Drinking Water Service Level and Gross Domestic Product (GDP) per capita in ASEAN Countries: A Comparative Study
Author
Adnan Rafif Wijayarso
Published
February 13, 2024
Abstract
The current Eastern and South-Eastern Asia region defined by the United Nations (UN) to report the progress towards Sustainable Development Goals (SDGs) target contains two contrasting groups of countries, in terms of income — therefore it is justified to emphasize the study on ASEAN countries as they may need to narrow the gap in drinking water service level compared to higher income countries. The study finds that the ASEAN region ranks 6th of all regions in level of access to improved drinking water service (94.9% of households served) and 3rd of all regions in level of access to safely managed drinking water service (39.8% of households served). Households in Singapore and Malaysia have the highest access to safely managed drinking water service in the ASEAN region, with service level being 99% and 93.9%, respectively. A strong, positive correlation (Spearman’s correlation coefficient ρ = 0.72, p-value = 0.000) exists between ASEAN and other countries’ access level to improved drinking water service and their GDP per capita. An even stronger correlation (Spearman’s correlation coefficient ρ = 0.88, p-value = 0.000) exists between ASEAN and other countries’ access level to safely managed drinking water service and their GDP per capita.
Introduction
Clean drinking water is essential for our life, therefore universal access to safe drinking water is a fundamental human right. On September 2000, the United Nations declared the Millenium Development Goals (MDGs) which targeted to halve the proportion of the population without sustainable access to safe drinking water by 2015. The agenda was even made to be more ambitious by the declaration of Sustainable Development Goals (SDGs) in 2016 which targets all countries participating to ensure availability of safely managed drinking water by 2030.
The United Nations (UN) currently reports the progress made towards SDGs target by dividing the world into eight regions, one of them being Eastern and South-Eastern Asia. At a glance, there seems to be a contrast between East Asian countries (which is dominated by higher income countries such as Japan, China, and South Korea) and Southeast Asian/ASEAN countries (which is dominated by middle income countries such as Indonesia, Thailand, and Viet Nam). Achieving the goal obviously requires tremendous resources, therefore it is justified to emphasize the study on ASEAN countries as they may need to narrow the gap in drinking water service level compared to higher income countries.
This data analysis project is created to explore how drinking water service level differs across ASEAN countries and how it correlates with their Gross Domestic Product (GDP) per capita as the most used tool by governments to measure the economic health of a country, in comparison to other SDG regions.
the summary of 2022 JMP drinking water service ladder in the regional level,
the 2022 JMP drinking water service ladder for each country in the ASEAN region,
the relationship between GDP per capita and the levels of access to improved water service,
and the relationship between GDP per capita and the levels of access to safely managed drinking water service.
There is currently no separate category that groups ASEAN countries in the JMP Global Database, as the United Nations (UN) only defined a more general “Eastern and South-Eastern Asia” region in its Report and Statistical Annex. Therefore, in order to make these analyses possible, a new regional system is defined in this study so ASEAN countries can be put into one separate “ASEAN” region.
Figure 1: ASEAN drinking water service level, compared to other SDG regions and world average
Figure 1 shows that the drinking water service level in the ASEAN region is slightly better than the world average. Most households in ASEAN region already have access to at least basic drinking water service, and 39.8% of households already have access to safely managed drinking water service. However, there is still large gap in the percentage of households with access to safely managed drinking water service between ASEAN region and Latin America & the Caribbean region.
Comparing water service level for each ASEAN country
Figure 2: ASEAN countries drinking water service level
If we delve deeper and compare all countries in ASEAN region, as pictured in Figure 2, it is shown that Singapore and Malaysia hold the highest percentages of households with access to safely managed drinking water service, with their respective service level being 99% and 93.9%. On the contrary, Lao People’s Democratic Republic and Cambodia have the least access to safely managed drinking water service, with service level being 17.9% and 29.1%, respectively.
We can also highlight from Figure 2 that Myanmar and Cambodia have relatively lack of access in improved drinking water service on the national level. In 2022, 11.1% of households in Myanmar and 7.3% of households in Cambodia only use surface water as their daily source of drinking water.
How GDP per capita correlates with improved drinking water service level
Comparison of all regions’ characteristics, GDP per capita, and %
of households with access to improved drinking water service
Region
No. of countries
Population (1000)
Min. GDP per capita
Median GDP per capita
Max. GDP per capita
Min. service level
Average service level
Max. service level
Australia and New Zealand
2
31,363
$48,419
$56,759
$65,100
99.0%
99.0%
99.0%
Europe and Northern America
52
1,118,593
$4,534
$29,675
$240,862
90.4%
98.1%
99.9%
Northern Africa and Western Asia
25
553,690
$650
$6,867
$87,661
89.1%
97.3%
100.0%
Eastern and South-Eastern Asia
18
2,344,325
$1,149
$6,910
$82,808
82.4%
96.9%
99.0%
Central and Southern Asia
14
2,084,590
$1,054
$2,550
$11,781
83.3%
96.1%
99.6%
ASEAN
10
679,784
$1,149
$4,476
$82,808
82.4%
94.9%
99.0%
Latin America and the Caribbean
50
660,269
$1,748
$12,264
$99,625
77.1%
89.6%
100.0%
Sub-Saharan Africa
51
1,166,766
$259
$1,471
$13,250
58.0%
78.1%
99.0%
Oceania
21
13,676
$1,702
$5,222
$35,745
52.6%
57.5%
99.0%
Code
plot3_allcountries <- WaterService_vs_GDP_long |>filter(year ==2022, residence =="national", levels_service =="basic"| levels_service =="limited") |>group_by(new_region, country) |>summarise(population_1000 =mean(country_population_1000, na.rm =TRUE),GDPpercap =median(GDPpercap, na.rm =TRUE),sum_served_population_1000 =ifelse(all(is.na(served_population_1000)), NA, sum(served_population_1000, na.rm =TRUE)) ) |>mutate(served_percentage = sum_served_population_1000 / population_1000) |>rename(region = new_region) |>mutate(region =case_when( region =="ASEAN"~ region,TRUE~"Other regions"))plot3_improved_rank <- plot3_allcountries |>filter(region =="ASEAN") |>arrange(desc(served_percentage)) |>mutate(rank =row_number()) |>relocate(country, .before = rank)plot3_improved_rank <- plot3_improved_rank[6:7]plot3 <- plot3_allcountries |>left_join(plot3_improved_rank, by ="country")# Calculating minimum GDP per capita a country need to provide improved drinking water service for allplot3_minGDP <- plot3_allcountries |>filter(served_percentage ==0.99) |>summarise(min_GDPpercap =min(GDPpercap, na.rm =TRUE)) |>arrange(min_GDPpercap)# Calculating Spearman's rank correlation coefficientplot3_cor <-cor.test(plot3$GDPpercap, plot3$served_percentage, use ="complete.obs", method ="spearman")plot3_conclusion <-paste(case_when(plot3_cor$estimate >=0.9& plot3_cor$estimate <=1~"very strong", plot3_cor$estimate >=0.7& plot3_cor$estimate <0.9~"strong", plot3_cor$estimate >=0.4& plot3_cor$estimate <0.7~"moderate", plot3_cor$estimate >=0.2& plot3_cor$estimate <0.4~"weak", plot3_cor$estimate >=0& plot3_cor$estimate <0.2~"very weak"))plot3_relation <-paste(case_when(plot3_cor$estimate >0& plot3_cor$estimate <=1~"positive", plot3_cor$estimate <0& plot3_cor$estimate >=-1~"negative"))plot3_caption <-paste("Spearman's ρ was used to assess the relationship between GDP per capita and water service level ( ρ = ",sprintf("%.2f", plot3_cor$estimate), ", p = ", sprintf("%.3f", plot3_cor$p.value), "), showing a", plot3_conclusion, "and ", plot3_relation, "correlation between the two variables")plot3_minGDP_print <-paste("US$", comma(min(plot3_minGDP$min_GDPpercap)))ggplot(data = plot3,mapping =aes(x = GDPpercap,y = served_percentage,color = region,size = region)) +geom_point() +scale_color_manual(values =c("ASEAN"="red3", "Other regions"="gray80")) +scale_size_manual(values =c("ASEAN"=3, "Other regions"=2)) +# Adding extra layers of geom_point so the ASEAN points can be on top of other regions' geom_point(data = plot3 |>filter(region =="ASEAN"),color ="red3",size =3) +geom_text(aes(label =case_when( rank <=2~ country, rank >=9~ country,TRUE~"")),nudge_y =-0.025) +ggtitle("GDP per capita vs % of households with access\nto improved drinking water services") +labs(x ="GDP per capita", y ="percentage",caption =str_wrap(plot3_caption, width =95)) +scale_x_continuous(labels = scales::dollar,trans ="log10") +scale_y_continuous(labels = scales::percent) +theme_minimal()
Figure 3: GDP per capita vs % of households with access to improved drinking water services
Table 1 shows that the ASEAN region ranks in 6th when it comes to providing access to improved drinking water service, with average water service level of 94.9%. On the regional level, the relationship between GDP per capita and the level of access to improved drinking water service remains unclear. However, if we plot the data on the country level using a scatter plot, as pictured in Figure 3, a strong, positive correlation exists[1] between the two variables. It appears that the countries able to provide all households with improved drinking water service have minimum GDP per capita of US$ 5,222.
How GDP per capita correlates with safely managed drinking water service level
Code
table2_ASEAN_2022_safelymanaged <- WaterService_vs_GDP_ASEAN |>filter(year ==2022, residence =="national", levels_waterservice =="safely managed") |>group_by(region) |>summarise(count_country =n(),population_1000 =sum(country_population_1000),min_GDPpercap =min(GDPpercap),max_GDPpercap =max(GDPpercap),median_GDPpercap =median(GDPpercap),min_served_percentage =min(served_percentage, na.rm =TRUE),max_served_percentage =max(served_percentage, na.rm =TRUE),served_population_1000 =sum(served_population_1000, na.rm =TRUE)) |>mutate(mean_served_percentage = served_population_1000 / population_1000 *100) |>relocate(median_GDPpercap, .after = min_GDPpercap) |>relocate(mean_served_percentage, .after = min_served_percentage)table2_region_worldAVG_2022_safelymanaged <- WaterService_vs_GDP_long |>filter(year ==2022, residence =="national", levels_service =="safely managed") |>group_by(sdg_region) |>summarise(count_country =n(),population_1000 =sum(country_population_1000, na.rm =TRUE),min_GDPpercap =min(GDPpercap, na.rm =TRUE),max_GDPpercap =max(GDPpercap, na.rm =TRUE),median_GDPpercap =median(GDPpercap, na.rm =TRUE),min_served_percentage =min(served_percentage, na.rm =TRUE),max_served_percentage =max(served_percentage, na.rm =TRUE),served_population_1000 =sum(served_population_1000, na.rm =TRUE)) |>mutate(mean_served_percentage = served_population_1000 / population_1000 *100) |>relocate(median_GDPpercap, .after = min_GDPpercap) |>relocate(mean_served_percentage, .after = min_served_percentage) |>rename(region = sdg_region)table2 <-bind_rows(table2_ASEAN_2022_safelymanaged, table2_region_worldAVG_2022_safelymanaged) |>arrange(desc(mean_served_percentage))table2[,1:9] |>rename("Region"= region) |>rename("No. of countries"= count_country) |>rename("Population (1000)"= population_1000) |>rename("Min. GDP per capita"= min_GDPpercap) |>rename("Median GDP per capita"= median_GDPpercap) |>rename("Max. GDP per capita"= max_GDPpercap) |>rename("Min. service level"= min_served_percentage) |>rename("Average service level"= mean_served_percentage) |>rename("Max. service level"= max_served_percentage) |>gt() |>fmt_number(columns ="Population (1000)",use_seps =TRUE,decimals =0) |>fmt_currency(columns ="Min. GDP per capita":"Max. GDP per capita",currency ="USD",use_seps =TRUE,decimals =0) |>fmt_percent(columns ="Min. service level":"Max. service level",scale =0,decimals =1) |>tab_options(column_labels.font.weight ="bold",table.font.size =px(13))
Table 2:
Comparison of all regions’ characteristics, GDP per capita, and %
of households with access to safely managed drinking water service
Region
No. of countries
Population (1000)
Min. GDP per capita
Median GDP per capita
Max. GDP per capita
Min. service level
Average service level
Max. service level
Europe and Northern America
52
1,118,593
$4,534
$29,675
$240,862
70.7%
93.3%
99.0%
Latin America and the Caribbean
50
660,269
$1,748
$12,264
$99,625
43.0%
55.3%
99.0%
ASEAN
10
679,784
$1,149
$4,476
$82,808
17.9%
39.8%
99.0%
Northern Africa and Western Asia
25
553,690
$650
$6,867
$87,661
47.7%
26.5%
99.0%
Eastern and South-Eastern Asia
18
2,344,325
$1,149
$6,910
$82,808
17.9%
20.1%
99.0%
Central and Southern Asia
14
2,084,590
$1,054
$2,550
$11,781
16.1%
18.1%
94.9%
Australia and New Zealand
2
31,363
$48,419
$56,759
$65,100
99.0%
16.4%
99.0%
Sub-Saharan Africa
51
1,166,766
$259
$1,471
$13,250
6.1%
13.9%
95.8%
Oceania
21
13,676
$1,702
$5,222
$35,745
8.7%
9.9%
99.0%
Code
plot4_allcountries <- WaterService_vs_GDP_long |>filter(year ==2022, residence =="national", levels_service =="safely managed") |>group_by(new_region, country) |>summarise(population_1000 =mean(country_population_1000, na.rm =TRUE),GDPpercap =median(GDPpercap, na.rm =TRUE),sum_served_population_1000 =ifelse(all(is.na(served_population_1000)), NA, sum(served_population_1000, na.rm =TRUE)) ) |>mutate(served_percentage = sum_served_population_1000 / population_1000) |>rename(region = new_region) |>mutate(region =case_when( region =="ASEAN"~ region,TRUE~"Other regions"))plot4_improved_rank <- plot4_allcountries |>filter(region =="ASEAN") |>arrange(desc(served_percentage)) |>mutate(rank =row_number()) |>relocate(country, .before = rank)plot4_improved_rank <- plot4_improved_rank[6:7]plot4 <- plot4_allcountries |>left_join(plot4_improved_rank, by ="country")# Calculating minimum GDP per capita a country need to provide safely drinking water service for allplot4_minGDP <- plot4_allcountries |>filter(served_percentage >=0.99) |>summarise(min_GDPpercap =min(GDPpercap, na.rm =TRUE)) |>arrange(min_GDPpercap)plot4_minGDP_print <-paste("US$", comma(min(plot4_minGDP$min_GDPpercap)))# Calculating Spearman's rank correlation coefficient for plot 4plot4_cor <-cor.test(plot4$GDPpercap, plot4$served_percentage, use ="complete.obs", method ="spearman")plot4_conclusion <-paste(case_when(plot4_cor$estimate >=0.9& plot4_cor$estimate <=1~"very strong", plot4_cor$estimate >=0.7& plot4_cor$estimate <0.9~"strong", plot4_cor$estimate >=0.4& plot4_cor$estimate <0.7~"moderate", plot4_cor$estimate >=0.2& plot4_cor$estimate <0.4~"weak", plot4_cor$estimate >=0& plot4_cor$estimate <0.2~"very weak"))plot4_relation <-paste(case_when(plot4_cor$estimate >0& plot4_cor$estimate <=1~"positive", plot4_cor$estimate <0& plot4_cor$estimate >=-1~"negative"))plot4_caption <-paste("Spearman's ρ was used to assess the relationship between GDP per capita and water service level ( ρ = ",sprintf("%.2f", plot4_cor$estimate), ", p = ", sprintf("%.3f", plot4_cor$p.value), "), showing a", plot4_conclusion, "and ", plot4_relation, "correlation between the two variables")ggplot(data = plot4,mapping =aes(x = GDPpercap,y = served_percentage,color = region,size = region)) +geom_point() +scale_color_manual(values =c("ASEAN"="red3", "Other regions"="gray80")) +scale_size_manual(values =c("ASEAN"=3, "Other regions"=2)) +# Adding extra layers of geom_point so the ASEAN points can be on top of other regions' geom_point(data = plot4 |>filter(region =="ASEAN"),color ="red3",size =3) +geom_text(aes(label =case_when( rank <=2~ country, rank >=9~ country,TRUE~"")),nudge_y =-0.025) +ggtitle("GDP per capita vs % of households with access to\nsafely managed drinking water services") +labs(x ="GDP per capita", y ="percentage",caption =str_wrap(plot4_caption, width =95)) +scale_x_continuous(labels = scales::dollar,trans ="log10") +scale_y_continuous(labels = scales::percent) +theme_minimal()
Figure 4: GDP per capita vs % of households with access to safely managed drinking water services
Table 2 attempts to take a glimpse on how all regions and countries progress towards the ambitious SDG target 6.1 and target 6.2. By region average, ASEAN countries place 3rd on providing safely managed drinking water service level. Figure 4 shows an even stronger correlation between GDP per capita and safely drinking water service level, and it appears the countries able to provide all households with safely drinking water service have minimum GDP per capita of US$ 18,390.
Further discussions
As it is established that the economic health of a country — represented by GDP per capita for the scope of this study — strongly correlates with drinking water service level, it is pivotal for all countries to maintain a healthy GDP growth to ensure their ability to achieve universal coverage of drinking water. The percentage of GDP allocated to improve drinking water service level may vary depends on the countries’ ambition for the goal, as providing safely managed drinking water services as targeted in the SDGs will be significantly more expensive than providing just the basic access as targeted in the MDGs[2]. Projections indicate that most countries in the Asia-Pacific region (including ASEAN countries) will need to allocate between 1-2% of GDP on water supply and sanitation infrastructure over the period of 2015-2030[3]. Based on the findings of this study, Myanmar and Cambodia will need to allocate greater share of their GDP to catch up with other countries in the ASEAN region.
Another important thing to note is to maintain the quality of open water sources as it intrinsically links to health and economic production of a country, and thus affects the country’s GDP. Research shows that when rivers in middle income countries become moderately polluted (BOD level between 2-8 mg/L), GDP growth is reduced by 1.76%, and when they become heavily polluted (BOD level >8 mg/L), GDP growth is reduced by 2.5%[4].
There are various GDP allocation strategy that countries can implement to achieve universal coverage of drinking water, including but not limited to (a) focusing on investments to extend access to unserved population and (b) focusing on investments on improving access level of the population with only access to unimproved and limited drinking water services. Future analyses should look into how the difference on strategies implemented in each country correlates on drinking water service level in each country.
Conclusions
From this study, we can conclude that:
On the regional level, ASEAN region ranks 6th of all regions in level of access to improved drinking water service, with the service level being 94.9% of its households. To break into more details based on the JMP drinking water ladder:
39.8% of ASEAN households already have access to safely managed drinking water service (ranks 3rd of all regions),
54.3% of ASEAN households have access to basic drinking water service,
and 0.8% of households have access to limited drinking water service.
Households in Singapore and Malaysia have the highest access to safely managed drinking water service in the ASEAN region, with service level being 99% and 93.9%, respectively.
A strong, positive correlation exists between ASEAN and other countries’ access level to improved drinking water service and their GDP per capita.
A positive, yet even stronger correlation exists between ASEAN and other countries’ access level to safely managed drinking water service and their GDP per capita.
References
1. Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ane.0000000000002864
2. Rozenberg, J., & Fay, M. (2019). Beyond the gap: How countries can afford the infrastructure they need while protecting the planet. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-1363-4
4. Russ, J., Zaveri, E., Desbureaux, S., Damania, R., & Rodella, A.-S. (2022). The impact of water quality on GDP growth: Evidence from around the world. Water Security, 17, 100130. https://doi.org/10.1016/j.wasec.2022.100130
@online{rafif wijayarso2024,
author = {Rafif Wijayarso, Adnan},
title = {Drinking {Water} {Service} {Level} and {Gross} {Domestic}
{Product} {(GDP)} Per Capita in {ASEAN} {Countries:} {A}
{Comparative} {Study}},
date = {2024-02-13},
url = {https://ds4owd-001.github.io/project-adnanwijayarso/},
langid = {en},
abstract = {The current Eastern and South-Eastern Asia region defined
by the United Nations (UN) to report the progress towards
Sustainable Development Goals (SDGs) target contains two contrasting
groups of countries, in terms of income — therefore it is justified
to emphasize the study on ASEAN countries as they may need to narrow
the gap in drinking water service level compared to higher income
countries. The study finds that the ASEAN region ranks 6th of all
regions in level of access to improved drinking water service
(94.9\% of households served) and 3rd of all regions in level of
access to safely managed drinking water service (39.8\% of
households served). Households in Singapore and Malaysia have the
highest access to safely managed drinking water service in the ASEAN
region, with service level being 99\% and 93.9\%, respectively. A
strong, positive correlation (Spearman’s correlation coefficient ρ =
0.72, p-value = 0.000) exists between ASEAN and other countries’
access level to improved drinking water service and their GDP per
capita. An even stronger correlation (Spearman’s correlation
coefficient ρ = 0.88, p-value = 0.000) exists between ASEAN and
other countries’ access level to safely managed drinking water
service and their GDP per capita.}
}
For attribution, please cite this work as:
1. Rafif Wijayarso, A. (2024, February 13). Drinking Water Service
Level and Gross Domestic Product (GDP) per capita in ASEAN Countries: A
Comparative Study. https://ds4owd-001.github.io/project-adnanwijayarso/
