This guide presents an illustration of a simple application of
mpindex package for computing Multidimensional Poverty
Index (MPI) using the Alkire-Foster (AF) counting method developed by
Sabina Alkire and James Foster.
1. Installation
To install the mpindex package from CRAN:
install.packages('mpindex')If you want to get the latest development version of
mpindex, install it from GitHub. Note that you may
need to install devtools.
# install.packages("devtools")
devtools::install_github('yng-me/mpindex')Load the package once you have successfully completed the installation.
2. MPI specifications
The initial step is to prepare an MPI specification file which will serve as references in the computation as well as generation of output in the later part of the process. This file should contain information about MPI dimensions, indicators and their corresponding weights.
This file should also be easy to create using the most common and
accessible file types such as .xlsx (Excel),
.json, .csv, or .txt (TSV).
Built-in specification files
For convenience, mpindex has included built-in
specification files (in different formats). Each file contains
dimensions, indicators, weight, and other relevant information of the
Global MPI.
To see the list of files available:
system.file("extdata", package = "mpindex") |> list.files()
#> [1] "global-mpi-specs.csv" "global-mpi-specs.json" "global-mpi-specs.txt"
#> [4] "global-mpi-specs.xlsx"To use a built-in specification file, say the .csv file,
use below script to first get the full path of the file.
specs_file <- system.file("extdata", "global-mpi-specs.csv", package = "mpindex")| Global MPI – Dimensions, Indicators, Deprivation Cutoffs, and Weights | ||||
| Dimension | Indicator | Variable | Weight | Description |
|---|---|---|---|---|
| Health | Nutrition | nutrition | 0.167 | Any person under 70 years of age for whom there is nutritional information is undernourished. |
| Health | Child mortality | child_mortality | 0.167 | A child under 18 has died in the household in the five-year period preceding the survey. |
| Education | Years of schooling | year_schooling | 0.167 | No eligible household member has completed six years of schooling. |
| Education | School attendance | school_attendance | 0.167 | Any school-aged child is not attending school up to the age at which he/she would complete class 8. |
| Living Standards | Cooking fuel | cooking_fuel | 0.056 | A household cooks using solid fuel, such as dung, agricultural crop, shrubs, wood, charcoal, or coal. |
| Living Standards | Sanitation | sanitation | 0.056 | The household has unimproved or no sanitation facility or it is improved but shared with other households. |
| Living Standards | Drinking water | drinking_water | 0.056 | The household's source of drinking water is not safe or safe drinking water is a 30-minute or longer walk from home, roundtrip. |
| Living Standards | Electricity | electricity | 0.056 | The household has no electricity. |
| Living Standards | Housing | housing | 0.056 | The household has inadequate housing materials in any of the three components: floor, roof, or walls. |
| Living Standards | Assets | assets | 0.056 | The household does not own more than one of these assets: radio, TV, telephone, computer, animal cart, bicycle, motorbike, or refrigerator, and does not own a car or truck. |
| Source: Alkire, S., Kanagaratnam, U. and Suppa, N. (2020). ‘The global Multidimensional Poverty Index (MPI): 2020 revision’, OPHI MPI Methodological Note 49, Oxford Poverty and Human Development Initiative, University of Oxford. | ||||
User-defined specification file
You can also define and create your own specification file if you prefer to or if you happen to use different sets of dimensions and indicators. At the minimum, however, this file should contain the following columns/variables:
-
Dimension, -
Indicator, -
Variable, -
Weight, -
Description(optional).
Note that the order in which you put these columns does not matter and also the names are not case sensitive, but make sure to spell the column names correctly.
You may download the template here if you do not want to start from scratch: MPI specification file sample.
Using define_mpi_specs
Once you have prepared the specification file, load it using the
define_mpi_specs function (type
?define_mpi_specs for a help text).
For demonstration purposes, we will use the built-in specification file, discussed above.
specs_file <- system.file("extdata", "global-mpi-specs.csv", package = "mpindex")
define_mpi_specs(specs_file)
#> # A tibble: 10 × 7
#> dimension indicator variable weight description variable_name label
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 Health Nutrition nutriti… 0.167 Any person… d01_i01_nutr… Heal…
#> 2 Health Child morta… child_m… 0.167 A child un… d01_i02_chil… Heal…
#> 3 Education Years of sc… year_sc… 0.167 No eligibl… d02_i01_year… Educ…
#> 4 Education School atte… school_… 0.167 Any school… d02_i02_scho… Educ…
#> 5 Living Standards Cooking fuel cooking… 0.0556 A househol… d03_i01_cook… Livi…
#> 6 Living Standards Sanitation sanitat… 0.0556 The househ… d03_i02_sani… Livi…
#> 7 Living Standards Drinking wa… drinkin… 0.0556 The househ… d03_i03_drin… Livi…
#> 8 Living Standards Electricity electri… 0.0556 The househ… d03_i04_elec… Livi…
#> 9 Living Standards Housing housing 0.0556 The househ… d03_i05_hous… Livi…
#> 10 Living Standards Assets assets 0.0556 The househ… d03_i06_asse… Livi…define_mpi_specs requires a specification file as its
first argument. The default poverty cutoff is set to 1/3
(based on Global MPI). You can also define a list of poverty cutoffs by
specifying in the .poverty_cutoffs argument to achieve
gradient MPIs.
define_mpi_specs(
.mpi_specs_file = specs_file,
.poverty_cutoffs = c(1/3, 0.2, 0.8)
)If your dataset contains unique ID, like uuid, it is
recommended to define it here using the .uid argument.
define_mpi_specs(
.mpi_specs_file = specs_file,
.uid = 'uuid'
)You can also set the aggregation level. Make sure it corresponds to
the column name present in your dataset (see ?df_househod
and more below).
define_mpi_specs(
.mpi_specs_file = specs_file,
.poverty_cutoffs = c(1/3, 0.2, 0.8),
.uid = 'uuid',
.aggregation = 'class'
).unit_of_analysis, .source_of_data, and
.names_separator are merely used for auto labels when
generating the output later.
Note: define_mpi_specs returns a data frame of MPI specs
defined in the specs file. By default, it saves a global option named
mpi_specs which can be accessed using
getOption('mpi_specs').
For our demonstration, we will use the
use_global_mpi_specs() wrapper function, which yields the
same result as calling define_mpi_specs and passing the
built-in specs file.
use_global_mpi_specs(
.uid = 'uuid',
.aggregation = 'class'
)3. Data preparation
The user of mpindex is assumed to have basic familiarity
with the concept of tidy data as well as able to perform data wrangling
and transformation using the tidyverse ecosystem. Under the
hood, mpindex uses dplyr verbs to perform data
manipulation.
We also assume that your dataset is already tidy and ready for analysis. See R for Data Science by Hadley Wickham and Garrett Grolemund if you need a refresher.
Dataset
For this demonstration, we will use two (2) synthetic datasets available within the package:
-
df_householdhousehold-level data (type?df_householdfor more info) -
df_household_rosterindividual-level data (type?df_household_rosterfor more info)
library(dplyr)
glimpse(df_household)
#> Rows: 198
#> Columns: 21
#> $ uuid <chr> "5dbec60a-ebda-47bd-ae18-3b017a221125", "8b70c208-8…
#> $ class <chr> "Rural", "Rural", "Rural", "Rural", "Rural", "Rural…
#> $ drinking_water <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ toilet <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 4, 1, 1, 1, …
#> $ with_child_died <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, …
#> $ roof <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ walls <int> 1, 1, 3, 3, 3, 5, 5, 1, 3, 1, 1, 4, 8, 2, 2, 10, 2,…
#> $ floor <int> 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 5, 2, 4, 1, 2, …
#> $ electricity <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, …
#> $ cooking_fuel <int> 3, 5, 5, 5, 5, 5, 5, 3, 3, 1, 3, 5, 5, 5, 5, 5, 5, …
#> $ asset_refrigerator <int> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, …
#> $ asset_radio <int> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, …
#> $ asset_tv <int> 1, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, …
#> $ asset_telephone <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ asset_mobile_phone <int> 4, 3, 2, 2, 5, 2, 1, 6, 2, 4, 1, 2, 3, 0, 0, 2, 0, …
#> $ asset_animal_cart <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ asset_computer <int> 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ asset_motorcycle <int> 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, …
#> $ asset_bicycle <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
#> $ asset_car <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ asset_truck <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
glimpse(df_household_roster)
#> Rows: 905
#> Columns: 8
#> $ uuid <chr> "5dbec60a-ebda-47bd-ae18-3b017a221125", "5dbe…
#> $ line_number <int> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 1, 2, 3, 4, 1, …
#> $ class <chr> "Rural", "Rural", "Rural", "Rural", "Rural", …
#> $ sex <chr> "Male", "Female", "Female", "Male", "Female",…
#> $ age <int> 55, 48, 17, 10, 30, 26, 3, 19, 66, 62, 34, 34…
#> $ attending_school <int> NA, NA, 1, 1, NA, NA, NA, 1, NA, NA, NA, NA, …
#> $ undernourished <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
#> $ completed_6yrs_schooling <int> 1, 1, 1, 2, 1, 1, NA, 2, 1, 1, 2, 1, 2, NA, 2…4. Creating deprivation profile
Using define_deprivation
First, we need to create an empty list, and name it
deprivation_profile (but feel free to name it whatever you
like).
deprivation_profile <- list()To create a deprivation profile for each indicator, we use the
define_deprivation function (see
?define_deprivation) and add to the
deprivation_profile list we created above. Make sure that
the deprivation profile for each indicator matches the variable name
declared in the specification file.
1. Heath dimension
1.1. Nutrition
For this indicator, we use the df_household_roster
dataset. By default, define_deprivation sets the
.collapse = FALSE. Since we need to collapse it to the
household level, we need to set .collapse = TRUE.
deprivation_profile$nutrition <- df_household_roster |>
define_deprivation(
.indicator = nutrition,
.cutoff = undernourished == 1 & age < 70,
.collapse = TRUE
)1.2. Child mortality
For child mortality, we use the df_household dataset.
But unlike in nutrition, we do not need to provide the
.collapse argument since it is not applicable here.
deprivation_profile$child_mortality <- df_household |>
define_deprivation(
.indicator = child_mortality,
.cutoff = with_child_died == 1
)2. Education dimension
2.1. Years of schooling
deprivation_profile$year_schooling <- df_household_roster |>
define_deprivation(
.indicator = year_schooling,
.cutoff = completed_6yrs_schooling == 2,
.collapse = TRUE
)2.2. School attendance
deprivation_profile$school_attendance <- df_household_roster |>
define_deprivation(
.indicator = school_attendance,
.cutoff = attending_school == 2 & age %in% c(5:24),
.collapse = TRUE
)3. Living standards dimension
3.1. Cooking fuel
deprivation_profile$cooking_fuel <- df_household |>
define_deprivation(
.indicator = cooking_fuel,
.cutoff = cooking_fuel %in% c(4:6, 9)
)3.2. Sanitation
deprivation_profile$sanitation <- df_household |>
define_deprivation(
.indicator = sanitation,
.cutoff = toilet > 1
)3.3. Drinking water
deprivation_profile$drinking_water <- df_household |>
define_deprivation(
.indicator = drinking_water,
.cutoff = drinking_water == 2
)3.4. Electricity
deprivation_profile$electricity <- df_household |>
define_deprivation(
.indicator = electricity,
.cutoff = electricity == 2
)3.6. Assets
For this indicator, we need additional transformation.
deprivation_profile$assets <- df_household |>
mutate_at(vars(starts_with('asset_')), ~ if_else(. > 0, 1L, 0L)) |>
mutate(
asset_phone = if_else(
(asset_telephone + asset_mobile_phone) > 0,
1L,
0L
)
) |>
mutate(
with_hh_conveniences = (
asset_tv + asset_phone + asset_computer +
asset_animal_cart + asset_bicycle +
asset_motorcycle + asset_refrigerator) > 1,
with_mobility_assets = (asset_car + asset_truck) > 0
) |>
define_deprivation(
.indicator = assets,
.cutoff = !(with_hh_conveniences & with_mobility_assets)
)5. Computing the MPI
Using compute_mpi
After completing the deprivation profile, use the
compute_mpi function and pass the
deprivation_profile list as the first argument.
mpi_result <- df_household |>
compute_mpi(deprivation_profile)
names(mpi_result)
#> [1] "index" "contribution" "headcount_ratio"
#> [4] "deprivation_matrix"Outputs
1. The MPI
mpi_result$index| MPI Results using 33% Poverty Cutoff | ||||
| Class | Number of cases | Headcount Ratio (H) | Intensity of Deprivation Among the Poor (A) | MPI (H x A) |
|---|---|---|---|---|
| Rural | 98 | 0.480 | 0.513 | 0.246 |
| Urban | 100 | 0.280 | 0.405 | 0.113 |
2. Contribution by dimension
mpi_result$contribution| Contribution by Dimenstion and Indicator to MPI using 33% Poverty Cutoff | |||||||||||
| Class | Number of cases | Health | Education | Living Standards | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets | ||
| Rural | 98 | 20.7 | 1.4 | 32.5 | 18.0 | 9.0 | 1.4 | 0.7 | 2.1 | 3.7 | 10.6 |
| Urban | 100 | 22.1 | 1.5 | 36.8 | 25.0 | 0.0 | 2.5 | 0.5 | 0.0 | 0.5 | 11.3 |
3. Headcount ratio
mpi_result$headcount_ratio$uncensored| Uncensored Headcount Ratio | |||||||||||
| Class | Number of cases | Health | Education | Living Standards | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets | ||
| Rural | 98 | 0.306 | 0.020 | 0.888 | 0.276 | 0.582 | 0.082 | 0.031 | 0.102 | 0.163 | 0.867 |
| Urban | 100 | 0.170 | 0.010 | 0.740 | 0.240 | 0.010 | 0.100 | 0.010 | 0.000 | 0.010 | 0.660 |
mpi_result$headcount_ratio$censored| Censored Headcount Ratio using 33% Poverty Cutoff | |||||||||||
| Class | Number of cases | Health | Education | Living Standards | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets | ||
| Rural | 98 | 0.306 | 0.020 | 0.480 | 0.265 | 0.398 | 0.061 | 0.031 | 0.092 | 0.163 | 0.469 |
| Urban | 100 | 0.150 | 0.010 | 0.250 | 0.170 | 0.000 | 0.050 | 0.010 | 0.000 | 0.010 | 0.230 |
4. Deprivation matrix (first 6 observations)
mpi_result$deprivation_matrix$uncensored |> head()| Uncensored Deprivation Matrix | ||||||||||||
| UUID | Class | Deprivation score | Health | Education | Living Standards | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets | |||
| 5dbec60a-ebda-47bd-ae18-3b017a221125 | Rural | 0.222 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 8b70c208-8642-408c-8a51-30bcaa106069 | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| aa7cb64d-ba16-4842-8994-8772060f432d | Rural | 0.111 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| df3e5c9b-7218-451d-9917-cd552cf4f40f | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 57babe6a-c163-4d8e-aa80-3a9bc9290d2d | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| ba3f75cd-102d-482d-a979-b9098e9e0e18 | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
mpi_result$deprivation_matrix$censored |> head()| Censored Deprivation Matrix using 33% Poverty Cutoff | ||||||||||||
| UUID | Class | Deprivation score | Health | Education | Living Standards | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets | |||
| 5dbec60a-ebda-47bd-ae18-3b017a221125 | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8b70c208-8642-408c-8a51-30bcaa106069 | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| aa7cb64d-ba16-4842-8994-8772060f432d | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| df3e5c9b-7218-451d-9917-cd552cf4f40f | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 57babe6a-c163-4d8e-aa80-3a9bc9290d2d | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ba3f75cd-102d-482d-a979-b9098e9e0e18 | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Note: Deprivation matrices are included by default when you run
compute_mpi. If you want to exclude these in your output,
set .include_deprivation_matrix equal to
FALSE.
6. Saving output
You may also save your output into an Excel file. You may choose to
format the output or retain its tidy format by setting the
formatted_output parameter appropriately.
Full script
# ----------------------------------
# Load MPI specs from the built-in specs file
use_global_mpi_specs(
.uid = 'uuid',
.aggregation = 'class'
)
# ----------------------------------
# Create an empty list to store deprivation profile for each indicator
deprivation_profile <- list()
deprivation_profile$nutrition <- df_household_roster |>
define_deprivation(
.indicator = nutrition,
.cutoff = undernourished == 1 & age < 70,
.collapse = TRUE
)
deprivation_profile$child_mortality <- df_household |>
define_deprivation(
.indicator = child_mortality,
.cutoff = with_child_died == 1
)
deprivation_profile$year_schooling <- df_household_roster |>
define_deprivation(
.indicator = year_schooling,
.cutoff = completed_6yrs_schooling == 2,
.collapse = TRUE
)
deprivation_profile$school_attendance <- df_household_roster |>
define_deprivation(
.indicator = school_attendance,
.cutoff = attending_school == 2 & age %in% c(5:24),
.collapse = TRUE
)
deprivation_profile$cooking_fuel <- df_household |>
define_deprivation(
.indicator = cooking_fuel,
.cutoff = cooking_fuel %in% c(4:6, 9)
)
deprivation_profile$sanitation <- df_household |>
define_deprivation(
.indicator = sanitation,
.cutoff = toilet > 1
)
deprivation_profile$drinking_water <- df_household |>
define_deprivation(
.indicator = drinking_water,
.cutoff = drinking_water == 2
)
deprivation_profile$electricity <- df_household |>
define_deprivation(
.indicator = electricity,
.cutoff = electricity == 2
)
deprivation_profile$housing <- df_household |>
define_deprivation(
.indicator = housing,
.cutoff = roof %in% c(5, 7, 9) |
walls %in% c(5, 8, 9, 99) == 2 |
floor %in% c(5, 6, 9)
)
deprivation_profile$assets <- df_household |>
dplyr::mutate_at(
dplyr::vars(dplyr::starts_with('asset_')),
~ dplyr::if_else(. > 0, 1L, 0L)
) |>
dplyr::mutate(
asset_phone = dplyr::if_else(
(asset_telephone + asset_mobile_phone) > 0,
1L,
0L
)
) |>
dplyr::mutate(
with_hh_conveniences = (
asset_tv + asset_phone + asset_computer +
asset_animal_cart + asset_bicycle +
asset_motorcycle + asset_refrigerator) > 1,
with_mobility_assets = (asset_car + asset_truck) > 0
) |>
define_deprivation(
.indicator = assets,
.cutoff = !(with_hh_conveniences & with_mobility_assets)
)
# ----------------------------------
# Compute the MPI
mpi_result <- df_household |>
compute_mpi(deprivation_profile)
# ----------------------------------
# You may also save your output into an Excel file
# Formatted output
# save_mpi(mpi_result, .filename = 'MPI Sample Output', .include_specs = T)
# Not formatted
save_mpi(
mpi_result,
.filename = 'MPI Sample Output (no format)',
.formatted_output = FALSE,
.include_specs = TRUE
)