Return main poverty and inequality statistics

Usage

pip_data(
  country = NULL,
  year = NULL,
  povline = NULL,
  popshare = NULL,
  fill_gaps = FALSE,
  nowcast = 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, or "all" for every available year or "MRV" for the most recent year. Default NULL, which returns every available year.
povline: (NULL | numeric(1))
Poverty line to be used to compute poverty measures, between 0 and 2700. Poverty lines are only accepted up to 3 decimals. Default NULL, which uses the international poverty line of the requested ppp_version, e.g. 3 for 2021 PPPs and 2.15 for 2017 PPPs.
popshare: (NULL | numeric(1))
Proportion of the population living below the poverty line, between 0 and 1. Takes precedence over povline: if both are supplied, the poverty line is derived from popshare. Default NULL.
fill_gaps: (logical(1))
Whether to fill gaps in the data. Default FALSE.
nowcast: (logical(1))
Whether to include nowcast estimates. Requires fill_gaps = TRUE. 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) | numeric(1))
Version of the data release in YYYYMMDD format. Default NULL.
ppp_version: (NULL | character(1) | numeric(1))
Version of the data in YYYY format. 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         SSF South Africa          ZAF           1993
#> 2 Sub-Saharan Africa         SSF South Africa          ZAF           2000
#> 3 Sub-Saharan Africa         SSF South Africa          ZAF           2005
#> 4 Sub-Saharan Africa         SSF South Africa          ZAF           2008
#> 5 Sub-Saharan Africa         SSF South Africa          ZAF           2010
#> 6 Sub-Saharan Africa         SSF 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            3 0.4461800  0.19319104
#> 2                    2             2000            3 0.4791000  0.21896628
#> 3                    3      2005 - 2022            3 0.4069980  0.15429069
#> 4                    3      2005 - 2022            3 0.3700637  0.14230019
#> 5                    3      2005 - 2022            3 0.2783952  0.09622671
#> 6                    3      2005 - 2022            3 0.2790018  0.09823253
#>   poverty_severity     watts      mean   median       mld      gini
#> 1       0.10267108 0.2835061  7.673708 3.507063 0.6366259 0.5933394
#> 2       0.12260432 0.3323840  6.555038 3.185121 0.5999924 0.5776966
#> 3       0.07731239 0.2236318 10.141722 3.736653 0.7832459 0.6500103
#> 4       0.07175632 0.2069866 10.465250 4.190742 0.7273998 0.6251803
#> 5       0.04551521 0.1360903 12.305911 5.260122 0.6841203 0.6087314
#> 6       0.04768261 0.1404713 11.572519 5.196361 0.6517375 0.5963908
#>   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    0.7533419 0.009981601 0.01578296 0.02081620 0.02612687 0.03292327
#> 4    0.7795235 0.009851315 0.01589892 0.02117398 0.02727510 0.03518300
#> 5    0.7398813 0.010337841 0.01679024 0.02268927 0.02915487 0.03760232
#> 6    0.7045509 0.010506335 0.01783890 0.02411633 0.03077615 0.03947990
#>      decile6    decile7    decile8   decile9  decile10       cpi      ppp
#> 1 0.05336964 0.07383648 0.10780574 0.1767219 0.4665975 0.2138148 7.524514
#> 2 0.05649406 0.07724597 0.11084411 0.1772000 0.4493053 0.3644379 7.524514
#> 3 0.04177270 0.05619889 0.08466862 0.1665420 0.5451869 0.4457968 7.524514
#> 4 0.04631718 0.06473768 0.09839545 0.1803587 0.5008087 0.5473402 7.524514
#> 5 0.04947219 0.06777731 0.10163055 0.1828016 0.4817438 0.6109178 7.524514
#> 6 0.05164180 0.07070396 0.10514788 0.1798380 0.4699508 0.7600676 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 3.054 0.4524100 10.726072            NA
#> 2          survey 3.000 0.4791000 12.006065            NA
#> 3          survey 3.168 0.4299225  9.742785            NA
#> 4          survey 3.395 0.4199089  9.177744            NA
#> 5          survey 3.930 0.3867875  7.435916            NA
#> 6          survey 3.898 0.3891268  7.564706            NA
# }

Return main poverty and inequality statistics

Usage

Source

Arguments

Value

See also

Examples