Return main poverty and inequality statistics

Usage

pip_data(
  country = NULL,
  year = NULL,
  povline = 2.15,
  popshare = NULL,
  fill_gaps = FALSE,
  welfare_type = c("all", "consumption", "income"),
  reporting_level = c("all", "national", "rural", "urban"),
  additional_ind = FALSE,
  release_version = NULL,
  ppp_version = NULL,
  version = NULL
)

Source

https://pip.worldbank.org/api

Arguments

country: (NULL | character())
Countries for which statistics are to be computed, specified as ISO3 codes. Default NULL.
year: (NULL | character() | numeric())
Years for which statistics are to be computed, specified as YYYY. Default NULL.
povline: (numeric(1))
Poverty line to be used to compute poverty mesures. Poverty lines are only accepted up to 3 decimals. Default 2.15.
popshare: (NULL | numeric(1))
Proportion of the population living below the poverty line. Will be ignored if povline is specified. Default NULL.
fill_gaps: (logical(1))
Whether to fill gaps in the data. Default FALSE.
welfare_type: (character(1))
Type of welfare measure to be used. Default "all".
reporting_level: (character(1))
level of reporting for the statistics. Default "all".
additional_ind: (logical(1))
Whether to include additional indicators. Default FALSE.
release_version: (NULL | character(1))
Version of the data release in YYYYMMDD format. Default NULL.
ppp_version: (NULL | character(1) | numeric(1))
Version of the data. Default NULL.
version: (NULL | character(1))
Version of the data. Default NULL.

Value

A data.frame() with the requested statistics.

Examples

# \donttest{
data <- pip_data(c("ZAF", "ZMB"))
head(data)
#>          region_name region_code country_name country_code reporting_year
#> 1 Sub-Saharan Africa         SSA South Africa          ZAF           1993
#> 2 Sub-Saharan Africa         SSA South Africa          ZAF           2000
#> 3 Sub-Saharan Africa         SSA South Africa          ZAF           2005
#> 4 Sub-Saharan Africa         SSA South Africa          ZAF           2008
#> 5 Sub-Saharan Africa         SSA South Africa          ZAF           2010
#> 6 Sub-Saharan Africa         SSA South Africa          ZAF           2014
#>   reporting_level survey_acronym survey_coverage survey_year welfare_type
#> 1        national           KIDS        national     1993.00  consumption
#> 2        national           HIES        national     2000.75  consumption
#> 3        national            IES        national     2005.00  consumption
#> 4        national            LCS        national     2008.67  consumption
#> 5        national            IES        national     2010.00  consumption
#> 6        national            LCS        national     2014.83  consumption
#>   survey_comparability comparable_spell poverty_line headcount poverty_gap
#> 1                    0             1993         2.15 0.3273500  0.11491448
#> 2                    2             2000         2.15 0.3602300  0.13788884
#> 3                    3      2005 - 2014         2.15 0.2738977  0.08866815
#> 4                    3      2005 - 2014         2.15 0.1784118  0.05158000
#> 5                    3      2005 - 2014         2.15 0.1736065  0.05210588
#> 6                    3      2005 - 2014         2.15 0.1983516  0.06567713
#>   poverty_severity      watts      mean   median       mld      gini
#> 1       0.05071058 0.15409808  7.683054 3.511334 0.6366259 0.5933394
#> 2       0.06619755 0.19199955  6.563024 3.189002 0.5999924 0.5776966
#> 3       0.04015528 0.12285010  9.755512 3.624245 0.7781536 0.6476189
#> 4       0.02152328 0.06903565 12.303904 4.862677 0.7351245 0.6300900
#> 5       0.02242921 0.07096926 12.981507 5.088423 0.7526440 0.6338256
#> 6       0.03093106 0.09252674 12.214114 4.861294 0.7502294 0.6302572
#>   polarization     decile1    decile2    decile3    decile4    decile5
#> 1    0.7147437 0.012500158 0.01696966 0.02260853 0.02990889 0.03968160
#> 2    0.6770340 0.012816826 0.01782763 0.02403983 0.03193381 0.04229243
#> 3    0.7549428 0.009988367 0.01586569 0.02091390 0.02623093 0.03312265
#> 4    0.7609605 0.010057582 0.01602856 0.02129058 0.02735578 0.03503969
#> 5    0.7837881 0.009384293 0.01530752 0.02053135 0.02663764 0.03442381
#> 6    0.7876801 0.008574058 0.01507192 0.02074483 0.02708842 0.03505580
#>      decile6    decile7    decile8   decile9  decile10       cpi      ppp
#> 1 0.05336964 0.07383648 0.10780574 0.1767219 0.4665975 0.2135547 7.524514
#> 2 0.05649406 0.07724597 0.11084411 0.1772000 0.4493053 0.3639944 7.524514
#> 3 0.04201133 0.05651407 0.08522587 0.1679377 0.5421895 0.4452543 7.524514
#> 4 0.04551465 0.06298532 0.09485078 0.1743129 0.5125641 0.5474131 7.524514
#> 5 0.04529464 0.06279666 0.09618902 0.1768472 0.5125879 0.6110209 7.524514
#> 6 0.04650064 0.06531549 0.09957486 0.1771185 0.5049555 0.7607006 7.524514
#>   reporting_pop reporting_gdp reporting_pce is_interpolated distribution_type
#> 1      43297156      4193.598      2372.839           FALSE             group
#> 2      47465030      4700.876      2790.873           FALSE             group
#> 3      49490033      5406.076      3292.593           FALSE             micro
#> 4      51525923      6009.720      3738.485           FALSE             micro
#> 5      52344051      5953.945      3758.295           FALSE             micro
#> 6      56531658      6155.006      3887.509           FALSE             micro
#>   estimation_type   spl       spr        pg estimate_type
#> 1          survey 3.056 0.4522100 10.713024            NA
#> 2          survey 3.000 0.4786800 11.991457            NA
#> 3          survey 3.112 0.4359328 10.127501            NA
#> 4          survey 3.731 0.3929107  7.772644            NA
#> 5          survey 3.844 0.3938811  7.727838            NA
#> 6          survey 3.731 0.3995142  8.395812            NA
# }

Return main poverty and inequality statistics

Usage

Source

Arguments

Value

See also

Examples