BNPMIXcluster (version 1.1.0)

poverty.data: Poverty data for testing the BNPMIXcluster package

Description

Poverty indicators observed in Mexico for 2014.

The original data is available in the file "R_2014.zip" from CONEVAL's website: http://www.coneval.org.mx/Medicion/MP/Paginas/Programas_BD_10_12_14.aspx

(download zip file directly from: http://www.coneval.org.mx/Medicion/MP/Documents/Programas_calculo_pobreza_10_12_14/R_2014.zip )

This data frame presents indicators aggregated by household. The aggregation was done by the authors acording with code in section Examples.

Usage

poverty.data

Details

poverty.data is a data frame with 58121 rows and 13 variables, with the following columns:

proyecto

Data source identifier (1=MCS, 2=ENIGH)

folioviv

Household identifier level 1

foliohog

Household identifier level 2

ict_norm

(continuous) Total income in the household (in mexican pesos).

ic_ali

(binary) Indicator for deprivation to feeding: 1-yes,0-no

ic_asalud

(binary) Indicator for deprivation to health services: 1-yes,0-no

ic_cv

(binary) Housing quality: 1-bad, 0-good

ic_rezedu

(binary) Indicator for education backwardness: 1-yes,0-no

ic_sbv

(binary) Indicator for deprivation to basic public services: 1-yes,0-no

ic_segsoc

(binary) Indicator for deprivation to social security: 1-yes,0-no

niv_ed

(categorical, ordered) Maximum educational level in the household: 0-incomplete primary: 1-incomplete secondary, 2-complete secondary or more

tam_loc

(categorical, nominal) Size of locality according to the number of people living there: 1-(100000, \(\infty\)), 2-[15000, 100000), 3-[2500, 15000), 4-[0, 2500)

factor_hog

Expansion factor for the household, according to the complex survey design.

See Also

MIXclustering

Examples

# NOT RUN {
##### Generates poverty.data using the original data from CONEVAL's website #####

# }
# NOT RUN {
# step 1:
#     Download and unzip the file "R_2014.zip"
#     available in:
#     http://www.coneval.org.mx/Medicion/MP/Documents/Programas_calculo_pobreza_10_12_14/R_2014.zip

# step 2:
#     extract and read the csv file "pobreza_14.csv"

coneval.poverty.data <- read.csv("pobreza_14.csv", na.strings=c("NA",""))

# step 3:
#     Execute the following code...

var_id <- c("proyecto","folioviv","foliohog","numren")
for(i in match(var_id,colnames(coneval.poverty.data)) ){
     coneval.poverty.data[,i] <- formatC( x=as.numeric(coneval.poverty.data[,i]),
                                          width=max(nchar(coneval.poverty.data[,i])),
                                          format="f",flag="0",digits=0
                                          )
}

# normalizing the continuous variable for income #
b <- quantile(coneval.poverty.data$ict,probs=0.01)
coneval.poverty.data$ict_norm <- log(coneval.poverty.data$ict+b)

# Aggregating data at household level
Y_names <- c("ict_norm",
             "ic_ali","ic_asalud","ic_cv",
             "ic_rezedu","ic_sbv","ic_segsoc",
             "niv_ed","tam_loc")
agg_form <- as.formula( paste( "cbind(",paste(c(Y_names,"factor_hog"),collapse=",") ,")",
                               "~proyecto+folioviv+foliohog"
                             )
                      )

poverty.data <- aggregate(agg_form,FUN="max",data=coneval.poverty.data)

# }
# NOT RUN {
# }