Water inequalities across CPLP member states

Author

Sílvia Paiva

Published

December 16, 2025

Introduction

Although water covers most of the Earth’s surface, only a tiny fraction is available as accessible freshwater for human use. Ensuring safe and reliable drinking water has become a global priority as population growth, environmental pressures, and infrastructural inequalities intensify challenges to water security (Arora and Mishra 2022). To track progress toward universal access, the WHO/UNICEF Joint Monitoring Programme (JMP) compiles global data estimates on drinking water services across countries and over time. This project uses these data to examine trends in drinking water access among Portuguese-speaking countries (CPLP) and to compare inequalities between rural and urban populations.

Methods

The data used in this project were obtained from the WHO/UNICEF JMP (https://washdata.org/data). The dataset includes country-level drinking water indicators reported between 2000 and 2024, along with associated demographic and socioeconomic classifications such as SDG region, income group, and population. Data were downloaded in raw Excel format and processed to standardise variable names, reconstruct multi-row headers, and convert the wide-format service-level indicators into a tidy long format. For the purposes of this analysis, the dataset was restricted to CPLP (e.g., at least basic, limited, unimproved, safely managed) and filtered to retain information relevant to drinking water access across rural, urban, and total populations.

Install & Load Packages

Code

# install packages
install.packages("patchwork")

# load packages
library(tidyverse)
library(ggthemes)
library(here)
library(knitr)
library(gt)
library(patchwork)

Import & Select Data

Code

# import processed data
water <- read_csv(here::here("data/processed/water_data_long.csv"))

# filter data for CPLP countries  
water_cplp <- water |> 
  filter(country %in% c("Portugal", "Brazil", "Angola", "Cabo Verde", 
                        "Guinea-Bissau", "Mozambique", 
                        "Sao Tome and Principe", "Timor-Leste"))

Results

Access to drinking water in rural and urban CPLP Populations

Figure 1 and Figure 2 illustrate access to “at least basic” and “limited” drinking water services among rural and urban populations in CPLP countries in 2004 and 2024, respectively.

Code

# check the differences in accessing drinking water between rural and urban populations
water_cplp_access_2004 <- water_cplp |> 
  filter(
    year == 2004,
    varname_short %in% c("wat_bas", "wat_lim"),
    residence != "Total") |> 
  mutate(
    percent = parse_number(percent)) # handles values like >99%
  
  ggplot(data = water_cplp_access_2004,
         mapping = aes(x = residence, 
                       y = percent, 
                       fill = varname_long)) +
  geom_col() +
  facet_wrap(~country) +
  scale_fill_calc(name = NULL) +
  geom_text(aes(label = round(percent, 1)),
            position = position_stack(vjust = 0.5),
            size = 3,
            colour = "white")

Figure 1: Access to drinking water in CPLP in 2004

Code

# check the differences in accessing drinking water between rural and urban populations
water_cplp_access_2024 <- water_cplp |> 
  filter(
    year == 2024,
    varname_short %in% c("wat_bas", "wat_lim"),
    residence != "Total") |> 
  mutate(
    percent = parse_number(percent)) # handles values like >99%
  
  ggplot(data = water_cplp_access_2024,
         mapping = aes(x = residence, 
                       y = percent, 
                       fill = varname_long)) +
  geom_col() +
  facet_wrap(~country) +
  scale_fill_calc(name = NULL) +
  geom_text(aes(label = round(percent, 1)),
            position = position_stack(vjust = 0.5),
            size = 3,
            colour = "white")

Figure 2: Access to drinking water in CPLP in 2024

All CPLP countries experienced improvements in access to at least basic drinking water between 2004 and 2024.
Access to at least basic drinking water remains consistently higher than reliance on limited services in both rural and urban populations.
Rural–urban disparities are most pronounced in Angola, Guinea-Bissau, and Mozambique, where rural populations continue to exhibit the lowest levels of access to basic drinking water.
Angola, Guinea-Bissau, Mozambique, São Tomé and Príncipe, and Timor-Leste increased their reliance on limited drinking water services in 2024.

Country-level trajectories in access to drinking water (2004-2024)

Figure 3 presents country-level trajectories of access to drinking water services in Angola, Cabo Verde, Mozambique, and Timor-Leste between 2004 and 2024.

Code

# summarise the evolution in access to drinking water in cplp between 2004 and 2024
water_cplp_evol <- water_cplp |>
  filter(
    residence == "Total",
    varname_short %in% c("wat_bas", "wat_lim"),
    year >= 2004,
    year <= 2024
  ) |>
  mutate(percent = parse_number(percent)) |>
  select(country, year, varname_short, percent) |>
  pivot_wider(
    names_from = varname_short,
    values_from = percent)

# select country for analysis - write country of interest
countries_to_plot <- c("Angola", "Cabo Verde", "Mozambique", "Timor-Leste")

water_subset <- water_cplp_evol |>
  filter(country %in% countries_to_plot)

global_max_lim <- max(water_subset$wat_lim, na.rm = TRUE)
scale_factor   <- 100 / global_max_lim   # maps wat_lim onto 0–100 scale

plots <- list()  # empty list to store each country's plot

for (i in seq_along(countries_to_plot)) {
  country_loop <- countries_to_plot[i]
  
  df_country <- water_subset |>
    filter(country == country_loop)


# plot evolution for each selected country
plot <- ggplot(data = df_country,
       mapping = aes(x = year)) +
  # Left axis: wat_bas
  geom_point(aes(y = wat_bas, colour = "At least basic"), size = 1) +
  geom_line(aes(y = wat_bas, colour = "At least basic"), linewidth = 0.5) +
  
  # Right axis: wat_lim (rescaled)
  geom_point(aes(y = wat_lim * scale_factor, colour = "Limited"), size = 1) +
  geom_line(aes(y = wat_lim * scale_factor, colour = "Limited"),
            linewidth = 0.5, linetype = "dashed") +
  scale_colour_manual(
    values = c("At least basic" = "steelblue", "Limited" = "coral2"),
    name = NULL) +
  labs(title = paste(country_loop),
      x = "Year",
      y = "Percentage (%)") + 
  scale_y_continuous(
    name = "At least basic drinking water (%)", 
    limits = c(0, 100),
    sec.axis = sec_axis(~ . / scale_factor, 
                        name = "Limited drinking water (%)")) + 
  theme_minimal(base_size = 8) +
  theme(legend.position = "bottom")

# store this plot in the list
plots[[i]] <- plot}

# 2x2 grid with patchwork
(plots[[1]] | plots[[2]]) /
(plots[[3]] | plots[[4]]) + 
  plot_annotation(
    title = "Country-level trajectories in access to drinking water (2004–2024)",
    theme = theme(
      plot.title = element_text(size = 12, face = "bold", hjust = 0.5)))

Figure 3: Access to drinking water in CPLP in 2024

Access to at least basic drinking water increased steadily over the two-decade period.
Improvements in basic access do not always coincide with reductions in reliance on limited services.
Cabo Verde and Timor-Leste show relatively high and sustained levels of basic water access by 2024.
Angola and Mozambique exhibit slower and more uneven progress.

Comparison of drinking water service levels in CPLP relative to SDG regional averages (2004 vs 2024)

Table 1 and Table 2 compare access to “at least basic” drinking water in each CPLP country with the corresponding SDG regional average in 2004 and 2024. This comparison contextualises national performance within broader regional trends and highlights whether countries are performing above, below, or in line with their respective SDG region.

Code

# summarise the progress in access to drinking water in each sdg_region between 2004 and 2024
water_region <- water |>
  filter(
    varname_short == "wat_bas",
    residence == "Total",
    year >= 2004,
    year <= 2024
  ) |>
  mutate(
    percent = na_if(percent, "-"),
    percent = parse_number(percent)) |>
  group_by(sdg_region, year) |>
  summarise(
    regional_mean = mean(percent, na.rm = TRUE),
    .groups = "drop")

# Compare access to drinking water in cplp to corresponding sdg_region
cplp_iso3 <- c("PRT","BRA","AGO","CPV","GNB","MOZ","STP","TLS")

water_cplp_vs_region <- water |>
  filter(
    iso3 %in% cplp_iso3,
    varname_short == "wat_bas",
    residence == "Total",
    year >= 2004,
    year <= 2024) |>
  mutate(
    percent = na_if(percent, "-"),
    percent = parse_number(percent)) |>
  left_join(
    water_region,
    by = c("sdg_region", "year")) |> # join CPLP rows to their region-year means
  mutate(
    diff_from_region = percent - regional_mean,   # positive = better than region
    level_to_region = case_when(
      diff_from_region > 0 ~ "Above regional average",
      diff_from_region < 0 ~ "Below regional average",
      TRUE                 ~ "At regional average"))


water_cplp_vs_region |>
  filter(year == 2004) |>
  select(country, sdg_region, income_id, percent, regional_mean, level_to_region) |> 
  mutate(
    percent = round(percent, 1),
    regional_mean = round(regional_mean, 1)) |> 
  gt(groupname_col = "varname_short") |> 
  cols_label(
    country       = "Country",
    sdg_region    = "SDG Region",
    income_id = "Income Level",
    percent       = "Country (%)",
    regional_mean = "Regional Mean (%)",
    level_to_region = "Level to Region") |>
  fmt_number(columns = c(percent, regional_mean),
             decimals = 1) |> 
  tab_options(
    table.font.size = px(11),        
    heading.title.font.size = px(12),
    column_labels.font.size = px(12),
    data_row.padding = px(1)         
  )

Table 1: Access to at least basic drinking water in CPLP countries vs regional averages in 2004.

Country	SDG Region	Income Level	Country (%)	Regional Mean (%)	Level to Region
Angola	Sub-Saharan Africa	Lower middle income	42.0	59.7	Below regional average
Brazil	Latin America and the Caribbean	Upper middle income	94.8	93.4	Above regional average
Cabo Verde	Sub-Saharan Africa	Lower middle income	80.7	59.7	Above regional average
Guinea-Bissau	Sub-Saharan Africa	Low income	56.9	59.7	Below regional average
Mozambique	Sub-Saharan Africa	Low income	28.9	59.7	Below regional average
Portugal	Europe and Northern America	High income	99.0	97.7	Above regional average
Sao Tome and Principe	Sub-Saharan Africa	Lower middle income	71.1	59.7	Above regional average
Timor-Leste	Eastern and South-Eastern Asia	Lower middle income	53.0	83.8	Below regional average

Code

water_cplp_vs_region |>
  filter(year == 2024) |>
  select(country, sdg_region, income_id, percent, regional_mean, level_to_region) |> 
  mutate(
    percent = round(percent, 1),
    regional_mean = round(regional_mean, 1)) |> 
  gt(groupname_col = "varname_short") |> 
  cols_label(
    country = "Country",
    sdg_region = "SDG Region",
    income_id = "Income Level",
    percent = "Country (%)",
    regional_mean = "Regional Mean (%)",
    level_to_region = "Level to Region") |>
  fmt_number(columns = c(percent, regional_mean),
             decimals = 1) |> 
  tab_options(
    table.font.size = px(11),        
    heading.title.font.size = px(12),
    column_labels.font.size = px(12),
    data_row.padding = px(1)         
  )

Table 2: Access to at least basic drinking water in CPLP countries vs regional averages in 2024.

Country	SDG Region	Income Level	Country (%)	Regional Mean (%)	Level to Region
Angola	Sub-Saharan Africa	Lower middle income	68.0	73.4	Below regional average
Brazil	Latin America and the Caribbean	Upper middle income	99.0	96.3	Above regional average
Cabo Verde	Sub-Saharan Africa	Lower middle income	92.3	73.4	Above regional average
Guinea-Bissau	Sub-Saharan Africa	Low income	61.8	73.4	Below regional average
Mozambique	Sub-Saharan Africa	Low income	66.6	73.4	Below regional average
Portugal	Europe and Northern America	High income	99.0	98.3	Above regional average
Sao Tome and Principe	Sub-Saharan Africa	Lower middle income	78.0	73.4	Above regional average
Timor-Leste	Eastern and South-Eastern Asia	Lower middle income	87.3	94.6	Below regional average

Although all CPLP countries improved access to drinking water over time, several did not converge toward regional averages by 2024.
CPLP countries located in Sub-Saharan Africa, specifically Angola, Guine-Bissau, and Mozambique, remain below their regional average for access to at least basic drinking water.
Brazil, Cabo Verde, Portugal, and São Tomé and Príncipe perform above their respective SDG regional averages in both 2004 and 2024.

Conclusions

In summary:

Access to drinking water has improved across all CPLP countries between 2004 and 2024, particularly for at least basic services; however, progress remains uneven, with persistent rural–urban gaps and heterogeneous country-level trajectories indicating ongoing challenges in extending services equitably;
The continued reliance on limited drinking water services in multiple countries, despite gains in basic access, highlights structural constraints in transitioning along the drinking water service ladder;
CPLP countries with low and lower-middle-income remain behind their SDG regional averages, suggesting that economic capacity play a critical role in shaping water service expansion and convergence toward SDG 6 targets (Valero et al. 2023; Arora and Mishra 2022).

Disclaimer

GenAI ChatGPT was used to assist with code debugging and language refinement. All data analysis, interpretation, and conclusions are the author’s own.

References

Arora, Naveen Kumar, and Isha Mishra. 2022. “Sustainable Development Goal 6: Global Water Security.” Environmental Sustainability 5 (3): 271–75. https://doi.org/10.1007/s42398-022-00246-5.

Valero, Diana, Jess Cook, Angus Lee, Alison L. Browne, Rowan Ellis, Vidya Sagar Pancholi, and Claire Hoolohan. 2023. “Addressing Water Poverty Under Climate Crisis: Implications for Social Policy.” Social Policy and Society 22 (4): 747–62. https://doi.org/10.1017/s1474746423000258.