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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
)

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.

See also

Other poverty and inequality statistics: pip_aux(), pip_citation(), pip_group(), pip_health_check(), pip_info(), pip_valid_params(), pip_versions()

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
# }