (% of adults aged 15 to 29 years with no disability)
1525 Adults aged 30 to 44 years with at least secondary education (% of adults aged 30 to 44 years with no disability)
1526 Adults aged 45 to 64 years with at least secondary education (% of adults aged 45 to 64 years with no disability)
1527 Adults aged 65 or older with at least secondary education (% of adults aged 65 or older with no disability)
1528 Adults with at least secondary education (% of adults with no disability)
1529 Female adults with at least secondary education (% of female adults with no disability)
1530 Male adults with at least secondary education (% of male adults with no disability)
1531 Adults living in rural areas with at least secondary education (% of adults living in rural areas with no disability)
1532 Adults living in urban areas with at least secondary education (% of adults living in urban areas with no disability)
1533 Adults aged 15 to 29 years with at least secondary education (% of adults aged 15 to 29 years with none or moderate disability)
1534 Adults aged 30 to 44 years with at least secondary education (% of adults aged 30 to 44 years with none or moderate disability)
1535 Adults aged 45 to 64 years with at least secondary education (% of adults aged 45 to 64 years with none or moderate disability)
1536 Adults aged 65 or older with at least secondary education (% of adults aged 65 or older with none or moderate disability)
1537 Adults with at least secondary education (% of adults with none or moderate disability)
1538 Female adults with at least secondary education (% of female adults with none or moderate disability)
1539 Male adults with at least secondary education (% of male adults with none or moderate disability)
1540 Adults living in rural areas with at least secondary education (% of adults living in rural areas with none or moderate disability)
1541 Adults living in urban areas with at least secondary education (% of adults living in urban areas with none or moderate disability)
1542 Adults aged 15 to 29 years with at least secondary education (% of adults aged 15 to 29 years with moderate disability)
1543 Adults aged 30 to 44 years with at least secondary education (% of adults aged 30 to 44 years with moderate disability)
1544 Adults aged 45 to 64 years with at least secondary education (% of adults aged 45 to 64 years with moderate disability)
1545 Adults aged 65 or older with at least secondary education (% of adults aged 65 or older with moderate disability)
1546 Adults with at least secondary education (% of adults with moderate disability)
1547 Female adults with at least secondary education (% of female adults with moderate disability)
1548 Male adults with at least secondary education (% of male adults with moderate disability)
1549 Adults living in rural areas with at least secondary education (% of adults living in rural areas with moderate disability)
1550 Adults living in urban areas with at least secondary education (% of adults living in urban areas with moderate disability)
1670 Barro-Lee: Percentage of female population age 15-19 with no education
1671 Barro-Lee: Percentage of population age 15-19 with no education
1672 Barro-Lee: Percentage of female population age 15+ with no education
1673 Barro-Lee: Percentage of population age 15+ with no education
1674 Barro-Lee: Percentage of female population age 20-24 with no education
1675 Barro-Lee: Percentage of population age 20-24 with no education
1676 Barro-Lee: Percentage of female population age 25-29 with no education
1677 Barro-Lee: Percentage of population age 25-29 with no education
1678 Barro-Lee: Percentage of female population age 25+ with no education
1679 Barro-Lee: Percentage of population age 25+ with no education
1680 Barro-Lee: Percentage of female population age 30-34 with no education
1681 Barro-Lee: Percentage of population age 30-34 with no education
1682 Barro-Lee: Percentage of female population age 35-39 with no education
1683 Barro-Lee: Percentage of population age 35-39 with no education
1684 Barro-Lee: Percentage of female population age 40-44 with no education
1685 Barro-Lee: Percentage of population age 40-44 with no education
1686 Barro-Lee: Percentage of female population age 45-49 with no education
1687 Barro-Lee: Percentage of population age 45-49 with no education
1688 Barro-Lee: Percentage of female population age 50-54 with no education
1689 Barro-Lee: Percentage of population age 50-54 with no education
1690 Barro-Lee: Percentage of female population age 55-59 with no education
1691 Barro-Lee: Percentage of population age 55-59 with no education
1692 Barro-Lee: Percentage of female population age 60-64 with no education
1693 Barro-Lee: Percentage of population age 60-64 with no education
1694 Barro-Lee: Percentage of female population age 65-69 with no education
1695 Barro-Lee: Percentage of population age 65-69 with no education
1696 Barro-Lee: Percentage of female population age 70-74 with no education
1697 Barro-Lee: Percentage of population age 70-74 with no education
1698 Barro-Lee: Percentage of female population age 75+ with no education
1699 Barro-Lee: Percentage of population age 75+ with no education
2102 Education workers, as a share of public formal employees
2105 Public sector employment, as a share of formal employment, by industry: Education
2115 Public sector female employment, as a share of paid employment by industry: Education
2124 Education workers, as a share of public paid employees
2128 Public sector employment, as a share of paid employment, by industry: Education
2146 Education workers, as a share of public total employees
2151 Number of employed employees, by industry: Education
2159 Public sector employment, as a share of total employment, by industry: Education
2172 Proportion of total employees with tertiary education working in public sector
2180 Median age of public paid employees, by industry: Education
2181 Mean age of public paid employees, by industry: Education
2196 Median age of private paid employees, by industry: Education
2197 Mean age of private paid employees, by industry: Education
2234 Individuals with no education as a share of private paid employees
2236 Individuals with primary education as a share of private paid employees
2238 Individuals with secondary education as a share of private paid employees
2242 Individuals with tertiary education as a share of private paid employees, by industry: Education
2243 Individuals with tertiary education as a share of private paid employees, by industry: Health
2244 Individuals with tertiary Education as a share of private paid employees, by occupation: Medical workers
2245 Individuals with tertiary Education as a share of private paid employees, by occupation: Teachers
2246 Individuals with tertiary education as a share of private paid employees
2251 Females, as a share of public paid employees by industry: Education
2262 Individuals with no education as a share of public paid employees
2265 Number of public paid employees, by industry: Education
2274 Individuals with primary education as a share of public paid employees
2277 Rural residents, as a share of public paid employees, by industry: Education
2285 Individuals with secondary education as a share of public paid employees
2291 Individuals with tertiary Education as a share of public paid employees, by industry: Core Public Administration
2292 Individuals with tertiary education as a share of public paid employees, by industry: Education
2293 Individuals with tertiary education as a share of public paid employees, by industry: Health
2294 Individuals with tertiary Education as a share of public paid employees, by occupation: Medical workers
2295 Individuals with tertiary education as a share of public paid employees, by industry: Public Administration
2296 Individuals with tertiary Education as a share of public paid employees, by industry: Public Safety
2297 Individuals with tertiary Education as a share of public paid employees, by industry: Social Security
2298 Individuals with tertiary Education as a share of public paid employees, by occupation: Teachers
2299 Individuals with tertiary education as a share of public paid employees
2303 Number of paid employees, by industry: Education
2321 Public sector wage premium, by industry: Education (compared to formal wage employees)
2322 Public sector wage premium, by industry: Education (compared to all private employees)
2323 Public sector wage premium for females, by industry: Education (compared to paid wage employees)
2329 Public sector wage premium for males, by industry: Education (compared to paid wage employees)
2337 P-Value: Public sector wage premium, by education level (compared to formal wage employees)
2338 P-Value: Public sector wage premium, by industry: Education (compared to all private employees)
2339 P-Value: Public sector wage premium, by industry: Education (compared to formal wage employees)
2342 P-Value: Public sector wage premium for females, by industry: Education (compared to paid wage employees)
2349 P-Value: Gender wage premium in the public sector, by industry: Education (compared to male paid employees)
2358 P-Value: Public sector wage premium for males, by industry: Education (compared to paid wage employees)
2364 Public sector wage premium, by education level: No education (compared to formal wage employees)
2367 Public sector wage premium, by education level: Primary education (compared to formal wage employees)
2369 Public sector wage premium, by education level: Secondary education (compared to formal wage employees)
2374 Public sector wage premium, by education level: Tertiary education (compared to formal wage employees)
2375 P-Value: Gender wage premium in the private sector, by industry: Education (compared to male paid employees)
2381 Gender wage premium in the private sector, by industry: Education (compared to male paid employees)
2391 Gender wage premium in the public sector, by industry: Education (compared to male paid employees)
2507 Borrowed any money, primary education or less (% ages 15+)
2508 Borrowed any money, secondary education or more (% ages 15+)
3031 Percentage of population with No Education
3032 Percentage of population with Primary Education
3033 Percentage of population with Secondary Education
3034 Percentage of population with Post Secondary Education
3035 Mean number of years of education completed, aged 17 and older
3292 Total Specific Allocation Grant for Education (in IDR Billion)
6241 Gross ODA aid disbursement for basic education, DAC donors total (current US$)
6242 Gross ODA aid disbursement for education, DAC donors total (current US$)
6243 Gross ODA aid disbursement for post-secondary education, DAC donors total (current US$)
6244 Gross ODA aid disbursement for secondary education, DAC donors total (current US$)
6245 Gross ODA aid disbursement for education (level unspecified), DAC donors total (current US$)
7228 656_Does a dedicated, national, multi-stakeholder structure exist to promote and coordinate provision of financial education?_#VHQB_00
7229 660_Does government, industry, and NGOs participate in the multi-stakeholder structure to promote and coordinate financial education?
7230 659_Does government and industry only participate in the multi-stakeholder structure to promote and coordinate financial education?
7231 657_Does government participate in the multi-stakeholder structure to promote and coordinate financial education?
7232 658_Does industry only participate in the multi-stakeholder structure to promote and coordinate financial education?
7233 661_Does government, industry, or NGOs participate in the multi-stakeholder structure to promote and coordinate financial education?_#VHQC_00
7234 655_Are there multiple agencies responsible for financial education policy and programs?_#VHQA_01
7235 654_Is there a single agency responsible for financial education policy and programs?_#VHQA_00
7236 653_Does this country have a formal definition for financial education or financial literacy or financial capability? _#VHPA_00
7237 685_Does the government itself or with partners maintain a website with educational content, tools, and resources for broader financial education?_#VHXA_01
7238 674_Is financial education integrated into any government-provided social assistance programs? _#VHVA_00
7239 669_Has the government issued written guidelines directed to all providers of financial education on content and/or methodology?
7240 668_Has the government issued written guidelines directed to a limited set of providers of financial education on content and/or methodology?
7241 670_Has the government issued written guidelines directed to all, some, or none of the providers of financial education on content and/or methodology?_#VHUA_00
7242 662_Has the government, either by itself or with partners, undertaken a national mapping of financial education activities in the past five years? _#VHRA_00
7245 678_Is financial education at the stage of curriculum development planned within the next 1-2 years within public school curriculums?_#VHWA_03
7246 675_Is financial education included in public school curriculums as a distinct topic or subject?_#VHWA_00
7247 677_Is financial education at the stage of implementation planned within the next 1-2 years within public school curriculums?_#VHWA_02
7248 681_Is financial education currently or soon to be included as a topic in public school curriculums at junior secondary level?_#VHWB_01
7249 679_Is financial education at the stage of not being included in public school curriculums?_#VHWA_04
7250 680_Is financial education currently or soon to be included as a topic in public school curriculums at primary level?_#VHWB_00
7251 682_Is financial education currently or soon to be included as a topic in public school curriculums at senior secondary level?_#VHWB_02
7252 676_Is financial education included in public school curriculums as a subtopic integrated into one or multiple other topics or subjects?_#VHWA_01
7253 683_Is financial education currently or soon to be included as a topic in public school curriculums at university level?_#VHWB_03
7255 672_Does the government explicitly require all financial service providers to offer financial education?
7256 671_Does the government explicitly require a limited set of financial service providers to offer financial education?
7257 673_Does the government explicitly require all, some, or none of the financial service providers to offer financial education?_#VHUB_00
7258 664_Does the government regularly collect data from all known providers of financial education on the reach of their programs?
7259 663_Does the government regularly collect data from a limited set of providers of financial education on the reach of their programs?
7260 665_Does the government regularly collect data from all, some, or none of the known providers of financial education on the reach of their programs?_#VHSA_00
7379 330_Is financial education one of the main activities of the financial consumer protection (FCP) unit?_#VGVA_10
7582 018_Is a national financial capability/literacy/education strategy (NFCS/NFLS/NFES) under development?_#VGAG_01
7583 017_Has a national financial capability/literacy/education strategy (NFCS/NFLS/NFES) already been launched?_#VGAG_00
7608 Education function expenditure (in IDR)
7690 Financial institution account, primary education or less (% ages 15+)
7691 Financial institution account, secondary education or more (% ages 15+)
7758 Made a digital in-store merchant payment: using a mobile phone, primary education or less (% ages 15+)
7759 Made a digital in-store merchant payment: using a mobile phone, secondary education or more (% ages 15+)
7773 Used a mobile phone or the internet to pay bills, primary education or less (% ages 15+)
7774 Used a mobile phone or the internet to pay bills, secondary education or more (% ages 15+)
7786 Used a mobile phone or the internet to send money, primary education or less (% ages 15+)
7787 Used a mobile phone or the internet to send money, secondary education or more (% ages 15+)
7799 Used a mobile phone or the internet to buy something online, primary education or less (% ages 15+)
7800 Used a mobile phone or the internet to buy something online, secondary education or more (% ages 15+)
7820 Saved to start, operate, or expand a farm or business, primary education or less (% ages 15+)
7821 Saved to start, operate, or expand a farm or business, secondary education or more (% ages 15+)
7835 Saved for old age, primary education or less (% ages 15+)
7836 Saved for old age, secondary education or more (% ages 15+)
7856 Saved at a financial institution or using a mobile money account, primary education or less (% ages 15+)
7857 Saved at a financial institution or using a mobile money account, secondary education or more (% ages 15+)
7869 Saved at a financial institution, primary education or less (% ages 15+)
7870 Saved at a financial institution, secondary education or more (% ages 15+)
7890 Saved money using a mobile money account, primary education or less (% ages 15+)
7891 Saved money using a mobile money account, secondary education or more (% ages 15+)
7903 Saved using a savings club or a person outside the family, primary education or less (% ages 15+)
7904 Saved using a savings club or a person outside the family, secondary education or more (% ages 15+)
7908 Saved for education or school fees (% age 15+)
7909 Saved for education or school fees, female (% age 15+)
7910 Saved for education or school fees, urban (% age 15+)
7911 Saved for education or school fees, out of labor force (% age 15+)
7912 Saved for education or school fees, in labor force (% age 15+)
7913 Saved for education or school fees, male (% age 15+)
7914 Saved for education or school fees, young (% ages 15-24)
7915 Saved for education or school fees, older (% age 25+)
7916 Saved for education or school fees, primary education or less (% ages 15+)
7917 Saved for education or school fees, secondary education or more (% ages 15+)
7918 Saved for education or school fees, income, poorest 40% (% ages 15+)
7919 Saved for education or school fees, income, richest 60% (% ages 15+)
7920 Saved for education or school fees, rural (% age 15+)
7930 Has an outstanding housing loan, primary education or less (% ages 15+)
7931 Has an outstanding housing loan, secondary education or more (% ages 15+)
7943 Owns a debit or credit card, primary education or less (% ages 15+)
7944 Owns a debit or credit card, secondary education or more (% ages 15+)
7956 Owns a debit card, primary education or less (% ages 15+)
7957 Owns a debit card, secondary education or more (% ages 15+)
7969 Borrowed for health or medical purposes, primary education or less (% ages 15+)
7970 Borrowed for health or medical purposes, secondary education or more (% ages 15+)
7982 Borrowed to start, operate, or expand a farm or business, primary education or less (% ages 15+)
7983 Borrowed to start, operate, or expand a farm or business, secondary education or more (% ages 15+)
7995 Borrowed from a store by buying on credit, primary education or less (% ages 15+)
7996 Borrowed from a store by buying on credit, secondary education or more (% ages 15+)
8000 Borrowed for education or school fees (% age 15+)
8001 Borrowed for education or school fees, female (% age 15+)
8002 Borrowed for education or school fees, urban (% age 15+)
8003 Borrowed for education or school fees, out of labor force (% age 15+)
8004 Borrowed for education or school fees, in labor force (% age 15+)
8005 Borrowed for education or school fees, male (% age 15+)
8006 Borrowed for education or school fees, young (% ages 15-24)
8007 Borrowed for education or school fees, older (% age 25+)
8008 Borrowed for education or school fees, primary education or less (% ages 15+)
8009 Borrowed for education or school fees, secondary education or more (% ages 15+)
8010 Borrowed for education or school fees, income, poorest 40% (% ages 15+)
8011 Borrowed for education or school fees, income, richest 60% (% ages 15+)
8012 Borrowed for education or school fees, rural (% age 15+)
8021 Borrowed any money from a formal financial institution or using a mobile money account, primary education or less (% ages 15+)
8022 Borrowed any money from a formal financial institution or using a mobile money account, secondary education or more (% ages 15+)
8034 Borrowed from a formal financial institution, primary education or less (% ages 15+)
8035 Borrowed from a formal financial institution, secondary education or more (% ages 15+)
8055 Borrowed from family or friends, primary education or less (% ages 15+)
8056 Borrowed from family or friends, secondary education or more (% ages 15+)
8068 Borrowed from a savings club, primary education or less (% ages 15+)
8069 Borrowed from a savings club, secondary education or more (% ages 15+)
8093 Coming up with emergency funds in 30 days: possible and very difficult, primary education or less (% ages 15+)
8094 Coming up with emergency funds in 30 days: possible and very difficult, secondary education or more (% ages 15+)
8106 Coming up with emergency funds in 30 days: possible and somewhat difficult, primary education or less (% ages 15+)
8107 Coming up with emergency funds in 30 days: possible and somewhat difficult, secondary education or more (% ages 15+)
8119 Coming up with emergency funds in 30 days: possible and not difficult at all, primary education or less (% ages 15+)
8120 Coming up with emergency funds in 30 days: possible and not difficult at all, secondary education or more (% ages 15+)
8132 Coming up with emergency funds in 30 days: possible and not difficult or somewhat difficult, primary education or less (% ages 15+)
8133 Coming up with emergency funds in 30 days: possible and not difficult or somewhat difficult, secondary education or more (% ages 15+)
8145 Coming up with emergency funds in 30 days: possible, primary education or less (% ages 15+)
8146 Coming up with emergency funds in 30 days: possible, secondary education or more (% ages 15+)
8158 Coming up with emergency funds in 30 days: not possible, primary education or less (% ages 15+)
8159 Coming up with emergency funds in 30 days: not possible, secondary education or more (% ages 15+)
8171 Coming up with emergency funds in 7 days: possible and very difficult, primary education or less (% ages 15+)
8172 Coming up with emergency funds in 7 days: possible and very difficult, secondary education or more (% ages 15+)
8184 Coming up with emergency funds in 7 days: possible and somewhat difficult, primary education or less (% ages 15+)
8185 Coming up with emergency funds in 7 days: possible and somewhat difficult, secondary education or more (% ages 15+)
8197 Coming up with emergency funds in 7 days: possible and not difficult, primary education or less (% ages 15+)
8198 Coming up with emergency funds in 7 days: possible and not difficult, secondary education or more (% ages 15+)
8210 Coming up with emergency funds in 7 days: possible and not difficult or somewhat difficult, primary education or less (% ages 15+)
8211 Coming up with emergency funds in 7 days: possible and not difficult or somewhat difficult, secondary education or more (% ages 15+)
8223 Coming up with emergency funds in 7 days: possible, primary education or less (% ages 15+)
8224 Coming up with emergency funds in 7 days: possible, secondary education or more (% ages 15+)
8244 Sent or received domestic remittances, primary education or less (% ages 15+)
8245 Sent or received domestic remittances, secondary education or more (% ages 15+)
8257 Sent domestic remittances, primary education or less (% ages 15+)
8258 Sent domestic remittances, secondary education or more (% ages 15+)
8282 Received domestic remittances, primary education or less (% ages 15+)
8283 Received domestic remittances, secondary education or more (% ages 15+)
8303 Made a utility payment, primary education or less (% ages 15+)
8304 Made a utility payment, secondary education or more (% ages 15+)
8335 Received public sector wages, primary education or less (% ages 15+)
8336 Received public sector wages, secondary education or more (% ages 15+)
8360 Received private sector wages, primary education or less (% ages 15+)
8361 Received private sector wages, secondary education or more (% ages 15+)
8373 Received wages, primary education or less (% ages 15+)
8374 Received wages, secondary education or more (% ages 15+)
8386 Paid school fees, primary education or less (% ages 15+)
8387 Paid school fees, secondary education or more (% ages 15+)
8429 Received government transfer or pension, primary education or less (% ages 15+)
8430 Received government transfer or pension, secondary education or more (% ages 15+)
8452 Received government transfer, primary education or less (% ages 15+)
8453 Received government transfer, secondary education or more (% ages 15+)
8475 Received a public sector pension, primary education or less (% ages 15+)
8476 Received a public sector pension, secondary education or more (% ages 15+)
8491 Received payments for the sale of agricultural products, livestock, or crops, primary education or less (% ages 15+)
8492 Received payments for the sale of agricultural products, livestock, or crops, secondary education or more (% ages 15+)
8514 Worried about not having enough money for old age: very worried, primary education or less (% ages 15+)
8515 Worried about not having enough money for old age: very worried, secondary education or more (% ages 15+)
8527 Worried about not having enough money for old age: somewhat worried, primary education or less (% ages 15+)
8528 Worried about not having enough money for old age: somewhat worried, secondary education or more (% ages 15+)
8540 Worried about not having enough money for old age: not worried at all, primary education or less (% ages 15+)
8541 Worried about not having enough money for old age: not worried at all, secondary education or more (% ages 15+)
8553 Worried about not being able to pay for medical costs in case of a serious illness or accident: very worried, primary education or less (% ages 15+)
8554 Worried about not being able to pay for medical costs in case of a serious illness or accident: very worried, secondary education or more (% ages 15+)
8566 Worried about not being able to pay for medical costs in case of a serious illness or accident: somewhat worried, primary education or less (% ages 15+)
8567 Worried about not being able to pay for medical costs in case of a serious illness or accident: somewhat worried, secondary education or more (% ages 15+)
8579 Worried about not being able to pay for medical costs in case of a serious illness or accident: not worried at all, primary education or less (% ages 15+)
8580 Worried about not being able to pay for medical costs in case of a serious illness or accident: not worried at all, secondary education or more (% ages 15+)
8592 Worried about not having enough money for monthly expenses or bills: very worried, primary education or less (% ages 15+)
8593 Worried about not having enough money for monthly expenses or bills: very worried, secondary education or more (% ages 15+)
8605 Worried about not having enough money for monthly expenses or bills: somewhat worried, primary education or less (% ages 15+)
8606 Worried about not having enough money for monthly expenses or bills: somewhat worried, secondary education or more (% ages 15+)
8618 Worried about not having enough money for monthly expenses or bills: not worried at all, primary education or less (% ages 15+)
8619 Worried about not having enough money for monthly expenses or bills: not worried at all, secondary education or more (% ages 15+)
8623 Worried about not being able to pay school fees or fees for education: very worried (% age 15+)
8624 Worried about not being able to pay school fees or fees for education: very worried, female (% age 15+)
8625 Worried about not being able to pay school fees or fees for education: very worried, urban (% age 15+)
8626 Worried about not being able to pay school fees or fees for education: very worried, out of labor force (% age 15+)
8627 Worried about not being able to pay school fees or fees for education: very worried, in labor force (% age 15+)
8628 Worried about not being able to pay school fees or fees for education: very worried, male (% age 15+)
8629 Worried about not being able to pay school fees or fees for education: very worried, young (% ages 15-24)
8630 Worried about not being able to pay school fees or fees for education: very worried, older (% age 25+)
8631 Worried about not being able to pay school fees or fees for education: very worried, primary education or less (% ages 15+)
8632 Worried about not being able to pay school fees or fees for education: very worried, secondary education or more (% ages 15+)
8633 Worried about not being able to pay school fees or fees for education: very worried, income, poorest 40% (% ages 15+)
8634 Worried about not being able to pay school fees or fees for education: very worried, income, richest 60% (% ages 15+)
8635 Worried about not being able to pay school fees or fees for education: very worried, rural (% age 15+)
8636 Worried about not being able to pay school fees or fees for education: somewhat worried (% age 15+)
8637 Worried about not being able to pay school fees or fees for education: somewhat worried, female (% age 15+)
8638 Worried about not being able to pay school fees or fees for education: somewhat worried, urban (% age 15+)
8639 Worried about not being able to pay school fees or fees for education: somewhat worried, out of labor force (% age 15+)
8640 Worried about not being able to pay school fees or fees for education: somewhat worried, in labor force (% age 15+)
8641 Worried about not being able to pay school fees or fees for education: somewhat worried, male (% age 15+)
8642 Worried about not being able to pay school fees or fees for education: somewhat worried, young (% ages 15-24)
8643 Worried about not being able to pay school fees or fees for education: somewhat worried, older (% age 25+)
8644 Worried about not being able to pay school fees or fees for education: somewhat worried, primary education or less (% ages 15+)
8645 Worried about not being able to pay school fees or fees for education: somewhat worried, secondary education or more (% ages 15+)
8646 Worried about not being able to pay school fees or fees for education: somewhat worried, income, poorest 40% (% ages 15+)
8647 Worried about not being able to pay school fees or fees for education: somewhat worried, income, richest 60% (% ages 15+)
8648 Worried about not being able to pay school fees or fees for education: somewhat worried, rural (% age 15+)
8649 Worried about not being able to pay school fees or fees for education: not worried at all (% age 15+)
8650 Worried about not being able to pay school fees or fees for education: not worried at all, female (% age 15+)
8651 Worried about not being able to pay school fees or fees for education: not worried at all, urban (% age 15+)
8652 Worried about not being able to pay school fees or fees for education: not worried at all, out of labor force (% age 15+)
8653 Worried about not being able to pay school fees or fees for education: not worried at all, in labor force (% age 15+)
8654 Worried about not being able to pay school fees or fees for education: not worried at all, male (% age 15+)
8655 Worried about not being able to pay school fees or fees for education: not worried at all, young (% ages 15-24)
8656 Worried about not being able to pay school fees or fees for education: not worried at all, older (% age 25+)
8657 Worried about not being able to pay school fees or fees for education: not worried at all, primary education or less (% ages 15+)
8658 Worried about not being able to pay school fees or fees for education: not worried at all, secondary education or more (% ages 15+)
8659 Worried about not being able to pay school fees or fees for education: not worried at all, income, poorest 40% (% ages 15+)
8660 Worried about not being able to pay school fees or fees for education: not worried at all, income, richest 60% (% ages 15+)
8661 Worried about not being able to pay school fees or fees for education: not worried at all, rural (% age 15+)
8670 Experience or continue to experience severe financial hardship as a result of the disruption caused by COVID-19: very worried, primary education or less (% ages 15+)
8671 Experience or continue to experience severe financial hardship as a result of the disruption caused by COVID-19: very worried, secondary education or more (% ages 15+)
8683 Experience or continue to experience severe financial hardship as a result of the disruption caused by COVID-19: somewhat worried, primary education or less (% ages 15+)
8684 Experience or continue to experience severe financial hardship as a result of the disruption caused by COVID-19: somewhat worried, secondary education or more (% ages 15+)
8696 Experience or continue to experience severe financial hardship as a result of the disruption caused by COVID-19: not worried, primary education or less (% ages 15+)
8697 Experience or continue to experience severe financial hardship as a result of the disruption caused by COVID-19: not worried, secondary education or more (% ages 15+)
8709 Most worrying financial issue: money for old age, primary education or less (% ages 15+)
8710 Most worrying financial issue: money for old age, secondary education or more (% ages 15+)
8722 Most worrying financial issue: paying for medical costs in case of a serious illness or accident, primary education or less (% ages 15+)
8723 Most worrying financial issue: paying for medical costs in case of a serious illness or accident, secondary education or more (% ages 15+)
8735 Most worrying financial issue: money to pay for monthly expenses or bills, primary education or less (% ages 15+)
8736 Most worrying financial issue: money to pay for monthly expenses or bills, secondary education or more (% ages 15+)
8740 Most worrying financial issue: paying school or education fees (% age 15+)
8741 Most worrying financial issue: paying school or education fees, female (% age 15+)
8742 Most worrying financial issue: paying school or education fees, urban (% age 15+)
8743 Most worrying financial issue: paying school or education fees, out of labor force (% age 15+)
8744 Most worrying financial issue: paying school or education fees, in labor force (% age 15+)
8745 Most worrying financial issue: paying school or education fees, male (% age 15+)
8746 Most worrying financial issue: paying school or education fees, young (% ages 15-24)
8747 Most worrying financial issue: paying school or education fees, older (% age 25+)
8748 Most worrying financial issue: paying school or education fees, primary education or less (% ages 15+)
8749 Most worrying financial issue: paying school or education fees, secondary education or more (% ages 15+)
8750 Most worrying financial issue: paying school or education fees, income, poorest 40% (% ages 15+)
8751 Most worrying financial issue: paying school or education fees, income, richest 60% (% ages 15+)
8752 Most worrying financial issue: paying school or education fees, rural (% age 15+)
8775 Used a mobile phone or the internet to access an account, primary education or less (% ages 15+)
8776 Used a mobile phone or the internet to access an account, secondary education or more (% ages 15+)
8803 Owns a credit card, primary education or less (% ages 15+)
8804 Owns a credit card, secondary education or more (% ages 15+)
8824 Has an inactive account, primary education or less (% ages 15+)
8825 Has an inactive account, secondary education or more (% ages 15+)
8848 Received government payments, primary education or less (% ages 15+)
8849 Received government payments, secondary education or more (% ages 15+)
8992 Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)
8993 Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)
9004 Made a digital payment, primary education or less (% ages 15+)
9005 Made a digital payment, secondary education or more (% ages 15+)
9017 Received digital payments, primary education or less (% ages 15+)
9018 Received digital payments, secondary education or more (% ages 15+)
9030 Made or received a digital payment, primary education or less (% ages 15+)
9031 Made or received a digital payment, secondary education or more (% ages 15+)
9227 4th pllar: Health and primary education
9228 5th pillar: Higher education and training
9870 DHS: Net intake rate for the first grade of primary education
9871 DHS: Net intake rate for the first grade of primary education. Female
9872 DHS: Net intake rate for the first grade of primary education. Male
9873 DHS: Net intake rate for the first grade of primary education. Quintile 1
9874 DHS: Net intake rate for the first grade of primary education. Quintile 2
9875 DHS: Net intake rate for the first grade of primary education. Quintile 3
9876 DHS: Net intake rate for the first grade of primary education. Quintile 4
9877 DHS: Net intake rate for the first grade of primary education. Quintile 5
9878 DHS: Net intake rate for the first grade of primary education. Rural
9879 DHS: Net intake rate for the first grade of primary education. Urban
10154 Monthly Per Capita Household Education Expenditure (in IDR)
10676 ID ownership, primary education or less (% age 15+)
10678 ID ownership, secondary education or more (% age 15+)
10775 Teacher Education Institutes (DIETs, CTEs, IASEs)
11040 Personal computers installed in education
11135 Average age of employers, aged 15-64, above primary education
11136 Average age of employers, aged 15-64, primary education and below
11144 Average age of self-employed or unpaid workers, aged 15-64, above primary education
11145 Average age of self-employed or unpaid workers, aged 15-64, primary education and below
11153 Average age, above primary education
11154 Average age, primary education and below
11162 Average age of wage workers, aged 15-64, above primary education
11163 Average age of wage workers, aged 15-64, primary education and below
11171 Average age of workers, aged 15-64, above primary education
11172 Average age of workers, aged 15-64, primary education and below
11180 Average age of workers in the agricultural sector, aged 15-64, above primary education
11181 Average age of workers in the agricultural sector, aged 15-64, primary education and below
11188 Wage employment in agriculture, aged 15-64, above primary education (% of workers with high education in the agricultural sector)
11189 Wage employment in agriculture, aged 15-64, primary education and below (% of workers with low education in the agricultural sector)
11193 Median earnings per month in the agricultural sector, aged 15-64, local currency values, above primary education
11194 Median earnings per month in the agricultural sector, aged 15-64, local currency values, primary education and below
11205 Average number of completed years in formal education, aged 17 and above, total
11206 Average number of completed years in formal education, aged 17 and above, female
11207 Average number of completed years in formal education, aged 17 and above, above primary education
11208 Average number of completed years in formal education, aged 17 and above, primary education and below
11209 Average number of completed years in formal education, aged 17 and above, male
11210 Average number of completed years in formal education, aged 25 and above
11211 Average number of completed years in formal education, aged 17 and above, rural
11212 Average number of completed years in formal education, aged 17 and above, urban
11213 Average number of completed years in formal education, aged 17-24
11215 Youth employment rate, aged 15-24, above primary education (% of youth labor force with high education)
11216 Youth employment rate, aged 15-24, primary education and below (% of youth labor force with low education)
11222 Employment rate, aged 15-64, above primary education (% of labor force with high education in working age)
11223 Employment rate, aged 15-64, primary education and below (% of labor force with low education in working age)
11231 Employment in the agricultural sector, aged 15-64, above primary education (% of employed population with high education in working age)
11232 Employment in the agricultural sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11240 Employment in the armed forces occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11241 Employment in the armed forces occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11249 Employment in the clerks occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11250 Employment in the clerks occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11258 Employment in the construction sector, aged 15-64, above primary education (% of employed population with high education in working age)
11259 Employment in the construction sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11267 Employment in the commerce sector, aged 15-64, above primary education (% of employed population with high education in working age)
11268 Employment in the commerce sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11276 Employed workers with a work contract, aged 15-64, above primary education (% of employed population with high education in working age)
11277 Employed workers with a work contract, aged 15-64, primary education and below (% of employed population with low education in working age)
11285 Employment in the craft workers occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11286 Employment in the craft workers occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11294 Employment in the eletricity and public utilities sector, aged 15-64, above primary education (% of employed population with high education in working age)
11295 Employment in the eletricity and public utilities sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11303 Employment in the elementary occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11304 Employment in the elementary occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11312 Employment in the financial and business services sector, aged 15-64, above primary education (% of employed population with high education in working age)
11313 Employment in the financial and business services sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11321 Employed workers with health insurance, aged 15-64, above primary education (% of employed population with high education in working age)
11322 Employed workers with health insurance, aged 15-64, primary education and below (% of employed population with low education in working age)
11330 Informal job workers, aged 15-64, above primary education (% of employed population with high education in working age)
11331 Informal job workers, aged 15-64, primary education and below (% of employed population with low education in working age)
11339 Employment in the industrial sector, aged 15-64, above primary education (% of employed population with high education in working age)
11340 Employment in the industrial sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11348 Employment in the machine operators occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11349 Employment in the machine operators occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11357 Employment in the manufacturing sector, aged 15-64, above primary education (% of employed population with high education in working age)
11358 Employment in the manufacturing sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11366 Employment in the mining sector, aged 15-64, above primary education (% of employed population with high education in working age)
11367 Employment in the mining sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11375 Employers, aged 15-64, above primary education (% of employed population with high education in working age)
11376 Employers, aged 15-64, primary education and below (% of employed population with low education in working age)
11379 Non-agricultural employers, aged 15-64, above primary education (% of employed population with high education in working age)
11380 Non-agricultural employers, aged 15-64, primary education and below (% of employed population with low education in working age)
11392 Female in non-agricultural employment, aged 15-64, above primary education (% of employed female population with high education in working age)
11393 Female in non-agricultural employment, aged 15-64, primary education and below (% of employed female population with low education in working age)
11400 Youth in non-agricultural employment, aged 15-24, above primary education (% of employed youth with high education aged 15-24)
11401 Youth in non-agricultural employment, aged 15-24, primary education and below (% of employed youth with low education aged 15-24)
11407 Employment in the other services sector, aged 15-64, above primary education (% of employed population with high education in working age)
11408 Employment in the other services sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11416 Employment in the public administration sector, aged 15-64, above primary education (% of employed population with high education in working age)
11417 Employment in the public administration sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11425 Employment in the professionals occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11426 Employment in the professionals occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11434 Employment in the public sector, aged 15-64, above primary education (% of employed population with high education in working age)
11435 Employment in the public sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11443 Self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)
11444 Self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)
11447 Non-agricultural self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)
11448 Non-agricultural self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)
11461 Employment in the senior officials occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11462 Employment in the senior officials occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11470 Employment in the service sector, aged 15-64, above primary education (% of employed population with high education in working age)
11471 Employment in the service sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11479 Employment in the skilled agriculture occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11480 Employment in the skilled agriculture occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11488 Employed workers with social security, aged 15-64, above primary education (% of employed population with high education in working age)
11489 Employed workers with social security, aged 15-64, primary education and below (% of employed population with low education in working age)
11497 Employment in the service and market sales occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11498 Employment in the service and market sales occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11506 Employment in the technicians occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
11507 Employment in the technicians occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
11515 Employment to population ratio, aged 15-64, above primary education (% of population with high education in working age)
11516 Employment to population ratio, aged 15-64, primary education and below (% of population with low education in working age)
11524 Employment in the transport and communication sector, aged 15-64, above primary education (% of employed population with high education in working age)
11525 Employment in the transport and communication sector, aged 15-64, primary education and below (% of employed population with low education in working age)
11533 Unpaid workers, aged 15-64, above primary education (% of employed population with high education in working age)
11534 Unpaid workers, aged 15-64, primary education and below (% of employed population with low education in working age)
11537 Non-agricultural unpaid employment, aged 15-64, above primary education (% of employed population with high education in working age)
11538 Non-agricultural unpaid employment, aged 15-64, primary education and below (% of employed population with low education in working age)
11551 Unpaid or self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)
11552 Unpaid or self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)
11560 Youth in non-agricultural wage employment, aged 15-24, above primary education (% of employed youth with high education aged 15-24)
11561 Youth in non-agricultural wage employment, aged 15-24, primary education and below (% of employed youth with low education aged 15-24)
11567 Wage workers, aged 15-64, above primary education (% of employed population with high education in working age)
11568 Wage workers, aged 15-64, primary education and below (% of employed population with low education in working age)
11571 Non-agricultural wage employment, aged 15-64, above primary education (% of employed population with high education in working age)
11572 Non-agricultural wage employment, aged 15-64, primary education and below (% of employed population with low education in working age)
11585 Enrollment rate, aged 6-16, above primary education (% of population with high education aged 6-16)
11586 Enrollment rate, aged 6-16, primary education and below (% of population with low education aged 6-16)
11594 Average age of workers in the industrial sector, aged 15-64, above primary education
11595 Average age of workers in the industrial sector, aged 15-64, primary education and below
11602 Wage employment in industry, aged 15-64, above primary education (% of workers with high education in the industrial sector)
11603 Wage employment in industry, aged 15-64, primary education and below (% of workers with low education in the industrial sector)
11607 Median earnings per month in the industrial sector, aged 15-64, local currency values, above primary education
11608 Median earnings per month in the industrial sector, aged 15-64, local currency values, primary education and below
11620 Workers with more than one jobs in last week, aged 15-64, above primary education (% of employed population with high education in working age)
11621 Workers with more than one jobs in last week, aged 15-64, primary education and below (% of employed population with low education in working age)
11629 Population aged 0-14, above primary education (% of population with high edcuation)
11630 Population aged 0-14, primary education and below (% of population with low edcuation)
11636 Population aged 15-24, above primary education (% of population with high edcuation)
11637 Population aged 15-24, primary education and below (% of population with low edcuation)
11643 Working-age population, aged 15-64, above primary education (% of population with high education)
11644 Working-age population, aged 15-64, primary education and below (% of population with low education)
11650 Population aged 25-64, above primary education (% of population with high edcuation)
11651 Population aged 25-64, primary education and below (% of population with low edcuation)
11657 Population aged 65 and above, above primary education (% of population with high edcuation)
11658 Population aged 65 and above, primary education and below (% of population with low edcuation)
11666 Working-age population with no education, female (% of female population in working age)
11667 Working-age population with no education, primary education and below (% of population with low education in working age)
11668 Working-age population with no education, male (% of male population in working age)
11669 Working-age population with no education, aged 25-64 (% of population aged 25-64)
11670 Working-age population with no education, rural (% of rural population in working age)
11671 Working-age population with no education, urban (% of urban population in working age)
11672 Working-age population with no education, aged 15-24 (% of population aged 15-24)
11673 Working-age population with no education, total (% of total population in working age)
11674 Working-age population with primary education, female (% of female population in working age)
11675 Working-age population with primary education, primary education and below (% of population with low education in working age)
11676 Working-age population with primary education, male (% of male population in working age)
11677 Working-age population with primary education, aged 25-64 (% of population aged 25-64)
11678 Working-age population with primary education, rural (% of rural population in working age)
11679 Working-age population with primary education, urban (% of urban population in working age)
11680 Working-age population with primary education, aged 15-24 (% of population aged 15-24)
11681 Working-age population with primary education, total (% of total population in working age)
11682 Working-age population with secondary education, female (% of female population in working age)
11683 Working-age population with secondary education, above primary education (% of population with high education in working age)
11684 Working-age population with secondary education, male (% of male population in working age)
11685 Working-age population with secondary education, aged 25-64 (% of population aged 25-64)
11686 Working-age population with post-secondary education, female (% of female population in working age)
11687 Working-age population with post-secondary education, above primary education (% of population with high education in working age)
11688 Working-age population with post-secondary education, male (% of male population in working age)
11689 Working-age population with post-secondary education, aged 25-64 (% of population aged 25-64)
11690 Working-age population with post-secondary education, rural (% of rural population in working age)
11691 Working-age population with post-secondary education, urban (% of urban population in working age)
11692 Working-age population with post-secondary education, aged 15-24 (% of population aged 15-24)
11693 Working-age population with post-secondary education, total (% of total population in working age)
11694 Working-age population with secondary education, rural (% of rural population in working age)
11695 Working-age population with secondary education, urban (% of urban population in working age)
11696 Working-age population with secondary education, aged 15-24 (% of population aged 15-24)
11697 Working-age population with secondary education, total (% of total population in working age)
11700 Total sample population, above primary education
11701 Total sample population, primary education and below
11708 Urban population, above primary education (% of population with high education)
11709 Urban population, primary education and below (% of population with low education)
11716 Average age of workers in the service sector, aged 15-64, above primary education
11717 Average age of workers in the service sector, aged 15-64, primary education and below
11724 Wage employment in services, aged 15-64, above primary education (% of workers with high education in the service sector)
11725 Wage employment in services, aged 15-64, primary education and below (% of workers with low education in the service sector)
11729 Median earnings per month in the service sector, aged 15-64, local currency values, above primary education
11730 Median earnings per month in the service sector, aged 15-64, local currency values, primary education and below
11742 Average weekly working hours, aged 15-64, above primary education
11743 Average weekly working hours, aged 15-64, primary education and below
11751 Underemployment, less than 35 hours per week, aged 15-64, above primary education (% of employed population with high education in working age)
11752 Underemployment, less than 35 hours per week, aged 15-64, primary education and below (% of employed population with low education in working age)
11760 Excessive working hours, more than 48 hours per week, aged 15-64, above primary education (% of employed population with high education in working age)
11761 Excessive working hours, more than 48 hours per week, aged 15-64, primary education and below (% of employed population with low education in working age)
11769 Labor force participation rate, aged 15-64, above primary education (% of labor force with high education in working age)
11770 Labor force participation rate, aged 15-64, primary education and below (% of labor force with low education in working age)
11779 Total labor force in the sample, aged 15-64, above primary education
11780 Total labor force in the sample, aged 15-64, primary education and below
11787 Youth unemployment rate, aged 15-24, above primary education (% of youth labor force with high education)
11788 Youth unemployment rate, aged 15-24, primary education and below (% of youth labor force with low education)
11794 Unemployment rate, aged 15-64, above primary education (% of labor force with high education in working age)
11795 Unemployment rate, aged 15-64, primary education and below (% of labor force with low education in working age)
11802 Youth not in employment or education, aged 15-24, female (% of female youth population)
11803 Youth not in employment or education, aged 15-24, above primary education (% of youth population with high education)
11804 Youth not in employment or education, aged 15-24, primary education and below (% of youth population with low education)
11805 Youth not in employment or education, aged 15-24, male (% of male youth population)
11806 Youth not in employment or education, aged 15-24, rural (% of rural youth population)
11807 Youth not in employment or education, aged 15-24, urban (% of urban youth population)
11808 Youth not in employment or education, aged 15-24, total (% of total youth population)
11810 Female to male gender wage gap, aged 15-64, above primary education
11811 Female to male gender wage gap, aged 15-64, primary education and below
11822 Real median earnings per hour, aged 15-64, deflated to 2010 and PPP adjusted, above primary education
11823 Median earnings per hour, aged 15-64, local currency values, above primary education
11824 Median earnings per hour, aged 15-64, deflated to 2010 local currency values, above primary education
11825 Real median earnings per hour, aged 15-64, deflated to 2010 and PPP adjusted, primary education and below
11826 Median earnings per hour, aged 15-64, local currency values, primary education and below
11827 Median earnings per hour, aged 15-64, deflated to 2010 local currency values, primary education and below
11849 Real median earnings per month, aged 15-64, deflated to 2010 and PPP adjusted, above primary education
11850 Median earnings per month, aged 15-64, local currency values, above primary education
11851 Median earnings per month, aged 15-64, deflated to 2010 local currency values, above primary education
11852 Real median earnings per month, aged 15-64, deflated to 2010 and PPP adjusted, primary education and below
11853 Median earnings per month, aged 15-64, local currency values, primary education and below
11854 Median earnings per month, aged 15-64, deflated to 2010 local currency values, primary education and below
11872 Public to private wage gap, aged 15-64, above primary education
11873 Public to private wage gap, aged 15-64, primary education and below
12494 PASEC: Mean performance on the mathematics scale for 2nd grade students who did not attend pre-primary education
12504 PASEC: Mean performance on the mathematics scale for 2nd grade students who attended pre-primary education
12505 PASEC: Average performance gap between 2nd grade students who attended/did not attend pre-primary education. Mathematics
12506 PASEC: Average performance gap between 2nd grade students in private and public education. Mathematics
12519 PASEC: Mean performance on the mathematics scale for 6th grade students who did not attend pre-primary education
12529 PASEC: Mean performance on the mathematics scale for 6th grade students who attended pre-primary education
12530 PASEC: Average performance gap between 6th grade students who attended/did not attend pre-primary education. Mathematics
12531 PASEC: Average performance gap between 6th grade students in private and public education. Mathematics
12562 PASEC: Mean performance on the language scale for 2nd grade students who did not attend pre-primary education
12572 PASEC: Mean performance on the language scale for 2nd grade students who attended pre-primary education
12573 PASEC: Average performance gap between 2nd grade students who attended/did not attend pre-primary education. Language
12574 PASEC: Average performance gap between 2nd grade students in private and public education. Language
12590 PASEC: Mean performance on the reading scale for 6th grade students who did not attend pre-primary education
12600 PASEC: Mean performance on the reading scale for 6th grade students who attended pre-primary education
12601 PASEC: Average performance gap between 6th grade students who attended/did not attend pre-primary education. Reading
12602 PASEC: Average performance gap between 6th grade students in private and public education. Reading
13035 Made a digital merchant payment, primary education or less (% ages 15+)
13036 Made a digital merchant payment, secondary education or more (% ages 15+)
13145 Mobile money account, primary education or less (% ages 15+)
13146 Mobile money account, secondary education or more (% ages 15+)
13173 GDP on Education Services Sector (in IDR Million), SNA 2008, Current Price
13174 GDP on Education Services Sector (in IDR Million), SNA 2008, Constant Price
13716 Adjusted savings: education expenditure (current US$)
13717 Adjusted savings: education expenditure (% of GNI)
13815 Genuine savings: education expenditure (% of GDP)
14019 World Bank: Share of total capital education expenditures for administration (%)
14026 World Bank: Share of education expenditures from international sources for adult education (%)
14027 World Bank: Salaries' share of adult education expenditures (%)
14028 World Bank: Share of total education expenditures for adult education salaries (%)
14029 World Bank: Average annual salary for adult education instructors (in USD or local currency)
14030 World Bank: Share of adult education expenditures from government sources (%)
14031 World Bank: Share of adult education expenditures from international sources (%)
14032 World Bank: Share of total education expenditure for adult education (%)
14033 World Bank: Per capita education expenditures (in USD or local currency)
14036 World Bank: Share of GDP for central government expenditures on education (%)
14039 World Bank: Share of total education expenditures for contract teachers (%)
14043 World Bank: Capital share of total education expenditure (%)
14044 World Bank: Public capital expenditure on education as % of GDP
14045 World Bank: Total education infrastructure expenditures (local currency)
14046 World Bank: Public capital expenditure on education as a % of total capital government expenditure
14047 World Bank: Total Capital Expenditures on Education (USD Millions or local currency)
14048 World Bank: Ratio of average education sector salary to average national wage
14050 World Bank: Wastage Index (%), recurrent education expenditures
14051 World Bank: Wastage Index (%), total education expenditures
14052 World Bank: Capital education budget execution rate (%)
14053 World Bank: Capital education budget execution rate (%), domestically financed
14054 World Bank: Capital education budget execution rate (%), internationally financed
14055 World Bank: Recurrent education budget execution rate (%)
14056 World Bank: Education salary execution rate (%)
14057 World Bank: Public education expenditures as a % of GNP
14058 World Bank: Total education expenditures on goods and services (in USD or local currency)
14060 World Bank: Ministry of Education employees (% administration/non-teaching)
14061 World Bank: Share of total public sector employees for the education sector (%)
14062 World Bank: Ministry of Education employees (% teachers)
14063 World Bank: Share of total education expenditures for non-salary items (%)
14064 World Bank: Share of total recurrent education expenditures for non-salary items (%)
14065 World Bank: Share of total education expenditures for the poorest 40% of students (%)
14069 World Bank: Public education expenditure per student (% of GDP per capita)
14082 World Bank: Share of total education expenditures for non-poor students (%)
14083 World Bank: Share of total education expenditures for poor students (%)
14084 World Bank: Share of total education expenditures for Quintile 1 (poorest) (%)
14085 World Bank: Share of total education expenditures for Quintile 2 (%)
14086 World Bank: Share of total education expenditures for Quintile 3 (%)
14087 World Bank: Share of total education expenditures for Quintile 4 (%)
14088 World Bank: Share of total education expenditures for Quintile 5 (richest) (%)
14089 World Bank: Share of total education expenditures for the richest 40% of students (%)
14090 World Bank: Share of total government expenditures (all sectors) for recurrent education expenditures (%)
14091 World Bank: Public recurrent expenditure on education (% of GDP)
14093 World Bank: Share of recurrent education expenditures for salaries (%)
14094 World Bank: Recurrent share of total education expenditures (%)
14095 World Bank: Public recurrent education expenditure as % of total recurrent government expenditure
14096 World Bank: Share of recurrent education expenditures for teachers salaries (%)
14097 World Bank: Total Recurrent Expenditures on Education (USD Millions or local currency)
14098 World Bank: Share of total education expenditures for salaries (%)
14100 World Bank: Education salary expenditures as a % of GDP
14103 World Bank: Share of total government salary expenditure for education salaries (%)
14104 World Bank: Total Salary Expenditures on Education (USD or local currency)
14109 World Bank: Share of GDP for subnational government expenditures on education (%)
14113 World Bank: Share of total education expenditures for teachers salaries (%)
14116 World Bank: Total education expenditure as % of GDP
14117 World Bank: Total public education expenditure as % of GDP
14118 World Bank: Total public education expenditure, % of government spending
14119 World Bank: Total education expenditure (in local currency)
14120 World Bank: Share of total education expenditure not allocated by educational level (%)
14121 World Bank: Total education expenditure (in USD millions)
14122 World Bank: Share of basic education expenditures for administration (%)
14123 World Bank: Capital share of basic education expenditures (%)
14124 World Bank: Share of total capital education expenditure (%), basic
14126 World Bank: Wastage Index (%), Basic Education
14127 World Bank: Capital education budget execution rate (%), basic
14128 World Bank: Recurrent education budget execution rate (%), basic
14129 World Bank: Education budget execution rate, basic (%)
14130 World Bank: Share of GDP for education expenditures on basic education (%)
14131 World Bank: Share of basic education recurrent expenditures for goods and services (%)
14132 World Bank: Household spending per student on room/board for basic education (USD or local currency)
14133 World Bank: Private/Household spending on basic education, Quintile 1
14134 World Bank: Private/Household spending on basic education, Quintile 2
14135 World Bank: Private/Household spending on basic education, Quintile 3
14136 World Bank: Private/Household spending on basic education, Quintile 4
14137 World Bank: Private/Household spending on basic education, Quintile 5
14138 World Bank: Household spending per student on textbooks for basic education (USD or local currency)
14139 World Bank: Household spending per student on transportation for basic education (USD or local currency)
14140 World Bank: Household spending per student on tuition for basic education (USD or local currency)
14141 World Bank: Household spending per student on uniforms/clothing for basic education (USD or local currency)
14142 World Bank: Total instructional hours in basic education (Grades 1-8)
14143 World Bank: Share of education expenditures from international sources for basic education (%)
14144 World Bank: Pupil/Classroom Ratio, basic education
14145 World Bank: Pupils/Class, basic education
14146 World Bank: Public education expenditure per student (% of GDP per capita), basic
14147 World Bank: Per student non-salary spending, basic education (in USD or local currency)
14148 World Bank: Recurrent expenditures per basic education student (USD or local currency)
14149 World Bank: Ratio of basic per capita/per student recurrent expenditures relative to primary education
14151 World Bank: Pupil/Total Staff Ratio, basic education
14152 World Bank: Pupil/Teacher Ratio, basic education
14153 World Bank: Share of basic education expenditures for Quintile 1 (poorest) (%)
14154 World Bank: Share of basic education expenditures for Quintile 2 (%)
14155 World Bank: Share of basic education expenditures for Quintile 3 (%)
14156 World Bank: Share of basic education expenditures for Quintile 4 (%)
14157 World Bank: Share of basic education expenditures for Quintile 5 (richest) (%)
14158 World Bank: Share of total basic education expenditures for non-poor students (%)
14159 World Bank: Share of education expenditures for the poorest 40% of students (%), Basic
14160 World Bank: Share of total basic education expenditures for poor students (%)
14161 World Bank: Share of education expenditures for the richest 40% of students (%), Basic
14162 World Bank: Recurrent share of basic education expenditures (%)
14163 World Bank: Share of total recurrent education expenditure (%), basic
14164 World Bank: Salaries' share of basic education expenditures (%)
14165 World Bank: Ratio of Teacher Salaries to per capita GDP, basic education
14166 World Bank: Share of recurrent education expenditures for basic education salaries (%)
14167 World Bank: Share of total salary expenditures for basic education (%)
14168 World Bank: Share of total education expenditures for basic education salaries (%)
14169 World Bank: Share of basic education expenditures from government sources (%)
14170 World Bank: Share of teachers for basic education (%)
14171 World Bank: Teaching/Non-Teaching Staff Ratio, basic education
14172 World Bank: Share of total education expenditure for basic education (%)
14173 World Bank: Capital share of total education expenditure (%), basic
14174 World Bank: Total expenditures on basic education (USD or local currency)
14175 World Bank: Share of basic education recurrent expenditures for transfers (%)
14177 World Bank: Share of GDP potentially saved with improved targeting of education subsidies (%)
14178 World Bank: Total potential savings with improved targeting of government education subsidies (USD or local currency)
14181 World Bank: Share of total national employment for the education sector (%)
14192 World Bank: Share of total domestically-financed/government expenditures for education (%)
14194 World Bank: Share of total household education spending/school fees appropriated to provincial/regional/central government levels from the school level (%)
14195 World Bank: Share of total household education expenditures for the poorest 40%
14196 World Bank: Share of total household education expenditures for Quintile 1 (%)
14197 World Bank: Share of total household education expenditures for Quintile 2 (%)
14198 World Bank: Share of total household education expenditures for Quintile 3 (%)
14199 World Bank: Share of total household education expenditures for Quintile 4 (%)
14200 World Bank: Share of total household education expenditures for Quintile 5 (%)
14201 World Bank: Share of total household education expenditures for the richest 40%
14202 World Bank: Share of total household education spending for salaries (%)
14203 World Bank: Share of international commitments for education disbursed to government (%)
14206 World Bank: Share of total capital education expenditure (%), junior secondary
14210 World Bank: Share of education expenditures from international sources for junior secondary education (%)
14213 World Bank: Public education expenditure per student (% of GDP per capita), junior secondary
14220 World Bank: Share of junior secondary education expenditures for Quintile 1 (poorest) (%)
14221 World Bank: Share of junior secondary education expenditures for Quintile 2 (%)
14222 World Bank: Share of junior secondary education expenditures for Quintile 3 (%)
14223 World Bank: Share of junior secondary education expenditures for Quintile 4 (%)
14224 World Bank: Share of junior secondary education expenditures for Quintile 5 (richest) (%)
14225 World Bank: Share of education expenditures for the poorest 40% of students (%), Junior Secondary
14226 World Bank: Share of education expenditures for the richest 40% of students (%), Junior Secondary
14228 World Bank: Share of total recurrent education expenditure (%), junior secondary
14231 World Bank: Share of recurrent education expenditures for junior secondary salaries (%)
14233 World Bank: Share of total education expenditures for junior secondary salaries (%)
14235 World Bank: Share of junior secondary education expenditures from government sources (%)
14237 World Bank: Share of teachers for junior secondary education (%)
14239 World Bank: Share of total education expenditure for junior secondary education (%)
14240 World Bank: Total expenditures on junior secondary education (USD or local currency)
14241 World Bank: Per student transfer by central to subnational government, junior secondary education (USD or local currency)
14243 World Bank: Share of total recurrent education expenditure for administration (%)
14245 World Bank: Share of total education expenditure for administration (%)
14248 World Bank: School Feeding Programs (% of total education expenditures)
14252 World Bank: Share of recurrent primary education expenditures for scholarships (%)
14258 World Bank: Share of total education spending for scholarships (%)
14267 World Bank: Share of scholarships/subsidy expenditures for tertiary education (%)
14269 World Bank: Share of scholarships/subsidy expenditures for technical/vocational education (%)
14276 World Bank: Share of education spending on rural areas (%)
14277 World Bank: Share of junior secondary education spending on rural areas (%)
14280 World Bank: Share of pre-primary education spending on rural areas (%)
14282 World Bank: Share of primary education spending on rural areas (%)
14287 World Bank: Share of secondary education spending on rural areas (%)
14289 World Bank: Share of senior secondary education spending on rural areas (%)
14290 World Bank: Share of tertiary education spending on rural areas (%)
14294 World Bank: Share of education spending on urban areas (%)
14295 World Bank: Share of junior secondary education spending on urban areas (%)
14298 World Bank: Share of pre-primary education spending on urban areas (%)
14300 World Bank: Share of primary education spending on urban areas (%)
14305 World Bank: Share of secondary education spending on urban areas (%)
14307 World Bank: Share of senior secondary education spending on urban areas (%)
14308 World Bank: Share of tertiary education spending on urban areas (%)
14310 World Bank: Share of total public education expenditure for private institutions (%)
14311 World Bank: Total education expenditure lost to HIV/AIDS related teacher absenteeism (USD millions)
14316 World Bank: Share of total education expenditures for HIV/AIDS interventions (%)
14317 World Bank: Share of total education expenditure lost due to HIV/AIDS (%)
14318 World Bank: Additional cost of teacher education due to HIV/AIDS (USD millions)
14319 World Bank: Total education expenditure lost to HIV/AIDS (USD millions)
14320 World Bank: Operations and Maintenance (% of recurrent education expenditure)
14321 World Bank: Operations and Maintenance (% of total education expenditure)
14323 World Bank: Share of total education expenditures for special education/disabilities (%)
14324 World Bank: Teaching/learning materials (% of total education expenditures)
14325 World Bank: Teaching/learning materials (% of total recurrent education expenditures)
14327 World Bank: Central Government Transfers for education (% of GDP)
14331 World Bank: Share of recurrent education expenditures for transfers (%)
14334 World Bank: Share of total central government transfers for education (%)
14335 World Bank: Share of total education expenditures for transfers (%)
14338 World Bank: Teacher Training (% of recurrent education expenditures)
14339 World Bank: Teacher Training (% of total education expenditure)
14340 World Bank: Textbooks (% of total recurrent education expenditures)
14341 World Bank: Textbooks (% of total education expenditures)
14342 World Bank: Utilities (% of recurrent education expenditure)
14343 World Bank: Utilities (% of total education expenditure)
14346 World Bank: Share of total capital education expenditure (%), pre-primary
14348 World Bank: Capital education budget execution rate (%), pre-primary
14349 World Bank: Recurrent education budget execution rate (%), pre-primary
14350 World Bank: Education budget execution rate, pre-primary (%)
14351 World Bank: Share of GDP for education expenditures on pre-primary education (%)
14352 World Bank: Share of education expenditures from international sources for pre-primary education (%)
14356 World Bank: Public education expenditure per student (% of GDP per capita), pre-primary
14359 World Bank: Ratio of pre-primary per capita/per student recurrent expenditures relative to primary education
14364 World Bank: Share of education expenditures for the poorest 40% of students (%), Pre-Primary
14365 World Bank: Share of pre-primary education expenditures for Quintile 1 (poorest) (%)
14366 World Bank: Share of pre-primary education expenditures for Quintile 2 (%)
14367 World Bank: Share of pre-primary education expenditures for Quintile 3 (%)
14368 World Bank: Share of pre-primary education expenditures for Quintile 4 (%)
14369 World Bank: Share of pre-primary education expenditures for Quintile 5 (richest) (%)
14370 World Bank: Share of total pre-primary education expenditures for non-poor students (%)
14371 World Bank: Share of total pre-primary education expenditures for poor students (%)
14372 World Bank: Share of education expenditures for the richest 40% of students (%), Pre-Primary
14374 World Bank: Share of total recurrent education expenditure (%), pre-primary
14376 World Bank: Share of recurrent education expenditures for pre-primary salaries (%)
14377 World Bank: Share of total education expenditures for pre-primary salaries (%)
14379 World Bank: Share of pre-primary education expenditures from government sources (%)
14380 World Bank: Share of teachers for pre-primary education (%)
14382 World Bank: Share of total education expenditure for pre-primary education (%)
14383 World Bank: Total expenditures on pre-primary education (USD or local currency)
14393 World Bank: Share of total capital education expenditure (%), primary
14399 World Bank: Cost Burden of Teacher Oversupply (% of total education expenditures)
14402 World Bank: Capital education budget execution rate (%), primary
14403 World Bank: Recurrent education budget execution rate (%), primary
14405 World Bank: Education budget execution rate, primary (%)
14406 World Bank: Share of GDP for education expenditures on primary education (%)
14408 World Bank: Household spending per student on room/board for primary education (USD or local currency)
14410 World Bank: Household spending per student on textbooks for primary education (USD or local currency)
14411 World Bank: Household spending per student on transportation for primary education (USD or local currency)
14412 World Bank: Household spending per student on tuition for primary education (USD or local currency)
14413 World Bank: Household spending per student on uniforms/clothing for primary education (USD or local currency)
14414 World Bank: Share of education expenditures from international sources for primary education (%)
14423 World Bank: Public education expenditure per student (% of GDP per capita), primary
14435 World Bank: Share of primary education expenditures for Quintile 1 (poorest) (%)
14436 World Bank: Share of primary education expenditures for Quintile 2 (%)
14437 World Bank: Share of primary education expenditures for Quintile 3 (%)
14438 World Bank: Share of primary education expenditures for Quintile 4 (%)
14439 World Bank: Share of primary education expenditures for Quintile 5 (richest) (%)
14440 World Bank: Share of total primary education expenditures for non-poor students (%)
14441 World Bank: Share of education expenditures for the poorest 40% of students (%), Primary
14442 World Bank: Share of total primary education expenditures for poor students (%)
14443 World Bank: Share of education expenditures for the richest 40% of students (%), Primary
14447 World Bank: Share of total recurrent education expenditure (%), primary
14452 World Bank: Share of recurrent education expenditures for primary salaries (%)
14455 World Bank: Share of total education expenditures for primary salaries (%)
14461 World Bank: Share of total education expenditure for primary education (%)
14462 World Bank: Capital share of total education expenditure (%), primary
14463 World Bank: Total expenditures on primary education (USD or local currency)
14466 World Bank: Share of total government expenditures (all sectors) for primary education expenditures (%)
14468 World Bank: Share of total education expenditures for private primary schools (%)
14469 World Bank: Share of total education expenditures for private secondary schools (%)
14470 World Bank: Share of total education expenditures for private tertiary institutions (%)
14471 World Bank: Share of total household expenditures for education, rural areas (%)
14472 World Bank: Share of total household education expenditures from rural areas (%)
14475 World Bank: Average monthly education sector salary (in USD or local currency)
14490 World Bank: Share of total capital education expenditure (%), secondary
14497 World Bank: Capital education budget execution rate (%), secondary
14498 World Bank: Recurrent education budget execution rate (%), secondary
14500 World Bank: Education budget execution rate, secondary (%)
14501 World Bank: Share of GDP for education expenditures on secondary education (%)
14503 World Bank: Household spending per student on room/board for secondary education (USD or local currency)
14504 World Bank: Household spending per student on textbooks for secondary education (USD or local currency)
14505 World Bank: Household spending per student on transportation for secondary education (USD or local currency)
14506 World Bank: Household spending per student on tuition for secondary education (USD or local currency)
14507 World Bank: Household spending per student on uniforms/clothing for secondary education (USD or local currency)
14508 World Bank: Share of education expenditures from international sources for secondary education (%)
14517 World Bank: Public education expenditure per student (% of GDP per capita), secondary
14521 World Bank: Ratio of secondary per capita/per student recurrent expenditures relative to primary education
14530 World Bank: Share of total secondary education expenditures for non-poor students (%)
14531 World Bank: Share of education expenditures for the poorest 40% of students (%), Secondary
14532 World Bank: Share of total secondary education expenditures for poor students (%)
14533 World Bank: Share of secondary education expenditures for Quintile 1 (poorest) (%)
14534 World Bank: Share of secondary education expenditures for Quintile 2 (%)
14535 World Bank: Share of secondary education expenditures for Quintile 3 (%)
14536 World Bank: Share of secondary education expenditures for Quintile 4 (%)
14537 World Bank: Share of secondary education expenditures for Quintile 5 (richest) (%)
14538 World Bank: Share of education expenditures for the richest 40% of students (%), Secondary
14542 World Bank: Share of total recurrent education expenditure (%), secondary
14543 World Bank: Salaries' share of secondary education expenditures (%)
14547 World Bank: Share of recurrent education expenditures for secondary salaries (%)
14550 World Bank: Share of total education expenditures for secondary salaries (%)
14556 World Bank: Share of total education expenditure for secondary education (%)
14557 World Bank: Capital share of total education expenditure (%), secondary
14558 World Bank: Total expenditures on secondary education (USD or local currency)
14561 World Bank: Share of basic education expenditures financed by central government sources (%)
14562 World Bank: Share of basic education expenditures financed by district/municipal government sources (%)
14563 World Bank: Share of basic education expenditures financed by provincial/state government sources (%)
14564 World Bank: Share of total central government education expenditures for basic education (%)
14565 World Bank: Share of central government education expenditures for capital (%)
14566 World Bank: Central government share of education capital expenditures (%)
14567 World Bank: Share of total central government education expenditures for junior secondary (%)
14568 World Bank: Share of total central government education expenditures for pre-primary (%)
14569 World Bank: Share of total central government education expenditures for primary (%)
14570 World Bank: Central government share of recurrent education expenditures (%)
14571 World Bank: Share of central government education expenditures for recurrent expenditures (%)
14572 World Bank: Central government share of education salaries (%)
14573 World Bank: Share of central government education expenditures for salaries (%)
14574 World Bank: Share of total central government education expenditures for secondary (%)
14575 World Bank: Share of total central government education expenditures for senior secondary education (%)
14576 World Bank: Share of total central government education expenditures for tertiary (%)
14577 World Bank: Share of total central government spending for education (%)
14578 World Bank: Share of total education expenditures disbursed by the central government for basic education (%)
14579 World Bank: Share of total education expenditures disbursed by the central government for pre-primary (%)
14580 World Bank: Share of total education expenditures disbursed by the central government for primary (%)
14581 World Bank: Share of total education expenditures disbursed by the central government for secondary (%)
14582 World Bank: Share of total education expenditures disbursed by the central government for tertiary education (%)
14583 World Bank: Share of total education expenditures disbursed by the central government for technical/vocational education (%)
14584 World Bank: Share of total central government education expenditures for technical/vocational education (%)
14585 World Bank: Share of total education expenditures financed by district/municipal government sources (%)
14586 World Bank: Share of total education expenditures disbursed by subnational levels of government for basic education (%)
14587 World Bank: Share of total education expenditures disbursed by the central government (%)
14588 World Bank: Share of total education expenditures disbursed by subnational levels of government for junior secondary education (%)
14589 World Bank: Share of total education expenditures disbursed by district/municipal levels of government (%)
14590 World Bank: Share of total education expenditures disbursed by subnational levels of government for pre-primary (%)
14591 World Bank: Share of total education expenditures disbursed by subnational levels of government for primary (%)
14592 World Bank: Share of total education expenditures disbursed by provincial/state levels of government (%)
14593 World Bank: Share of total district/municipal government expenditures for the education sector (%)
14594 World Bank: Share of total education expenditures disbursed by subnational levels of government for secondary (%)
14595 World Bank: Share of total education expenditures disbursed by subnational levels of government for senior secondary education (%)
14596 World Bank: Share of total education expenditures disbursed by subnational levels of government (%)
14597 World Bank: Share of total education expenditures disbursed by subnational levels of government for tertiary education (%)
14598 World Bank: Share of total education expenditures disbursed by subnational levels of government for technical/vocational education (%)
14600 World Bank: Share of total junior secondary education expenditures from parental contributions/school fees (%)
14601 World Bank: Share of total pre-primary education expenditures from parental contributions/school fees (%)
14603 World Bank: Share of total expenditures on primary education recovered from school fees/household contributions (%)
14605 World Bank: Share of total expenditures on secondary education recovered from school fees/household expenditures (%)
14607 World Bank: Share of total senior secondary education expenditures from parental contributions/school fees (%)
14609 World Bank: Share of total cost of public tertiary education recovered from student fees (Cost Recovery %)
14610 World Bank: Share of total education revenue from school fees (%)
14612 World Bank: Total education expenditure from public sources (% of GDP)
14617 World Bank: Government expenditures per technical/vocational education student (in USD or local currency)
14620 World Bank: Share of total tertiary education expenditures (% from public sources)
14621 World Bank: Share of total education expenditure (% from public sources)
14622 World Bank: Share of total capital expenditure on education (% from public sources)
14628 World Bank: Total household spending on education (local currency)
14629 World Bank: Share of total Private/Household spending on primary education for poorest 20% of students (%)
14630 World Bank: Share of total Private/Household spending on secondary education for poorest 20% of students (%)
14631 World Bank: Share of total Private/Household spending on tertiary education for poorest 20% of students (%)
14632 World Bank: Private/Household spending on education, poorest 40% of students
14634 World Bank: Private/Household spending on education, Quintile 1
14635 World Bank: Share of total Private/Household spending on primary education for Quintile 1 (%)
14636 World Bank: Share of total Private/Household spending on secondary education for Quintile 1 (%)
14637 World Bank: Share of total Private/Household spending on tertiary education for Quintile 1 (%)
14638 World Bank: Private/Household spending on education, Quintile 2
14639 World Bank: Share of total Private/Household spending on primary education for Quintile 2 (%)
14640 World Bank: Share of total Private/Household spending on secondary education for Quintile 2 (%)
14641 World Bank: Share of total Private/Household spending on tertiary education for Quintile 2 (%)
14642 World Bank: Private/Household spending on education, Quintile 3
14643 World Bank: Share of total Private/Household spending on primary education for Quintile 3 (%)
14644 World Bank: Share of total Private/Household spending on secondary education for Quintile 3 (%)
14645 World Bank: Share of total Private/Household spending on tertiary education for Quintile 3 (%)
14646 World Bank: Private/Household spending on education, Quintile 4
14647 World Bank: Share of total Private/Household spending on primary education for Quintile 4 (%)
14648 World Bank: Share of total Private/Household spending on secondary education for Quintile 4 (%)
14649 World Bank: Share of total Private/Household spending on tertiary education for Quintile 4 (%)
14650 World Bank: Private/Household spending on education, Quintile 5
14651 World Bank: Share of total Private/Household spending on primary education for Quintile 5 (%)
14652 World Bank: Share of total Private/Household spending on secondary education for Quintile 5 (%)
14653 World Bank: Share of total Private/Household spending on tertiary education for Quintile 5 (%)
14654 World Bank: Share of total Private/Household spending on primary education for richest 20% of students (%)
14655 World Bank: Share of total Private/Household spending on secondary education for richest 20% of students (%)
14656 World Bank: Share of total Private/Household spending on tertiary education for richest 20% of students (%)
14657 World Bank: Private/Household spending on education, richest 40% of students
14661 World Bank: Share of total education expenditure (% from parent/household contributions)
14662 World Bank: Total household spending on education (USD)
14663 World Bank: Total household spending on education (USD), basic
14664 World Bank: Total household spending on education (USD or local currency), junior secondary
14665 World Bank: Total household spending on education (USD), pre-primary
14666 World Bank: Total household spending on education (USD or local currency), primary
14667 World Bank: Total household spending on education (USD or local currency), secondary
14668 World Bank: Total household spending on education (USD or local currency), senior secondary
14669 World Bank: Total household spending on education (USD), tertiary
14670 World Bank: Share of basic education expenditures from international sources (%)
14671 World Bank: Share of total capital education expenditure from international sources (%)
14672 World Bank: Total education expenditures from international sources as a % of GDP
14674 World Bank: Off-budget international education spending, total (in USD or local currency)
14681 World Bank: Share of total education expenditure from international sources (%)
14682 World Bank: Total education expenditures from international sources (in USD or local currency)
14684 World Bank: Share of pre-primary education expenditures financed by central government sources (%)
14685 World Bank: Share of pre-primary education expenditures financed by district/municipal government sources (%)
14686 World Bank: Share of pre-primary education expenditures financed by provincial/state government sources (%)
14687 World Bank: Share of primary education expenditures financed by central government sources (%)
14688 World Bank: Share of primary education expenditures financed by district/municipal government sources (%)
14689 World Bank: Share of primary education expenditures financed by provincial/state government sources (%)
14690 World Bank: Share of total education expenditures financed by provincial/state government sources (%)
14691 World Bank: Share of total provincial/state government expenditures for the education sector (%)
14692 World Bank: Secondary share of total private spending on education (%)
14693 World Bank: Total private expenditure on education (% of GDP)
14694 World Bank: Total private expenditure on pre-primary education (% of GDP)
14695 World Bank: Total private expenditure on primary education (% of GDP)
14696 World Bank: Total private expenditure on secondary education (% of GDP)
14697 World Bank: Total private expenditure on tertiary education (% of GDP)
14698 World Bank: Junior secondary share of total private spending on education (%)
14699 World Bank: Share of household consumption for private expenditures on education (%)
14700 World Bank: Share of household consumption for private expenditures on primary education (%)
14701 World Bank: Share of Quintile 1 household consumption for private expenditures on education (%)
14702 World Bank: Share of Quintile 2 household consumption for private expenditures on education (%)
14703 World Bank: Share of Quintile 3 household consumption for private expenditures on education (%)
14704 World Bank: Share of Quintile 4 household consumption for private expenditures on education (%)
14705 World Bank: Share of Quintile 5 household consumption for private expenditures on education (%)
14706 World Bank: Share of household consumption for private expenditures on secondary education (%)
14707 World Bank: Share of household consumption for private expenditures on tertiary education (%)
14708 World Bank: Pre-primary share of total private spending on education (%)
14709 World Bank: Primary share of total private spending on education (%)
14710 World Bank: Senior secondary share of total private spending on education (%)
14711 World Bank: Tertiary share of total private/household spending on education (%)
14712 World Bank: Share of total education expenditure (% from private sources)
14713 World Bank: Technical/Vocational share of total private/household spending on education (%)
14714 World Bank: Share of secondary education expenditures financed by central government sources (%)
14715 World Bank: Share of secondary education expenditures financed by district/municipal government sources (%)
14716 World Bank: Share of secondary education expenditures financed by provincial/state government sources (%)
14717 World Bank: Share of subnational education expenditures, basic (%)
14718 World Bank: Share of subnational education expenditures for capital (%)
14719 World Bank: Subnational government share of education capital expenditures (%)
14720 World Bank: Share of subnational education expenditures, pre-primary (%)
14721 World Bank: Share of subnational education expenditures, primary (%)
14722 World Bank: Subnational government share of recurrent education expenditures (%)
14723 World Bank: Share of subnational education expenditures for recurrent expenditures (%)
14724 World Bank: Share of total subnational government expenditures for the education sector (%)
14726 World Bank: Share of subnational education expenditures for salaries (%)
14727 World Bank: Share of subnational education expenditures, secondary (%)
14728 World Bank: Share of total education staff employed at the subnational level (%)
14729 World Bank: Share of subnational education expenditures, tertiary (%)
14730 World Bank: Share of subnational education expenditures, technical/vocational (%)
14731 World Bank: Share of tertiary education expenditures financed by central government sources (%)
14732 World Bank: Share of tertiary education expenditures financed by district/municipal government sources (%)
14733 World Bank: Share of tertiary education expenditures financed by provincial/state government sources (%)
14734 World Bank: Share of total education revenues from central sources (%)
14735 World Bank: Share of total education revenues from subnational sources (%)
14736 World Bank: Share of vocational education expenditures financed by central government sources (%)
14737 World Bank: Share of vocational education expenditures financed by district/municipal government sources (%)
14738 World Bank: Share of vocational education expenditures financed by provincial/state government sources (%)
14739 World Bank: Share of education expenditures from international sources for special education (%)
14740 World Bank: Average annual salary for special education teachers (in USD or local currency)
14741 World Bank: Share of special education expenditures from government sources (%)
14742 World Bank: Share of special education expenditures from international sources (%)
14745 World Bank: Share of total capital education expenditure (%), senior secondary
14749 World Bank: Share of education expenditures from international sources for senior secondary education (%)
14752 World Bank: Public education expenditure per student (% of GDP per capita), senior secondary
14759 World Bank: Share of senior secondary education expenditures for Quintile 1 (poorest) (%)
14760 World Bank: Share of senior secondary education expenditures for Quintile 2 (%)
14761 World Bank: Share of senior secondary education expenditures for Quintile 3 (%)
14762 World Bank: Share of senior secondary education expenditures for Quintile 4 (%)
14763 World Bank: Share of senior secondary education expenditures for Quintile 5 (richest) (%)
14764 World Bank: Share of education expenditures for the poorest 40% of students (%), Senior Secondary
14765 World Bank: Share of education expenditures for the richest 40% of students (%), Senior Secondary
14767 World Bank: Share of total recurrent education expenditure (%), senior secondary
14770 World Bank: Share of recurrent education expenditures for senior secondary salaries (%)
14772 World Bank: Share of total education expenditures for senior secondary salaries (%)
14774 World Bank: Share of senior education expenditures from government sources (%)
14776 World Bank: Share of teachers for senior secondary education (%)
14778 World Bank: Share of total education expenditure for senior secondary education (%)
14779 World Bank: Total expenditures on senior secondary education (USD or local currency)
14780 World Bank: Per student transfer by central to subnational government, senior secondary education (USD or local currency)
14782 World Bank: Share of district/municipal education expenditures for capital (%)
14783 World Bank: Share of district/municipal education expenditures for recurrent (%)
14784 World Bank: Share of district/municipal education expenditures for salaries (%)
14785 World Bank: Share of provincial/state education expenditures for capital (%)
14786 World Bank: Share of provincial/state education expenditures for recurrent expenditures (%)
14787 World Bank: Share of provincial/state education expenditures for salaries (%)
14788 World Bank: Recurrent expenditures per teacher education student (USD or local currency)
14789 World Bank: Salary expenditures per teacher education student (USD or local currency)
14792 World Bank: Share of total capital education expenditure (%), tertiary
14795 World Bank: Capital education budget execution rate (%), tertiary
14796 World Bank: Recurrent education budget execution rate (%), tertiary
14798 World Bank: Education budget execution rate, tertiary (%)
14801 World Bank: Share of GDP for education expenditures on tertiary education (%)
14803 World Bank: Household spending per student on room/board for tertiary education (USD or local currency)
14804 World Bank: Household spending per student on textbooks for tertiary education (USD or local currency)
14805 World Bank: Household spending per student on transportation for tertiary education (USD or local currency)
14806 World Bank: Household spending per student on uniforms/clothing for tertiary education (USD or local currency)
14807 World Bank: Share of education expenditures from international sources for tertiary education (%)
14809 World Bank: Public education expenditure per student (% of GDP per capita), tertiary
14810 World Bank: Per student non-salary spending, tertiary education (in USD or local currency)
14812 World Bank: Ratio of tertiary per capita/per student recurrent expenditures relative to primary education
14819 World Bank: Share of total tertiary education expenditures for non-poor students (%)
14820 World Bank: Share of education expenditures for the poorest 40% of students (%), Tertiary
14821 World Bank: Share of total tertiary education expenditures for poor students (%)
14822 World Bank: Share of tertiary education expenditures for Quintile 1 (poorest) (%)
14823 World Bank: Share of tertiary education expenditures for Quintile 2 (%)
14824 World Bank: Share of tertiary education expenditures for Quintile 3 (%)
14825 World Bank: Share of tertiary education expenditures for Quintile 4 (%)
14826 World Bank: Share of tertiary education expenditures for Quintile 5 (richest) (%)
14827 World Bank: Share of education expenditures for the richest 40% of students (%), Tertiary
14831 World Bank: Share of total recurrent education expenditure (%), tertiary
14832 World Bank: Salaries' share of tertiary education expenditures (%)
14835 World Bank: Share of recurrent education expenditures for tertiary salaries (%)
14838 World Bank: Share of total education expenditures for tertiary salaries (%)
14840 World Bank: Share of tertiary education expenditures for private institutions (%)
14841 World Bank: Share of teachers for tertiary education (%)
14845 World Bank: Share of total education expenditure for tertiary education (%)
14846 World Bank: Capital share of total education expenditure (%), tertiary
14847 World Bank: Total expenditures on tertiary education (USD or local currency)
14851 World Bank: Share of total capital education expenditures (%), teacher training
14853 World Bank: Share of education expenditures from international sources for teacher training education (%)
14856 World Bank: Public education expenditure per student (% of GDP per capita), teacher training
14859 World Bank: Share of teacher education expenditures from government sources (%)
14860 World Bank: Share of teacher education expenditures from international sources (%)
14861 World Bank: Share of total household expenditures for education, urban areas (%)
14862 World Bank: Share of total household education expenditures from urban areas (%)
14865 World Bank: Share of total capital education expenditure (%), technical/vocational
14866 World Bank: Capital education budget execution rate (%), technical/vocational
14867 World Bank: Recurrent education budget execution rate (%), technical/vocational
14868 World Bank: Education budget execution rate, technical/vocational (%)
14871 World Bank: Share of GDP for education expenditures on technical/vocational education (%)
14872 World Bank: Share of technical/vocational education recurrent expenditures for goods and services (%)
14873 World Bank: Household spending per student on technical/vocational education (in USD or local currency)
14874 World Bank: Pupil/Classroom Ratio, technical/vocational education
14875 World Bank: Pupils/Class, technical/vocational education
14876 World Bank: Public education expenditure per student (% of GDP per capita), technical/vocational education
14877 World Bank: Per student non-salary spending, technical/vocational education (in USD or local currency)
14879 World Bank: Expenditures per student per year (in USD/local currency), technical/vocational education
14880 World Bank: Pupil/Total Staff Ratio, technical/vocational education
14881 World Bank: Pupil/Teacher Ratio, technical/vocational education
14883 World Bank: Share of education expenditures for the poorest 40% of students (%), Technical/Vocational
14884 World Bank: Share of technical/vocational education expenditures for Quintile 1 (poorest) (%)
14885 World Bank: Share of technical/vocational education expenditures for Quintile 2 (%)
14886 World Bank: Share of technical/vocational education expenditures for Quintile 3 (%)
14887 World Bank: Share of technical/vocational education expenditures for Quintile 4 (%)
14888 World Bank: Share of technical/vocational education expenditures for Quintile 5 (richest) (%)
14889 World Bank: Share of education expenditures for the richest 40% of students (%), Technical/Vocational
14892 World Bank: Share of total recurrent education expenditure (%), technical/vocational
14893 World Bank: Salaries' share of technical/vocational education expenditures (%)
14894 World Bank: Share of recurrent education expenditures for technical/vocational salaries (%)
14897 World Bank: Annual Teacher Salaries, technical/vocational education (in USD or local currency)
14898 World Bank: Share of teachers for technical/vocational education (%)
14899 World Bank: Teaching/Non-Teaching Staff Ratio, technical/vocational education
14900 World Bank: Share of total education expenditure for technical/vocational education (%)
14901 World Bank: Capital share of total education expenditure (%), technical/vocational
14902 World Bank: Total expenditures on technical vocational education (USD or local currency)
14903 World Bank: Share of technical/vocational education recurrent expenditures for transfers (%)
17727 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Female
17728 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Male
17729 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Total
17730 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Female
17731 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Male
17732 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Total
17733 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Female
17734 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Male
17735 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Total
17736 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Female
17737 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Male
17738 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Total
17739 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Female
17740 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Male
17741 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Total
17742 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Female
17743 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Male
17744 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Total
17745 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Female
17746 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Male
17747 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Total
17748 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Female
17749 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Male
17750 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Total
17751 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Female
17752 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Male
17753 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Total
17754 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Female
17755 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Male
17756 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Total
17757 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Female
17758 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Male
17759 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Total
17760 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Female
17761 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Male
17762 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Total
17763 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Female
17764 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Male
17765 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Total
17766 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Female
17767 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Male
17768 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Total
17769 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Female
17770 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Male
17771 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Total
17772 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Female
17773 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Male
17774 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Total
17775 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Female
17776 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Male
17777 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Total
17778 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Female
17779 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Male
17780 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Total
17781 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Female
17782 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Male
17783 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Total
17784 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Female
17785 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Male
17786 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Total
17787 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Female
17788 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Male
17789 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Total
17790 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Female
17791 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Male
17792 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Total
17793 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Female
17794 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Male
17795 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Total
17796 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Female
17797 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Male
17798 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Total
17799 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Female
17800 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Male
17801 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Total
17802 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Female
17803 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Male
17804 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Total
17805 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Female
17806 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Male
17807 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Total
17808 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Female
17809 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Male
17810 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Total
17811 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Female
17812 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Male
17813 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Total
17814 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Female
17815 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Male
17816 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Total
17817 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Female
17818 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Male
17819 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Total
17820 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Female
17821 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Male
17822 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Total
17823 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Female
17824 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Male
17825 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Total
17826 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Female
17827 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Male
17828 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Total
17829 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Female
17830 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Male
17831 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Total
17832 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Female
17833 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Male
17834 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Total
17835 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Female
17836 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Male
17837 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Total
17838 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Female
17839 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Male
17840 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Total
17841 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Female
17842 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Male
17843 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Total
17844 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Female
17845 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Male
17846 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Total
17847 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Female
17848 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Male
17849 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Total
17850 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Female
17851 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Male
17852 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Total
17853 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Female
17854 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Male
17855 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Total
17856 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Female
17857 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Male
17858 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Total
17859 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Female
17860 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Male
17861 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Total
17862 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Female
17863 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Male
17864 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Total
17865 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Female
17866 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Male
17867 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Total
17868 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Female
17869 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Male
17870 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Total
17871 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Female
17872 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Male
17873 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Total
17874 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Female
17875 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Male
17876 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Total
17877 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Female
17878 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Male
17879 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Total
17880 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Female
17881 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Male
17882 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Total
17883 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Female
17884 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Male
17885 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Total
17886 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Female
17887 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Male
17888 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Total
17889 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Female
17890 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Male
17891 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Total
17892 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Female
17893 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Male
17894 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Total
17895 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Female
17896 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Male
17897 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Total
17898 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Female
17899 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Male
17900 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Total
17901 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Female
17902 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Male
17903 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Total
17904 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Female
17905 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Male
17906 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Total
17907 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Female
17908 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Male
17909 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Total
17910 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Female
17911 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Male
17912 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Total
17913 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Female
17914 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Male
17915 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Total
17916 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Female
17917 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Male
17918 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Total
17919 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Female
17920 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Male
17921 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Total
17922 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Female
17923 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Male
17924 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Total
17963 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Female
17964 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Male
17965 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Total
17966 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Female
17967 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Male
17968 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Total
17969 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Female
17970 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Male
17971 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Total
17972 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Female
17973 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Male
17974 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Total
17975 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Female
17976 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Male
17977 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Total
17978 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Female
17979 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Male
17980 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Total
17981 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Female
17982 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Male
17983 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Total
17984 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Female
17985 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Male
17986 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Total
17987 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Female
17988 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Male
17989 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Total
17990 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Female
17991 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Male
17992 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Total
17993 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Female
17994 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Male
17995 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Total
17996 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Female
17997 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Male
17998 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Total
17999 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Female
18000 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Male
18001 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Total
18002 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Female
18003 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Male
18004 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Total
18005 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Female
18006 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Male
18007 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Total
18008 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Female
18009 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Male
18010 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Total
18011 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Female
18012 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Male
18013 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Total
18014 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Female
18015 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Male
18016 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Total
18017 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Female
18018 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Male
18019 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Total
18020 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Female
18021 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Male
18022 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Total
18023 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Female
18024 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Male
18025 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Total
18026 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Female
18027 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Male
18028 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Total
18029 Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Female
18030 Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Male
18031 Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Total
18032 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Female
18033 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Male
18034 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Total
18229 SABER: (Education Management Information Systems) Policy Goal 1: Enabling Environment
18230 SABER: (Education Management Information Systems) Policy Goal 1 Lever 1: Legal Framework
18231 SABER: (Education Management Information Systems) Policy Goal 1 Lever 2: Organizational Structure
18232 SABER: (Education Management Information Systems) Policy Goal 1 Lever 3: Human Resources
18233 SABER: (Education Management Information Systems) Policy Goal 1 Lever 4: Infrastructural capacity
18234 SABER: (Education Management Information Systems) Policy Goal 1 Lever 5: Budget
18235 SABER: (Education Management Information Systems) Policy Goal 1 Lever 6: Data-driven Culture
18236 SABER: (Education Management Information Systems) Policy Goal 2: System Soundness
18237 SABER: (Education Management Information Systems) Policy Goal 2 Lever 1: Data Architecture
18238 SABER: (Education Management Information Systems) Policy Goal 2 Lever 2: Data Coverage
18239 SABER: (Education Management Information Systems) Policy Goal 2 Lever 3: Data Analytics
18240 SABER: (Education Management Information Systems) Policy Goal 2 Lever 4: Dynamic System
18241 SABER: (Education Management Information Systems) Policy Goal 2 Lever 5: Serviceability
18242 SABER: (Education Management Information Systems) Policy Goal 3: Quality data
18243 SABER: (Education Management Information Systems) Policy Goal 3 Lever 1: Methodological Soundness
18244 SABER: (Education Management Information Systems) Policy Goal 3 Lever 2: Accuracy and Reliability
18245 SABER: (Education Management Information Systems) Policy Goal 3 Lever 3: Integrity
18246 SABER: (Education Management Information Systems) Policy Goal 3 Lever 4: Periodicity and Timeliness
18247 SABER: (Education Management Information Systems) Policy Goal 4: Utilization in decision making
18248 SABER: (Education Management Information Systems) Policy Goal 4 Lever 1: Openness
18249 SABER: (Education Management Information Systems) Policy Goal 4 Lever 2: Operational Use
18250 SABER: (Education Management Information Systems) Policy Goal 4 Lever 3: Accessibility
18251 SABER: (Education Management Information Systems) Policy Goal 4 Lever 4: Effectiveness in Disseminating Findings
18300 SABER: (School Health and School Feeding) Health Policy Goal 4: Skills-Based Health Education
18354 SABER: (School Finance) Policy Goal 1 Lever 1: Are ther policies and systems set up to provide basic educational inputs to all?
18355 SABER: (School Finance) Policy Goal 1 Lever 2: Are there basic educational inputs for all primary school students?
18369 SABER: (School Finance) Policy Goal 6 Lever 1: Are there systems in place to verify the use of educational resources?
18370 SABER: (School Finance) Policy Goal 6 Lever 2: Are education expenditures audited?
18395 SABER: (Teachers) Policy Goal 3 Lever 1: Are there minimum standards for pre-service teaching education programs?
18401 SABER: (Teachers) Policy Goal 5 Lever 1: Does the education system invest in developing qualified school leaders?
18415 SABER: (Tertiary Education) Policy Goal 1: Vision for Tertiary Education
18416 SABER: (Tertiary Education) Policy Goal 1 Lever 1: Clear vision
18417 SABER: (Tertiary Education) Policy Goal 2: Regulatory Framework for Tertiary Education
18418 SABER: (Tertiary Education) Policy Goal 2 Lever 1: Steering the system
18419 SABER: (Tertiary Education) Policy Goal 3: Governance
18420 SABER: (Tertiary Education) Policy Goal 3 Lever 1: Articulation
18421 SABER: (Tertiary Education) Policy Goal 3 Lever 2: Institutional autonomy
18422 SABER: (Tertiary Education) Policy Goal 4: Finance
18423 SABER: (Tertiary Education) Policy Goal 4 Lever 1: Coverage of resource allocation
18424 SABER: (Tertiary Education) Policy Goal 4 Lever 2: Resource allocation
18425 SABER: (Tertiary Education) Policy Goal 4 Lever 3: Resource utilization (Equity)
18426 SABER: (Tertiary Education) Policy Goal 5: Quality Assurance
18427 SABER: (Tertiary Education) Policy Goal 5 Lever 1: Accreditation and Institutional Quality Standards
18428 SABER: (Tertiary Education) Policy Goal 5 Lever 2: Tertiary Education Management Information
18429 SABER: (Tertiary Education) Policy Goal 6: The Relevance of Tertiary Education for Economic and Social Needs
18430 SABER: (Tertiary Education) Policy Goal 6 Lever 1: Economic Development
18431 SABER: (Tertiary Education) Policy Goal 6 Lever 2: Fostering RDI and Innovation
18432 SABER: (Tertiary Education) Policy Goal 6 Lever 3: Fostering Social and Cultural Development and Environmental Protection and Sustainability
18555 Saved any money, primary education or less (% ages 15+)
18556 Saved any money, secondary education or more (% ages 15+)
20348 Compulsory education, duration (years)
20371 Preprimary education, duration (years)
20376 Trained teachers in preprimary education, female (% of female teachers)
20377 Trained teachers in preprimary education, male (% of male teachers)
20378 Trained teachers in preprimary education (% of total teachers)
20381 (Financing) - Does the country spend 4-5% of GDP or 15-20% of public expenditures on education spending?
20394 Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)
20395 Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)
20396 Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)
20397 Primary education, duration (years)
20399 Primary education, pupils
20400 Primary education, pupils (% female)
20406 Gross intake ratio in first grade of primary education, female (% of relevant age group)
20407 Gross intake ratio in first grade of primary education, male (% of relevant age group)
20408 Gross intake ratio in first grade of primary education, total (% of relevant age group)
20433 Trained teachers in primary education, female (% of female teachers)
20434 Trained teachers in primary education, male (% of male teachers)
20435 Trained teachers in primary education (% of total teachers)
20436 Primary education, teachers
20437 Primary education, teachers (% female)
20449 Education coefficient of efficiency (ideal years to graduate as % of actual)
20458 Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)
20459 Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)
20460 Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)
20461 Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)
20462 Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)
20463 Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)
20464 Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)
20465 Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)
20466 Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)
20467 Secondary education, duration (years)
20468 Secondary education, pupils
20470 Secondary education, pupils (% female)
20471 Secondary education, general pupils
20472 Secondary education, general pupils (% female)
20474 Share of male students in secondary education enrolled in vocational programmes (%)
20477 Secondary education, vocational pupils
20478 Secondary education, vocational pupils (% female)
20493 Trained teachers in secondary education, female (% of female teachers)
20494 Trained teachers in lower secondary education, female (% of female teachers)
20495 Trained teachers in lower secondary education, male (% of male teachers)
20496 Trained teachers in lower secondary education (% of total teachers)
20497 Trained teachers in secondary education, male (% of male teachers)
20498 Trained teachers in upper secondary education, female (% of female teachers)
20499 Trained teachers in upper secondary education, male (% of male teachers)
20500 Trained teachers in upper secondary education (% of total teachers)
20501 Trained teachers in secondary education (% of total teachers)
20502 Secondary education, teachers
20503 Secondary education, teachers, female
20504 Secondary education, teachers (% female)
20505 Secondary education, general teachers
20506 Secondary education, general teachers (% female)
20507 Secondary education, vocational teachers
20508 Secondary education, vocational teachers (% female)
20525 Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)
20526 Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)
20527 Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)
20528 Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)
20529 Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)
20530 Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)
20531 Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)
20532 Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)
20533 Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)
20534 Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)
20535 Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)
20536 Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)
20537 Percentage of students in tertiary education who are female (%)
20543 Female share of graduates in Education programmes, tertiary (%)
20553 Tertiary education, academic staff (% female)
20554 Current education expenditure, primary (% of total expenditure in primary public institutions)
20555 Current education expenditure, secondary (% of total expenditure in secondary public institutions)
20556 Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)
20557 Current education expenditure, total (% of total expenditure in public institutions)
20558 Public Expenditure on Education (% GDP)
20559 All education staff compensation, primary (% of total expenditure in primary public institutions)
20560 All education staff compensation, secondary (% of total expenditure in secondary public institutions)
20561 All education staff compensation, tertiary (% of total expenditure in tertiary public institutions)
20562 All education staff compensation, total (% of total expenditure in public institutions)
20563 Public spending on education, primary (% of GDP)
20565 Expenditure on primary education (% of government expenditure on education)
20567 Public spending on education, secondary (% of GDP)
20569 Expenditure on secondary education (% of government expenditure on education)
20571 Teachers' salaries (% of current education expenditure)
20572 Public spending on education, tertiary (% of GDP)
20574 Expenditure on tertiary education (% of government expenditure on education)
20575 Government expenditure on education, total (% of government expenditure)
20576 Government expenditure on education, total (% of GDP)
20577 Public spending on education, total (% of GNI, UNESCO)
21893 Labor force with advanced education, female (% of female working-age population with advanced education)
21894 Labor force with advanced education, male (% of male working-age population with advanced education)
21895 Labor force with advanced education (% of total working-age population with advanced education)
21896 Labor force with basic education, female (% of female working-age population with basic education)
21897 Labor force with basic education, male (% of male working-age population with basic education)
21898 Labor force with basic education (% of total working-age population with basic education)
21924 Labor force with intermediate education, female (% of female working-age population with intermediate education)
21925 Labor force with intermediate education, male (% of male working-age population with intermediate education)
21926 Labor force with intermediate education (% of total working-age population with intermediate education)
21931 Labor force with primary education, female (% of female labor force)
21932 Labor force with primary education, male (% of male labor force)
21933 Labor force with primary education (% of total)
21934 Labor force with secondary education, female (% of female labor force)
21935 Labor force with secondary education, male (% of male labor force)
21936 Labor force with secondary education (% of total)
21937 Labor force with tertiary education, female (% of female labor force)
21938 Labor force with tertiary education, male (% of male labor force)
21939 Labor force with tertiary education (% of total)
21955 Unemployment with advanced education, female (% of female labor force with advanced education)
21956 Unemployment with advanced education, male (% of male labor force with advanced education)
21957 Unemployment with advanced education (% of total labor force with advanced education)
21958 Unemployment with basic education, female (% of female labor force with basic education)
21959 Unemployment with basic education, male (% of male labor force with basic education)
21960 Unemployment with basic education (% of total labor force with basic education)
21961 Unemployment with intermediate education, female (% of female labor force with intermediate education)
21962 Unemployment with intermediate education, male (% of male labor force with intermediate education)
21963 Unemployment with intermediate education (% of total labor force with intermediate education)
21967 Share of youth not in education, employment or training, female (% of female youth population) (modeled ILO estimate)
21968 Share of youth not in education, employment or training, female (% of female youth population)
21969 Share of youth not in education, employment or training, male (% of male youth population) (modeled ILO estimate)
21970 Share of youth not in education, employment or training, male (% of male youth population)
21971 Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)
21972 Share of youth not in education, employment or training, total (% of youth population)
21973 Unemployment with primary education, female (% of female unemployment)
21974 Unemployment with primary education, male (% of male unemployment)
21975 Unemployment with primary education (% of total unemployment)
21976 Unemployment with secondary education, female (% of female unemployment)
21977 Unemployment with secondary education, male (% of male unemployment)
21978 Unemployment with secondary education (% of total unemployment)
21979 Unemployment with tertiary education, female (% of female unemployment)
21980 Unemployment with tertiary education, male (% of male unemployment)
21981 Unemployment with tertiary education (% of total unemployment)
22345 GOAL 4: Quality Education (5 year moving average)
22693 Percentage of students in lower secondary vocational education who are female (%)
22694 Percentage of students in upper secondary vocational education who are female (%)
22695 Percentage of students in post-secondary non-tertiary vocational education who are female (%)
22699 Share of female students in lower secondary education enrolled in vocational programmes (%)
22700 Share of male students in lower secondary education enrolled in vocational programmes (%)
22701 Share of female students in upper secondary education enrolled in vocational programmes (%)
22702 Share of male students in upper secondary education enrolled in vocational programmes (%)
22703 Share of female students in post-secondary non-tertiary education enrolled in vocational programmes (%)
22704 Share of male students in post-secondary non-tertiary education enrolled in vocational programmes (%)
22760 Account, primary education or less (% ages 15+) [ts]
22761 Account, secondary education or more (% ages 15+) [ts]# Notice that we're putting the text we're searching for in quotation marksWow! We got over 2,000 indicators with the term “education” in them! Clearly we need a better strategy. One thing we can do is use this text: .*, which will will have R search for the text before it followed anywhere in the description by the text afterwards. For example, to get information on the gender parity of school completion rates, we could do the following:
WDIsearch("completion.*gender") [1] indicator name
<0 rows> (or 0-length row.names)While that is a bit better, by this point it’s probably pretty clear that it would be easiest just to look the indicator codes up on the World Development Indicators website, which you can find at the link here. When you find an indicator you want, click on it, and then click on the “Details” button in the upper-right-hand corner of the graph that shows up. That will bring up a pop-up window where you can find the indicator’s ID code.
Let’s pick a few indicators and look at how we can download and process them:
# Define vector listing the World Development Indicators we want to retrieve.
indicators <- c(
"NY.GDP.PCAP.PP.KD", # GDP per capita, PPP (constant 2017 international $)
"SI.POV.GINI", # Gini index (income inequality)
"SE.ADT.LITR.ZS", # Adult literacy rate (percentage)
"SP.DYN.LE00.IN" # Life expectancy at birth (years)
)
# Retrieve WDI data for all countries from 2000 to 2020.
wdi_data <- WDI(
country = "all",
indicator = indicators,
start = 2000,
end = 2020,
extra = TRUE # Retrieves additional metadata about countries.
) # BE PATIENT - RUNNING THIS QUERY CAN TAKE A WHILE
# Rename columns for easier reference.
wdi_data <- wdi_data %>%
rename(
gdp_per_capita = NY.GDP.PCAP.PP.KD,
gini = SI.POV.GINI,
literacy = SE.ADT.LITR.ZS,
life_expectancy = SP.DYN.LE00.IN
) # this new function rename() takes the names of existing columns, on the
# right-hand side of the = sign, and gives them the new names, on the
# left-hand side
# Preview the first few rows of the WDI dataset.
head(wdi_data) country iso2c iso3c year status lastupdated gdp_per_capita gini literacy
1 Afghanistan AF AFG 2005 2025-07-01 1908.115 NA NA
2 Afghanistan AF AFG 2000 2025-07-01 1617.826 NA NA
3 Afghanistan AF AFG 2006 2025-07-01 1929.724 NA NA
4 Afghanistan AF AFG 2016 2025-07-01 2958.785 NA NA
5 Afghanistan AF AFG 2004 2025-07-01 1776.918 NA NA
6 Afghanistan AF AFG 2001 2025-07-01 1454.111 NA NA
life_expectancy region capital longitude latitude income lending
1 58.247 South Asia Kabul 69.1761 34.5228 Low income IDA
2 55.005 South Asia Kabul 69.1761 34.5228 Low income IDA
3 58.553 South Asia Kabul 69.1761 34.5228 Low income IDA
4 62.646 South Asia Kabul 69.1761 34.5228 Low income IDA
5 57.810 South Asia Kabul 69.1761 34.5228 Low income IDA
6 55.511 South Asia Kabul 69.1761 34.5228 Low income IDAQuestion 8. Create a new code block below, and then write code that will create a new data table, called wdi_data_2, that has a selection of three other WDIs of your choice. Be sure to rename those columns to something that will be easier to remember and understand.
Visualizing GDP per Capita
Now let’s have a look at how we can use histograms to visualize the distribution of continuous variables.
# Create a histogram of GDP per capita for the year 2019.
wdi_data %>%
filter(year == 2019 & !is.na(gdp_per_capita)) %>% # Filter for valid GDP data.
ggplot(aes(x = gdp_per_capita)) +
geom_histogram(fill = "steelblue", color = "white", bins = 30) +
# Add a dashed vertical line at the mean GDP per capita.
geom_vline(
aes(xintercept = mean(gdp_per_capita, na.rm = TRUE)),
color = "darkred", linetype = "dashed", linewidth = 1
) + # geom_vline creates a vertile like that crosses the x-axis at xintercept,
# which in this case is set to the mean of GDP per capita
scale_x_continuous(labels = scales::dollar_format()) +
# the above line tells ggplot() to label the x-axis in terms of US$
labs(
title = "Global Distribution of GDP per Capita (2019)",
subtitle = "Mean indicated by a dashed line",
x = "GDP per Capita (PPP, constant 2017 international $)",
y = "Number of Countries"
) +
theme_minimal()
Notice that we have a lot of countries with GDP per capita well below the mean, while we have a few with extremely high values. Sometimes it can be useful to view these kinds of variables on a logarithmic scale. In this case, we’ll use a base-10 scale, so we’ll see the values in terms of powers of ten:
# Create a histogram of GDP per capita for the year 2019.
wdi_data %>%
filter(year == 2019 & !is.na(gdp_per_capita)) %>% # Filter for valid GDP data.
ggplot(aes(x = gdp_per_capita)) +
geom_histogram(fill = "steelblue", color = "white", bins = 30) +
# Add a dashed vertical line at the mean GDP per capita.
geom_vline(
aes(xintercept = mean(gdp_per_capita, na.rm = TRUE)),
color = "darkred", linetype = "dashed", linewidth = 1
) +
scale_x_log10(labels = scales::dollar_format()) +
labs(
title = "Global Distribution of GDP per Capita (2019)",
subtitle = "Log scale with mean indicated by a dashed line",
x = "GDP per Capita (PPP, constant 2017 international $)",
y = "Number of Countries"
) +
theme_minimal()
Question 9. Create histograms of a variable that you added to your wdi_data_2 data table, with and without a logarithmic scale on the x-axis. Describe the patterns you see. What might be some advantages and disadvantages of using a logarithmic scale to display this indicator?
Now we’ll look at kernal density plots, which are pretty similar to histograms but try to draw the shape of the distribution as a smooth line, rather than approximating it by dividing the data into evenly spaced bins:
# Create a kernel density plot for life expectancy by region.
wdi_data %>%
filter(year == 2019 & !is.na(life_expectancy) & !is.na(region)) %>%
filter((!region %in% c("Aggregates")) & !is.na(region)) %>%
# Removing non-country aggregates (like EU or World) and countries that have not
# been assigned a region
ggplot(aes(x = life_expectancy, fill = region)) +
geom_density(alpha = 0.5) + # Semi-transparent density curves.
labs(
title = "Distribution of Life Expectancy by Region (2019)",
subtitle = "Kernel density estimates",
x = "Life Expectancy at Birth (years)",
y = "Density",
fill = "Region"
) +
theme_minimal() +
theme(legend.position = "bottom")
Explanation:
- geom_density() creates a smooth density curve for the life expectancy data. - The plot is split by region (using the fill aesthetic).
Question 10. Create a code block below and add in code that will create a kernal density plot for one of the variables in your wdi_data_2 object. Discuss the patterns that you find.
Combining Data from Multiple Country-Year Datasets
So far, we have just been looking at data tables from individual sources, like peacesciencer and WDI.
If we really want to do interesting analysis, though, we will often want to combine datasets from multiple datasets.
As you will remember from our discussions of peacesciencer, one straightforward way to do this is with a table join, which uses matching identifiers that are common across datasets to combine their data.
The problem with often run into with country-level data, however, is that country’s names are suprisingly unstandardized.
Different datasets may record full or abbreviated country names in English, in their native language, using alphabetic codes, using numeric codes, and so on. That makes joining different datasets challenging.
Fortunately, some folks have created the countrycode package, which is really just a large data table that allows us to translate the most commonly used country identifications in major international databases into each other.
That means we can use countrycode to combine data across a large range of databases.
Let’s start by learning how countrycode works.
Let’s say we wanted to see characteristics of democratic governance by world region.
We don’t have regional groupings in the state_data object, but we can use countrycode to translate between the Gleditsch-Ward country codes, which we have in the state_data that we got from peacesciencer and the regions defined by a variety of different organizations, such as the World Bank’s World Development Indictors, the United Nations, and the United Nations High Commissioner for Human Rights.
We’re also going to introduce a new function, mutate(), which we can use to add new columns to data tables in a pipeline.
This time, we’re going to use World Bank regions:
# Use countrycode to add regions
state_data_with_regions <- state_data %>%
mutate(
region = countrycode(
sourcevar = gwcode, # The column with country codes.
origin = "gwn", # Origin coding system. This stands for Gleditsch-Ward
# numeric
destination = "region" # Desired coding system.
) # So we're creating a new column called region in the tibble and
# filling it with the countries recoded into regions
)Warning: There was 1 warning in `mutate()`.
ℹ In argument: `region = countrycode(sourcevar = gwcode, origin = "gwn",
destination = "region")`.
Caused by warning:
! Some values were not matched unambiguously: 54, 55, 56, 57, 58, 60, 221, 223, 232, 331, 396, 397, 403, 591, 816, 935, 970, 971, 972, 973, 983, 986, 987, 990Notice that the above gives us a warning that not all the Gleditsch-Ward country codes could be correctly matched to World Development Indicator regions.
That is because different datasets will actually encode some countries, particularly semi-sovereign oversees territories, differently.
If we were wanting to act on the results of analysis like this, we would want to make sure and see what countries aren’t being matched, so we can take that into account when we interpret whatever we find:
state_data_with_regions$statename[state_data_with_regions$gwcode==816] %>% unique()Warning: Unknown or uninitialised column: `statename`.NULLQuestion 11. Explain what each component of the previous code block is doing. Then discuss why you think why we can’t match this particular country to the World Bank regions.
Now, we can use these regions to examine different variables across regions. Several ggplot2 geometries are good for these purposes.
We’ll look at boxplots, violin plots, kernel density plots, and histograms.
Let’s look at the distribution of the Polyarchy Index, the V-DEM dataset’s democratic summary score, across World Development Indicators regions:
state_data_with_regions %>%
ggplot(aes(y = region, x = v2x_polyarchy)) +
# notice that we set the region as the y-axis - we usually set the
# categorical variable as the x-axis when making boxplots, but b/c the
# category names are long, it is easier to read if we flip the plot on its
# side.
geom_boxplot() +
labs(
title = "V-Dem Polyarchy Index by Region (1970-2020)",
y = "WDI Region",
x = "V-Dem Polyarchy Index"
) +
theme_gray() # Another plot themeWarning: Removed 676 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Question 12. Create a code block below and write code that would allow you to make boxplots of two other variables in state_data_with_regions across regions. Write a short reflection on any notable differences that you find.
Question 13. Create a code block below and write code that will reproduce the V-Dem Polyarchy Index plot above as a violin plot. HINT: The function for creating violin plots is geom_violin.
Table Joining Datasets to World Bank Data
Now we’re reading to use the countrycode() function to help us join data from peacesciencer with data from the World Development Indicators (WDI) database.
Let’s pick a few indicators and look at how we can download and process them:
# Define vector listing the World Development Indicators we want to retrieve.
indicators <- c(
"NY.GDP.PCAP.PP.KD", # GDP per capita, PPP (constant 2017 international $)
"SI.POV.GINI", # Gini index (income inequality)
"SE.ADT.LITR.ZS", # Adult literacy rate (percentage)
"SP.DYN.LE00.IN" # Life expectancy at birth (years)
)
# Retrieve WDI data for all countries from 1970 to 2020.
wdi_data <- WDI(
country = "all",
indicator = indicators,
start = 1970,
end = 2020,
extra = TRUE # Retrieves additional metadata about countries.
) # BE PATIENT - RUNNING THIS QUERY CAN TAKE A WHILE
# Rename columns for easier reference.
wdi_data <- wdi_data %>%
rename(
gdp_per_capita = NY.GDP.PCAP.PP.KD,
gini = SI.POV.GINI,
literacy = SE.ADT.LITR.ZS,
life_expectancy = SP.DYN.LE00.IN
) # this new function rename() takes the names of existing columns, on the
# right-hand side of the = sign, and gives them the new names, on the
# left-hand side
# Preview the first few rows of the WDI dataset.
head(wdi_data) country iso2c iso3c year status lastupdated gdp_per_capita gini literacy
1 Afghanistan AF AFG 1982 2025-07-01 NA NA NA
2 Afghanistan AF AFG 1981 2025-07-01 NA NA NA
3 Afghanistan AF AFG 1983 2025-07-01 NA NA NA
4 Afghanistan AF AFG 2014 2025-07-01 3017.943 NA NA
5 Afghanistan AF AFG 1980 2025-07-01 NA NA NA
6 Afghanistan AF AFG 2011 2025-07-01 2757.053 NA 31
life_expectancy region capital longitude latitude income lending
1 36.058 South Asia Kabul 69.1761 34.5228 Low income IDA
2 39.406 South Asia Kabul 69.1761 34.5228 Low income IDA
3 36.517 South Asia Kabul 69.1761 34.5228 Low income IDA
4 62.260 South Asia Kabul 69.1761 34.5228 Low income IDA
5 39.258 South Asia Kabul 69.1761 34.5228 Low income IDA
6 61.250 South Asia Kabul 69.1761 34.5228 Low income IDAYou’ll notice that the table we created has a variety of country codes that we can use to identify particular countries, as well as the years for which each variable is available. However, the codes that the WDI uses are from the International Standards Organization (ISO), not the Gleditsch-Ward codes. That means we need to use countrycode() to help us generate a common column that we can use to conduct a table join, attaching the data from WDI to the data from peacesciencer:
plot_data <- state_data_with_regions %>%
mutate(
iso3c = countrycode(
sourcevar = gwcode,
origin = "gwn",
destination = "iso3c"
)
) %>%
left_join(wdi_data) # This is doing the table join; it will automatically try toWarning: There was 1 warning in `mutate()`.
ℹ In argument: `iso3c = countrycode(sourcevar = gwcode, origin = "gwn",
destination = "iso3c")`.
Caused by warning:
! Some values were not matched unambiguously: 54, 55, 56, 57, 58, 60, 221, 223, 232, 265, 315, 331, 345, 347, 396, 397, 403, 591, 678, 680, 816, 935, 970, 971, 972, 973, 983, 986, 987, 990Joining with `by = join_by(year, region, iso3c)` # join based on columns with common names. In this case, we
# have the "year" and "iso3c" columns, which have the same
# name in both datasets, so it will try to match up those
# columns in each dataset.
head(plot_data)# A tibble: 6 × 27
gwcode gw_name microstate year euds aeuds polity2 v2x_polyarchy ucdpongoing
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 United … 0 1990 1.76 0.948 10 0.866 0
2 2 United … 0 1991 1.76 0.948 10 0.865 0
3 2 United … 0 1992 1.94 1.13 10 0.869 0
4 2 United … 0 1993 1.95 1.15 10 0.868 0
5 2 United … 0 1994 1.89 1.08 10 0.868 0
6 2 United … 0 1995 1.89 1.08 10 0.868 0
# ℹ 18 more variables: ucdponset <dbl>, maxintensity <dbl>, conflict_ids <chr>,
# region <chr>, iso3c <chr>, country <chr>, iso2c <chr>, status <chr>,
# lastupdated <chr>, gdp_per_capita <dbl>, gini <dbl>, literacy <dbl>,
# life_expectancy <dbl>, capital <chr>, longitude <chr>, latitude <chr>,
# income <chr>, lending <chr>Question 14. Create two data visualizations using the combined dataset from peacesciencer and WDI. Discuss what your visualizations tell you that might be of interest.