Compute Multidimensional Poverty Index (MPI)

This function uses the Alkire-Foster (AF) counting method developed by Sabina Alkire and James Foster. It requires a deprivation profile created using the (define_deprivation) fuction containing all indicators defined in the specification files.

Usage

compute_mpi(
  .data,
  .deprivation_profile,
  ...,
  .mpi_specs = getOption("mpi_specs"),
  .include_deprivation_matrix = TRUE,
  .generate_output = FALSE,
  .formatted_output = TRUE,
  .mpi_output_filename = NULL,
  .include_table_summary = TRUE,
  .include_specs = FALSE
)

Arguments

.data: A tidy data frame where each observation is the unit of analysis defined in define_mpi_specs.
.deprivation_profile: list of deprivation profile created using define_deprivation.
...: Grouping columns (supports tidyselect), e.g. area (country, urbanity, region, province), sex, ethnic group, etc.
.mpi_specs: MPI specifications defined in define_mpi_specs.
.include_deprivation_matrix: Whether to include deprivation matrix in the output.
.generate_output: Whether to generate an output (Excel file) as side effect.
.formatted_output: NOT YET IMPLEMENTED. Whether formatting is to be applied to the output.
.mpi_output_filename: Output filename.
.include_table_summary: NOT YET IMPLEMENTED. Whether to include summary information in the generated output.
.include_specs: NOT YET IMPLEMENTED. Whether to include MPI specification in the generated output.

Value

Returns list of objects: index (the MPI), contribution (contribution by dimension), headcount_ratio (censored and uncensored), and deprivation_matrix (censored and uncensored). If poverty_cutoffs defined in define_mpi_specs contain more than one (1) value, index and contribution object will output each cutoff in a separate table.

References

Alkire-Foster Method
How to Apply the Alkire-Foster Method

Examples

# ----------------------------------
# Load MPI specs from the built-in specs file
specs_file <- system.file("extdata", "global-mpi-specs.csv", package = "mpindex")
mpi_specs <- define_mpi_specs(specs_file, .uid = 'uuid')

# ----------------------------------
# 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
if (FALSE) {
save_mpi(mpi_result, .filename = 'MPI Sample Output')
}