1 No Poverty

End poverty in all its forms everywhere

1.1 Original Data

Setup Code

library(tidyverse)
theme_set(theme_bw())

pip <- read_csv("data/01/pip.csv", show_col_types = FALSE)

Source: World Bank (2025), Poverty and Inequality Platform (version 20250401_2021_01_02_PROD) [data set]. pip.worldbank.org. Accessed on 2025-08-05

Download all country data from https://pip.worldbank.org/poverty-calculator

pip.csv

Column names are below, with my best guess at the meaning for some columns. You can learn more at the methodology handbook.

Codebook

region_name
region_code: “SSA” “ECA” “OHI” “LAC” “SAS” “EAP” “MNA”
country_name
country_code: 3-letter country code
reporting_year: 1963 - 2024
reporting_level: “national”, “urban”, or “rural”
survey_acronym
survey_coverage
survey_year
welfare_type
survey_comparability
comparable_spell
poverty_line
headcount: The poverty headcount ratio measures the proportion of the population that is counted as poor
poverty_gap: The poverty gap index is a measure that adds up the extent to which individuals on average fall below the poverty line (i.e. the depth of poverty), and expresses it as a percentage of the poverty line.
poverty_severity: The poverty severity index is a measure of the weighted sum of poverty gaps (as a proportion of the poverty line), where the weights are the proportionate poverty gaps themselves.
watts: The Watts index is an inequality-sensitive poverty measure
mean: Average welfare per capita
median: The median is the amount of welfare per capita that divides the distribution into two equal halves.
mld: The mean log deviation belongs to the family of generalized entropy inequality measures
gini: The Gini index ranges from 0 (perfect equality) to 1 (complete inequality)
polarization: The Wolfson polarization index measures the extent to which the distribution of welfare is “spread out” and bi-modal.
decile1:decile10
cpi: Consumer Price Index
ppp: Purchasing Power Parity
reporting_pop
reporting_gdp
reporting_pce
is_interpolated
distribution_type
estimation_type
spl
spr
pg: The prosperity gap is the average factor by which incomes fall short of a prosperity standard of $28 per person per day (expressed in 2021 PPP dollars)
estimate_type

Plot Code

count(pip, region_name, country_name, reporting_year) |>
  count(region_name, reporting_year) |>
  ggplot(aes(y = fct_rev(region_name), x = reporting_year, fill = n)) +
  geom_tile() +
  scale_fill_viridis_c(limits = c(0, 30),
    guide = guide_colourbar(
      position = "inside", 
      nbin = 4, 
      display = "rectangles", 
      draw.ulim = TRUE
    )) +
  scale_x_continuous(breaks = seq(1960, 2030, 10)) +
  labs(x = NULL,
       y = NULL,
       fill = NULL) +
  theme(legend.position.inside = c(.06, .7),
        legend.key.height = unit(0.4, "cm"),
        legend.background = element_blank())

Figure 1.1: Number of countries per region per year in the dataset

1.2 Simplified Subsets

1.2.1 GINI 2010

This data set provides the GINI index for each country, at the national reporting_level, in the reporting_year 2010. Where there is more than one survery per country, we choose the first.

This data set is good for distribution plots and descriptive analyses, grouped by world region.

Code to create pip_gini_2010

pip_gini_2010 <- pip |>
  filter(reporting_year == 2010, 
         reporting_level == "national") |>
  filter(n() == 1, .by = country_name) |>
  select(region_name:reporting_year, reporting_level, gini)

pip_gini_2010.csv

Table 1.1: The first rows of pip_gini_2010

region_name	region_code	country_name	country_code	reporting_year	reporting_level	gini
Europe & Central Asia	ECA	Armenia	ARM	2010	national	0.2999258
Other High Income Countries	OHI	Australia	AUS	2010	national	0.3465673
Other High Income Countries	OHI	Austria	AUT	2010	national	0.3025166
Other High Income Countries	OHI	Belgium	BEL	2010	national	0.2837483
South Asia	SAS	Bangladesh	BGD	2010	national	0.3213032
Europe & Central Asia	ECA	Bulgaria	BGR	2010	national	0.3565347

Plot Code

ggplot(pip_gini_2010, aes(x = fct_rev(region_name), 
                          y = gini, 
                          fill = region_name)) +
  geom_violin(alpha = 0.5, 
              bounds = c(0, 1), 
              scale = "width",
              show.legend = FALSE) +
  scale_y_continuous(breaks = seq(0, 1, .1)) +
  labs(x = NULL, y = "GINI Index") +
  coord_flip()

Figure 1.2: The distribution of wealth inequality across the world regions in 2010

1.2.2 Welfare 2010

This data file provides the mean and median welfare per capita for all countries, at the national reporting_level, in the reporting_year 2010. Where there is more than one survey per country, we choose the first.

In combination with the Gini set above, it is good for teaching about data joining. The resulting set is good for plotting the relationship between two continuous variables and related descriptive and inferential statistics.

Code to create pip_welfare_2010

pip_welfare_2010 <- pip |>
  filter(reporting_year == 2010, 
         reporting_level == "national") |>
  filter(n() == 1, .by = country_name) |>
  select(region_name:reporting_year, reporting_level, mean, median)

pip_welfare_2010.csv

Table 1.2: The first rows of pip_welfare_2010

region_name	region_code	country_name	country_code	reporting_year	reporting_level	mean	median
Europe & Central Asia	ECA	Armenia	ARM	2010	national	8.182075	6.806526
Other High Income Countries	OHI	Australia	AUS	2010	national	66.453958	53.316423
Other High Income Countries	OHI	Austria	AUT	2010	national	75.037370	65.371284
Other High Income Countries	OHI	Belgium	BEL	2010	national	63.008029	55.914805
South Asia	SAS	Bangladesh	BGD	2010	national	5.127794	4.074886
Europe & Central Asia	ECA	Bulgaria	BGR	2010	national	19.126000	16.019157

Plot Code

inner_join(pip_gini_2010, pip_welfare_2010, 
           by = c("region_name", "region_code", "country_name", "country_code", "reporting_year", "reporting_level")) |>
  ggplot(aes(x = gini, y = median, colour = region_name)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = lm, formula = y ~ x, se = F) +
  scale_x_continuous(breaks = seq(0, 1, .1)) +
  labs(x = "GINI Index", y = "Median Welfare per Capita", colour = NULL) +
  scale_color_discrete(
    guide = guide_legend(position = "inside")
  ) +
  theme(
    legend.position.inside = c(.8, .75),
    legend.background = element_blank(),
    legend.key = element_blank()
  )

Figure 1.3: The relationship between the Gini index and median welfare per captia across world regions in 2010

1.2.3 Gini for USA, UK and China

These three data files provide all of the available years of the GINI index for China, USA, and the UK (countries with many years of data).

This set is good for teaching about data merging and reshaping.

Code to create pip_gini_china, pip_gini_usa, pip_gini_uk

gini <- pip |>
  filter(reporting_level == "national") |>
  select(region_name:reporting_year, reporting_level, gini)

pip_gini_usa <- filter(gini, country_code == "USA")
pip_gini_china <- filter(gini, country_name == "China")
pip_gini_uk <- filter(gini, country_name == "United Kingdom")

Table 1.3: The first rows of pip_gini_china

region_name	region_code	country_name	country_code	reporting_year	reporting_level	gini
East Asia & Pacific	EAP	China	CHN	1981	national	0.2816410
East Asia & Pacific	EAP	China	CHN	1984	national	0.2710230
East Asia & Pacific	EAP	China	CHN	1987	national	0.2938596
East Asia & Pacific	EAP	China	CHN	1990	national	0.3222662
East Asia & Pacific	EAP	China	CHN	1993	national	0.3387780
East Asia & Pacific	EAP	China	CHN	1996	national	0.3522766

Plot Code

bind_rows(pip_gini_china, pip_gini_uk, pip_gini_usa) |>
  ggplot(aes(x = reporting_year, y = gini, color = country_name)) +
  geom_point(alpha = 0.5) +
  geom_line(show.legend = FALSE) +
  scale_x_continuous(breaks = seq(1960, 2030, 10)) +
  scale_color_manual(
    values = c("#E5243B", "#4C9F38", "#19486A"), 
    guide = guide_legend(position = "inside")
  ) +
  labs(x = NULL, y = "Gini Index", color = NULL) +
  theme(
    legend.position.inside = c(.15, .85),
    legend.background = element_blank(),
    legend.key = element_blank()
  )

Figure 1.4: The change in Gini index across time for China, USA and UK

1.3 Resources

# No Poverty {#sec-sdg01} ![](images/icons/E-WEB-Goal-01.png){.sdg-icon} ::: {.tagline .sdg01} End poverty in all its forms everywhere ::: ## Original Data ```{r} #| code-summary: Setup Code library(tidyverse) theme_set(theme_bw()) pip <- read_csv("data/01/pip.csv", show_col_types = FALSE) ``` Source: World Bank (2025), Poverty and Inequality Platform (version 20250401_2021_01_02_PROD) [data set]. pip.worldbank.org. Accessed on 2025-08-05 Download all country data from <https://pip.worldbank.org/poverty-calculator> - [pip.csv](data/01/pip.csv){download=""} Column names are below, with my best guess at the meaning for some columns. You can learn more at the [methodology handbook](https://datanalytics.worldbank.org/PIP-Methodology/). ::: {.callout-tip title="Codebook" collapse="true"} - region_name - region_code: "SSA" "ECA" "OHI" "LAC" "SAS" "EAP" "MNA" - country_name - country_code: 3-letter country code - reporting_year: 1963 - 2024 - reporting_level: "national", "urban", or "rural" - survey_acronym - survey_coverage - survey_year - welfare_type - survey_comparability - comparable_spell - poverty_line - headcount: The poverty headcount ratio measures the proportion of the population that is counted as poor - poverty_gap: The poverty gap index is a measure that adds up the extent to which individuals on average fall below the poverty line (i.e. the depth of poverty), and expresses it as a percentage of the poverty line. - poverty_severity: The poverty severity index is a measure of the weighted sum of poverty gaps (as a proportion of the poverty line), where the weights are the proportionate poverty gaps themselves. - watts: The Watts index is an inequality-sensitive poverty measure - mean: Average welfare per capita - median: The median is the amount of welfare per capita that divides the distribution into two equal halves. - mld: The mean log deviation belongs to the family of generalized entropy inequality measures - gini: The Gini index ranges from 0 (perfect equality) to 1 (complete inequality) - polarization: The Wolfson polarization index measures the extent to which the distribution of welfare is “spread out” and bi-modal. - decile1:decile10 - cpi: Consumer Price Index - ppp: Purchasing Power Parity - reporting_pop - reporting_gdp - reporting_pce - is_interpolated - distribution_type - estimation_type - spl - spr - pg: The prosperity gap is the average factor by which incomes fall short of a prosperity standard of $28 per person per day (expressed in 2021 PPP dollars) - estimate_type ::: ```{r} #| label: fig-pip-region-year #| fig-cap: Number of countries per region per year in the dataset #| fig-width: 8 #| fig-height: 2 #| code-summary: Plot Code count(pip, region_name, country_name, reporting_year) |> count(region_name, reporting_year) |> ggplot(aes(y = fct_rev(region_name), x = reporting_year, fill = n)) + geom_tile() + scale_fill_viridis_c(limits = c(0, 30), guide = guide_colourbar( position = "inside", nbin = 4, display = "rectangles", draw.ulim = TRUE )) + scale_x_continuous(breaks = seq(1960, 2030, 10)) + labs(x = NULL, y = NULL, fill = NULL) + theme(legend.position.inside = c(.06, .7), legend.key.height = unit(0.4, "cm"), legend.background = element_blank()) ``` ## Simplified Subsets ### GINI 2010 This data set provides the GINI index for each country, at the national *reporting_level*, in the *reporting_year* 2010. Where there is more than one survery per country, we choose the first. This data set is good for distribution plots and descriptive analyses, grouped by world region. ```{r} #| code-summary: Code to create pip_gini_2010 pip_gini_2010 <- pip |> filter(reporting_year == 2010, reporting_level == "national") |> filter(n() == 1, .by = country_name) |> select(region_name:reporting_year, reporting_level, gini) ``` ```{r} #| echo: false write_csv(pip_gini_2010, "data/01/pip_gini_2010.csv") ``` - [pip_gini_2010.csv](data/01/pip_gini_2010.csv){download=""} ```{r} #| echo: false #| label: tbl-pip_gini_2010 #| tbl-cap: The first rows of pip_gini_2010 head(pip_gini_2010) ``` ```{r} #| label: fig-gini-region-2010 #| fig-cap: The distribution of wealth inequality across the world regions in 2010 #| code-summary: Plot Code ggplot(pip_gini_2010, aes(x = fct_rev(region_name), y = gini, fill = region_name)) + geom_violin(alpha = 0.5, bounds = c(0, 1), scale = "width", show.legend = FALSE) + scale_y_continuous(breaks = seq(0, 1, .1)) + labs(x = NULL, y = "GINI Index") + coord_flip() ``` ### Welfare 2010 This data file provides the mean and median welfare per capita for all countries, at the national *reporting_level*, in the *reporting_year* 2010. Where there is more than one survey per country, we choose the first. In combination with the Gini set above, it is good for teaching about data joining. The resulting set is good for plotting the relationship between two continuous variables and related descriptive and inferential statistics. ```{r} #| code-summary: Code to create pip_welfare_2010 pip_welfare_2010 <- pip |> filter(reporting_year == 2010, reporting_level == "national") |> filter(n() == 1, .by = country_name) |> select(region_name:reporting_year, reporting_level, mean, median) ``` ```{r} #| echo: false write_csv(pip_welfare_2010, "data/01/pip_welfare_2010.csv") ``` - [pip_welfare_2010.csv](data/01/pip_welfare_2010.csv){download=""} ```{r} #| echo: false #| label: tbl-pip_welfare_2010 #| tbl-cap: The first rows of pip_welfare_2010 head(pip_welfare_2010) ``` ```{r} #| label: fig-gini-median-2010 #| fig.cap: The relationship between the Gini index and median welfare per captia across world regions in 2010 #| code-summary: Plot Code inner_join(pip_gini_2010, pip_welfare_2010, by = c("region_name", "region_code", "country_name", "country_code", "reporting_year", "reporting_level")) |> ggplot(aes(x = gini, y = median, colour = region_name)) + geom_point(alpha = 0.5) + geom_smooth(method = lm, formula = y ~ x, se = F) + scale_x_continuous(breaks = seq(0, 1, .1)) + labs(x = "GINI Index", y = "Median Welfare per Capita", colour = NULL) + scale_color_discrete( guide = guide_legend(position = "inside") ) + theme( legend.position.inside = c(.8, .75), legend.background = element_blank(), legend.key = element_blank() ) ``` ### Gini for USA, UK and China These three data files provide all of the available years of the GINI index for China, USA, and the UK (countries with many years of data). This set is good for teaching about data merging and reshaping. ```{r} #| code-summary: Code to create pip_gini_china, pip_gini_usa, pip_gini_uk #| gini <- pip |> filter(reporting_level == "national") |> select(region_name:reporting_year, reporting_level, gini) pip_gini_usa <- filter(gini, country_code == "USA") pip_gini_china <- filter(gini, country_name == "China") pip_gini_uk <- filter(gini, country_name == "United Kingdom") ``` ```{r} #| echo: false write_csv(pip_gini_usa, "data/01/pip_gini_usa.csv") write_csv(pip_gini_china, "data/01/pip_gini_china.csv") write_csv(pip_gini_uk, "data/01/pip_gini_uk.csv") ``` - [pip_gini_china.csv](data/01/pip_gini_china.csv){download=""} - [pip_gini_uk.csv](data/01/pip_gini_uk.csv){download=""} - [pip_gini_usa.csv](data/01/pip_gini_usa.csv){download=""} ```{r} #| echo: false #| label: tbl-pip_gini_china #| tbl-cap: The first rows of pip_gini_china head(pip_gini_china) ``` ```{r} #| label: fig-gini-countries #| fig.cap: The change in Gini index across time for China, USA and UK #| code-summary: Plot Code bind_rows(pip_gini_china, pip_gini_uk, pip_gini_usa) |> ggplot(aes(x = reporting_year, y = gini, color = country_name)) + geom_point(alpha = 0.5) + geom_line(show.legend = FALSE) + scale_x_continuous(breaks = seq(1960, 2030, 10)) + scale_color_manual( values = c("#E5243B", "#4C9F38", "#19486A"), guide = guide_legend(position = "inside") ) + labs(x = NULL, y = "Gini Index", color = NULL) + theme( legend.position.inside = c(.15, .85), legend.background = element_blank(), legend.key = element_blank() ) ``` ## Resources - [Our World in Data: Poverty](https://ourworldindata.org/poverty) - [The World Bank: Poverty and Inequality Platform](https://pip.worldbank.org/home)