Progress in Reducing Working Poverty Rates in Africa (2000-2019)

Poverty rates for both women and men have steadily declined across all age groups in Africa’s lower-middle-income countries. Adults and youth have shown different paces of reduction in poverty, with all populations living below $2.15/day PPP.

SWDchallenge

2024

A visualization examining two decades of working poverty trends in Africa (2000-2019), highlighting the decline in poverty rates across gender and age groups. The analysis reveals distinct patterns between youth (15-24) and adult (25+) populations, showcasing overall improvement but persistent gender disparities in working poverty rates.

Author

Steven Ponce

Published

December 2, 2024

Figure 1: Line chart showing progress in reducing working poverty rates in Africa’s lower-middle-income countries from 2000 to 2019. The chart compares trends among women and men and the overall total across three age groups: Age 15 and older, youth age 15-24, and adults age 25 and older. Poverty rates declined for both men and women, with notable differences between adults and youth.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  pacman::p_load(
  tidyverse,         # Easily Install and Load the 'Tidyverse'
  ggtext,            # Improved Text Rendering Support for 'ggplot2'
  showtext,          # Using Fonts More Easily in R Graphs
  scales,            # Scale Functions for Visualization
  glue,              # Interpreted String Literals
  here,              # A Simpler Way to Find Your Files
  paletteer,         # Comprehensive Collection of Color Palettes
  janitor,           # Simple Tools for Examining and Cleaning Dirty Data
  skimr,             # Compact and Flexible Summaries of Data
  camcorder          # Record Your Plot History
)  
})

suppressMessages(source(here::here("_setup.R")))

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  10,
  height =  08,
  units  = "in",
  dpi    = 320
)

### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)

2. Read in the Data

Show code

## Data:      International Labour Organization 
##            Africa: Lower-middle income - Annual
## Link:      https://rshiny.ilo.org/dataexplorer5/?lang=en&id=X08_A

## Citation:
#' International Labour Organization. Africa: Lower-middle income - Annual
#' [ILOSTAT explorer. Africa: Lower-middle income - Annual (1990 - 2024)]. 
#' Accessed [2024-12-02].

poverty_rate_data_raw <- read_csv(
 here::here("data/ILO_X08_A-full-2024-12-02.csv")) |>
 filter(
    indicator.label == "SDG indicator 1.1.1 - Working poverty rate (percentage of employed living below US$2.15 PPP) (%)"
  ) |> 
  clean_names()

3. Examine the Data

Show code

glimpse(poverty_rate_data_raw)
skim(poverty_rate_data_raw)

4. Tidy Data

Show code

### |- tidy data ----

poverty_rate_clean <- poverty_rate_data_raw |> 
  select(-obs_status_label, -note_classif_label, -note_indicator_label, -note_source_label, -classif2_label) |> 
  rename(
    year = time,
    pct = obs_value
  ) |> 
  mutate(
    sex_label = str_remove(sex_label, pattern = "Sex: "),
    pct = pct / 100,
    age_bin = case_when(
      classif1_label == "Age (Youth, adults): 15+" ~ "Ages 15 and Older",
      classif1_label == "Age (Youth, adults): 15-24" ~ "Youth: Ages 15-24",
      classif1_label == "Age (Youth, adults): 25+" ~ "Adults: Ages 25 and Older",
      TRUE ~ classif1_label
    ),
    age_bin = factor(age_bin, levels = c("Ages 15 and Older", "Youth: Ages 15-24", "Adults: Ages 25 and Older"))
  ) |> 
  filter(year <= 2019) |> 
  pivot_wider(names_from = sex_label, values_from = pct)

5. Visualization Parameters

Show code

### |- plot aesthetics ----
bkg_col      <- "#f5f5f2"  
title_col    <- "gray20"           
subtitle_col <- "gray20"     
caption_col  <- "gray30"   
text_col     <- "gray20"    
col_palette  <- paletteer::paletteer_d("nbapalettes::mavericks_retro")

### |-  titles and caption ----
# icons
tt <- str_glue("#SWDchallenge: Dec 2024 &bull; Source: Source: Source: ILOSTAT<br>")
li <- str_glue("<span style='font-family:fa6-brands'>&#xf08c;</span>")
gh <- str_glue("<span style='font-family:fa6-brands'>&#xf09b;</span>")
bs <- str_glue("<span style='font-family:fa6-brands'>&#xe671; </span>")

# text
women  <- str_glue("<span style='font-size:16pt; color:{ col_palette[1] }'>**Women**</span>")
men    <- str_glue("<span style='font-size:16pt; color:{ col_palette[2] }'>**Men**</span>")
total  <- str_glue("<span style='font-size:16pt; color:{ col_palette[3] }'>**Total**</span>")

title_text   <- str_glue("Progress in Reducing Working Poverty Rates in Africa (2000-2019)") 

subtitle_text <- str_glue("Poverty rates for both {women} and {men} have steadily declined across all age groups in Africa\\'s<br>
                           lower-middle-income countries. Adults and youth have shown different paces of reduction in poverty,<br>
                           with all populations living below $2.15/day PPP. The dashed line represents the { total } (combined average)<br><br>
                          <span style='font-size:15.4pt;'><strong>Percentage of Workers in Poverty</strong></span>")

caption_text  <- str_glue("{tt} {li} stevenponce &bull; {bs} sponce1 &bull; {gh} poncest &bull; #rstats #ggplot2")

### |-  fonts ----
setup_fonts()

### |-  plot theme ----
theme_set(theme_minimal(base_size = 14, base_family = "text"))                

theme_update(
  plot.title.position   = "plot",
  plot.caption.position = "plot",
  legend.position       = "plot",
  plot.background       = element_rect(fill = bkg_col, color = bkg_col),
  panel.background      = element_rect(fill = bkg_col, color = bkg_col),
  plot.margin           = margin(t = 10, r = 20, b = 10, l = 20),
  axis.title.x          = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1), 
                                       color = text_col, family = "text", face = "bold", hjust = 0.5),
  axis.title.y          = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1), 
                                       color = text_col, family = "text", face = "bold", hjust = 0.5),
  axis.text             = element_text(size = rel(0.8), color = text_col, family = "text"),
  axis.line.x           = element_line(color = "#252525", linewidth = .3),
  axis.ticks.x          = element_line(color = text_col),  
  axis.title            = element_text(face = "bold"),
  panel.grid.minor      = element_blank(),
  panel.grid.major      = element_blank(),
  panel.grid.major.y    = element_line(color = "grey85", linewidth = .4),
  strip.background      = element_rect(fill = "#f0f0f0", color = NA),
  strip.text            = element_textbox(size = rel(1),  
                                          face = 'bold',
                                          color = text_col,
                                          hjust = 0.5,
                                          halign = 0.5,
                                          r = unit(5, "pt"),
                                          width = unit(5.5, "npc"),
                                          padding = margin(3, 0, 3, 0),
                                          margin = margin(3, 3, 3, 3),
                                          fill = "transparent"
  ),
  panel.spacing         = unit(1.5, 'lines'),
)

6. Plot

Show code

### |-  plot ----
p <- poverty_rate_clean |>
  ggplot(aes(x = year)) +
  # Fill the gap between Male and Female
  geom_ribbon(aes(ymin = Male, ymax = Female), fill = "gray75", alpha = 0.3) +
  # Geoms for Male and Female
  geom_line(aes(y = Female, color = "Female"), linewidth = 1) +
  geom_line(aes(y = Male, color = "Male"), linewidth = 1) +
  # Geom for Total (dashed line)
  geom_line(aes(y = Total, linetype = "Total"), color = col_palette[3], linewidth = 0.55, alpha = 0.6) +
  
  # Scales
  scale_x_continuous(
    breaks = seq(2000, 2020, by = 10),
    limits = c(2000, 2020)
  ) +
  scale_y_continuous(
    breaks = seq(0, .5, by = .1),
    limits = c(.1, .50),
    label = percent_format()
  ) +
  coord_cartesian(clip = "off") +
  scale_color_manual(values = col_palette) +
  scale_linetype_manual(values = c("dashed")) +
  
  # Labs
  labs(
    x = "Year",
    y = NULL,
    color = "Gender",
    linetype = "Total",
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
  ) +
  
  # Facet
  facet_wrap(~ age_bin, nrow = 1) +
  
  # Theme
  theme(
    plot.title = element_markdown(
      size = rel(1.7),
      family = "title",
      face = "bold",
      color = title_col,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.95),
      family = "subtitle",
      color = caption_col,
      lineheight = 1.1,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.65),
      family = "caption",
      color = caption_col,
      lineheight = 1.1,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 15, b = 5)
    )
  )

7. Save

Show code

### |-  plot image ----  

source(here::here("R/image_utils.R"))
save_plot(p, type = 'swd', year = 2024, month = 12, width = 10, height = 08)

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] ggplotify_0.1.2   gghighlight_0.4.1 paletteer_1.6.0   patchwork_1.3.0  
 [5] here_1.0.1        glue_1.8.0        scales_1.3.0      skimr_2.1.5      
 [9] janitor_2.2.0     showtext_0.9-7    showtextdb_3.0    sysfonts_0.8.9   
[13] ggtext_0.1.2      lubridate_1.9.3   forcats_1.0.0     stringr_1.5.1    
[17] dplyr_1.1.4       purrr_1.0.2       readr_2.1.5       tidyr_1.3.1      
[21] tibble_3.2.1      ggplot2_3.5.1     tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      pacman_0.5.1      
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] camcorder_0.1.0    repr_1.1.7         withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   cli_3.6.3          rmarkdown_2.29    
[29] crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1  tzdb_0.4.0        
[33] commonmark_1.9.2   splines_4.4.0      parallel_4.4.0     base64enc_0.1-3   
[37] vctrs_0.6.5        yulab.utils_0.1.8  Matrix_1.7-0       jsonlite_1.8.9    
[41] gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2        systemfonts_1.1.0 
[45] magick_2.8.5       gifski_1.32.0-1    rematch2_2.1.2     codetools_0.2-20  
[49] stringi_1.8.4      gtable_0.3.6       munsell_0.5.1      pillar_1.9.0      
[53] htmltools_0.5.8.1  R6_2.5.1           rprojroot_2.0.4    vroom_1.6.5       
[57] evaluate_1.0.1     lattice_0.22-6     markdown_1.13      gridtext_0.1.5    
[61] snakecase_0.11.1   renv_1.0.3         Rcpp_1.0.13-1      svglite_2.1.3     
[65] nlme_3.1-164       mgcv_1.9-1         xfun_0.49          fs_1.6.5          
[69] pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in swd_2024_12.qmd.

For the full repository, click here.

--- title: "Progress in Reducing Working Poverty Rates in Africa (2000-2019)" subtitle: "Poverty rates for both women and men have steadily declined across all age groups in Africa's lower-middle-income countries. Adults and youth have shown different paces of reduction in poverty, with all populations living below $2.15/day PPP." description: "A visualization examining two decades of working poverty trends in Africa (2000-2019), highlighting the decline in poverty rates across gender and age groups. The analysis reveals distinct patterns between youth (15-24) and adult (25+) populations, showcasing overall improvement but persistent gender disparities in working poverty rates." author: "Steven Ponce" date: "2024-12-02" categories: ["SWDchallenge", "2024"] tags: [poverty, africa, gender-equality, economic-development, time-series, visualization, working-poverty, youth-employment, demographic-analysis] image: "thumbnails/swd_2024_12.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/swd_2024_12.png" # description: "Exploring 20 years of working poverty reduction in Africa: A data visualization showing how poverty rates have declined differently for women and men across age groups, with particular focus on youth (15-24) versus adult (25+) populations. The analysis reveals sustained progress in reducing working poverty while highlighting persistent gender gaps. #DataVisualization #Africa #PovertyReduction #GenderEquality #EconomicDevelopment" # linkedin: true # twitter: true # email: true --- ![Line chart showing progress in reducing working poverty rates in Africa's lower-middle-income countries from 2000 to 2019. The chart compares trends among women and men and the overall total across three age groups: Age 15 and older, youth age 15-24, and adults age 25 and older. Poverty rates declined for both men and women, with notable differences between adults and youth.](swd_2024_12.png){#fig-1} ### __Steps to Create this Graphic__ #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files paletteer, # Comprehensive Collection of Color Palettes janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data camcorder # Record Your Plot History ) }) suppressMessages(source(here::here("_setup.R"))) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 10, height = 08, units = "in", dpi = 320 ) ### |- resolution ---- showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false ## Data: International Labour Organization ## Africa: Lower-middle income - Annual ## Link: https://rshiny.ilo.org/dataexplorer5/?lang=en&id=X08_A ## Citation: #' International Labour Organization. Africa: Lower-middle income - Annual #' [ILOSTAT explorer. Africa: Lower-middle income - Annual (1990 - 2024)]. #' Accessed [2024-12-02]. poverty_rate_data_raw <- read_csv( here::here("data/ILO_X08_A-full-2024-12-02.csv")) |> filter( indicator.label == "SDG indicator 1.1.1 - Working poverty rate (percentage of employed living below US$2.15 PPP) (%)" ) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(poverty_rate_data_raw) skim(poverty_rate_data_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- poverty_rate_clean <- poverty_rate_data_raw |> select(-obs_status_label, -note_classif_label, -note_indicator_label, -note_source_label, -classif2_label) |> rename( year = time, pct = obs_value ) |> mutate( sex_label = str_remove(sex_label, pattern = "Sex: "), pct = pct / 100, age_bin = case_when( classif1_label == "Age (Youth, adults): 15+" ~ "Ages 15 and Older", classif1_label == "Age (Youth, adults): 15-24" ~ "Youth: Ages 15-24", classif1_label == "Age (Youth, adults): 25+" ~ "Adults: Ages 25 and Older", TRUE ~ classif1_label ), age_bin = factor(age_bin, levels = c("Ages 15 and Older", "Youth: Ages 15-24", "Adults: Ages 25 and Older")) ) |> filter(year <= 2019) |> pivot_wider(names_from = sex_label, values_from = pct) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- bkg_col <- "#f5f5f2" title_col <- "gray20" subtitle_col <- "gray20" caption_col <- "gray30" text_col <- "gray20" col_palette <- paletteer::paletteer_d("nbapalettes::mavericks_retro") ### |- titles and caption ---- # icons tt <- str_glue("#SWDchallenge: Dec 2024 • Source: Source: Source: ILOSTAT ") li <- str_glue("") gh <- str_glue("") bs <- str_glue(" ") # text women <- str_glue("**Women**") men <- str_glue("**Men**") total <- str_glue("**Total**") title_text <- str_glue("Progress in Reducing Working Poverty Rates in Africa (2000-2019)") subtitle_text <- str_glue("Poverty rates for both {women} and {men} have steadily declined across all age groups in Africa\\'s lower-middle-income countries. Adults and youth have shown different paces of reduction in poverty, with all populations living below $2.15/day PPP. The dashed line represents the { total } (combined average) Percentage of Workers in Poverty") caption_text <- str_glue("{tt} {li} stevenponce • {bs} sponce1 • {gh} poncest • #rstats #ggplot2") ### |- fonts ---- setup_fonts() ### |- plot theme ---- theme_set(theme_minimal(base_size = 14, base_family = "text")) theme_update( plot.title.position = "plot", plot.caption.position = "plot", legend.position = "plot", plot.background = element_rect(fill = bkg_col, color = bkg_col), panel.background = element_rect(fill = bkg_col, color = bkg_col), plot.margin = margin(t = 10, r = 20, b = 10, l = 20), axis.title.x = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1), color = text_col, family = "text", face = "bold", hjust = 0.5), axis.title.y = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1), color = text_col, family = "text", face = "bold", hjust = 0.5), axis.text = element_text(size = rel(0.8), color = text_col, family = "text"), axis.line.x = element_line(color = "#252525", linewidth = .3), axis.ticks.x = element_line(color = text_col), axis.title = element_text(face = "bold"), panel.grid.minor = element_blank(), panel.grid.major = element_blank(), panel.grid.major.y = element_line(color = "grey85", linewidth = .4), strip.background = element_rect(fill = "#f0f0f0", color = NA), strip.text = element_textbox(size = rel(1), face = 'bold', color = text_col, hjust = 0.5, halign = 0.5, r = unit(5, "pt"), width = unit(5.5, "npc"), padding = margin(3, 0, 3, 0), margin = margin(3, 3, 3, 3), fill = "transparent" ), panel.spacing = unit(1.5, 'lines'), ) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- plot ---- p <- poverty_rate_clean |> ggplot(aes(x = year)) + # Fill the gap between Male and Female geom_ribbon(aes(ymin = Male, ymax = Female), fill = "gray75", alpha = 0.3) + # Geoms for Male and Female geom_line(aes(y = Female, color = "Female"), linewidth = 1) + geom_line(aes(y = Male, color = "Male"), linewidth = 1) + # Geom for Total (dashed line) geom_line(aes(y = Total, linetype = "Total"), color = col_palette[3], linewidth = 0.55, alpha = 0.6) + # Scales scale_x_continuous( breaks = seq(2000, 2020, by = 10), limits = c(2000, 2020) ) + scale_y_continuous( breaks = seq(0, .5, by = .1), limits = c(.1, .50), label = percent_format() ) + coord_cartesian(clip = "off") + scale_color_manual(values = col_palette) + scale_linetype_manual(values = c("dashed")) + # Labs labs( x = "Year", y = NULL, color = "Gender", linetype = "Total", title = title_text, subtitle = subtitle_text, caption = caption_text, ) + # Facet facet_wrap(~ age_bin, nrow = 1) + # Theme theme( plot.title = element_markdown( size = rel(1.7), family = "title", face = "bold", color = title_col, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(0.95), family = "subtitle", color = caption_col, lineheight = 1.1, margin = margin(t = 5, b = 20) ), plot.caption = element_markdown( size = rel(0.65), family = "caption", color = caption_col, lineheight = 1.1, hjust = 0.5, halign = 0.5, margin = margin(t = 15, b = 5) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- source(here::here("R/image_utils.R")) save_plot(p, type = 'swd', year = 2024, month = 12, width = 10, height = 08) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`swd_2024_12.qmd`]( https://github.com/poncest/personal-website/tree/master/data_visualizations/SWD%20Challenge/2024/swd_2024_12.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). :::