MPI_demo

Simple Simulation

library(MPI)
library(kableExtra)

Loading Data

MPI::examplePovertydf is a simulation poverty data frame contains 16 indicators column which 1 means deprived and 0 means not deprived, and simulated forth-level administrative division of France.

data(examplePovertydf)
Access to health care Child Mortality Access to clean source of water Access to an improve sanatation Electricity Asset ownership Cooking Fuel Housing Materials School lag School attendance Years of education Region
1 0 1 0 1 1 1 0 0 0 1 Olmeto
0 0 1 1 0 0 0 0 1 1 1 Olmeto
1 0 1 1 1 1 1 0 0 0 0 Olmeto

Calculation

For calculating MPI using AF_Seq for sequential run or AF_Par for parallel run.

Input will be

  • df A poverty data frame
  • g A column name that will be used to divide data into groups. When the value is NULL, the entire data is not separated into groups.
  • w An indicator weight vectors
  • k A poverty cut-off. If an aggregate value of indicators of a specific person is above or equal the value of k, then this person is considered to be a poor.(default as 1)
out_seq <- AF_Seq(examplePovertydf, g = "Region", k = 3)

Output will be list of lists separated into group, and each list contains

  • groupname A Grouped value from column input g
## [1] "Bastia"
  • total Number of population in each group
## [1] 2
  • poors Number of deprived people in each group
## [1] 2
  • H Head count ratio, the proportion of the population that is multidimensionally deprived calculated by divide the number of poor people with the total number of people.
## [1] 1
  • A Average deprivation share among poor people, by aggregating the proportion of total deprivations each person and dividing by the total number of poor people.
## [1] 0.4090909
  • M0 Multidimensional Poverty Index, calculated by H times A.
## [1] 0.4090909
  • DimentionalContribution
    • indnames The poverty indicators
    • diCont Dimensional contributions denotes the magnitude of each indicator impacts on MPI.
    • UncensoredHCount Uncensored head count of indicator denotes the population that are deprived in that indicator.
    • UncensoredHRatio Uncensored head count ratio of indicator denotes the proportion of the population deprived in that indicator.
    • CensoredHCount Censored head count of indicator denotes the population that are multidimensionally poor and deprived in that indicator at the same time.
    • CensoredHRatio Censored head count ratio of indicator denotes the proportion that is multidimensionally poor and deprived in that indicator at the same time.
      indnames diCont UncensoredHCounts UncensoredHRatio CensoredHCounts CensoredHRatio
      Access.to.health.care 0.1111111 1 0.5 1 0.5
      Child.Mortality 0.1111111 1 0.5 1 0.5
      Access.to.clean.source.of.water 0.1111111 1 0.5 1 0.5
      Access.to.an.improve.sanatation 0.2222222 2 1.0 2 1.0
      Electricity 0.0000000 0 0.0 0 0.0
      Asset.ownership 0.2222222 2 1.0 2 1.0
      Cooking.Fuel 0.0000000 0 0.0 0 0.0
      Housing.Materials 0.1111111 1 0.5 1 0.5
      School.lag 0.0000000 0 0.0 0 0.0
      School.attendance 0.1111111 1 0.5 1 0.5
      Years.of.education 0.0000000 0 0.0 0 0.0
  • pov_df poverty data frame
    • Cvector is a vector of total values of deprived indicators adjusted by weight of indicators. Each element in Cvector represents a total value of each individual.
    • IsPoverty is a binary variable with only 1 and 0, with 1 indicating that person does not meet the threshold(poor person) and 0 indicating the opposite.
    • Intensity The intensity of a deprived indication among impoverished people is computed by dividing the number of deprived indicators by the total number of indicators.
      Access to health care Child Mortality Access to clean source of water Access to an improve sanatation Electricity Asset ownership Cooking Fuel Housing Materials School lag School attendance Years of education Cvector IsPoverty Intensity
      1 1 0 1 0 1 0 1 0 0 0 5 1 0.4545455
      0 0 1 1 0 1 0 0 0 1 0 4 1 0.3636364