This post is the fourth of a series of (probably) seven on population issues in the Pacific, regenerating the graphs I used in a keynote speech before the Pacific Heads of Planning and Statistics meeting in November 2025 in Wellington, New Zealand. The seven pieces of the puzzle are:
Today’s post is all about creating this one striking graph, which compares the number of people in a country with its diaspora – people who identify ethnically or otherwise with the country but live abroad:
As the graph indicates, the diaspora figures are an underestimate, as I only used the New Zealand, Australian and US censuses, and only partially before that. For example, I decided that the number of Papua New Guineans living in New Zealand and the US was not material and that they were not included. I’m confident this doesn’t change the appearance of the graph, but if I were trying to make the best possible comprehensive estimates I would obviously have to include these.
It’s quite a dramatic story. We see seven countries where more people live abroad than in the country itself: Niue, Pitcairn Islands, Cook Islands, Tokelau, Samoa, Tonga and the Marshall Islands. Apart from the Marshall Islands, these are all Polynesian. In fact, Tuvalu is the only Polynesian country in this collection where more people live at home than abroad (for now, this is likely to change now that Australia has agreed with Tuvalu for a regular annual influx of people through lotteries).
Note that the three French territories (New Caledonia, Wallis and Futuna, and French Polynesia) and three American territories (American Samoa, Northern Mariana Islands, and Guam) are excluded from the plot.
For the four small countries in the bottom row of the graph, the difference is particularly important: a large majority of their population lives abroad. From my last post we know that many of these are in Auckland. Pitcairn is the only one of these four with more diaspora in Australia than in New Zealand (there are also Pitcairn-identifying people in Britain, but not enough to make me systematically add Britain to my data in what was essentially a pragmatic and visual exercise – see comments above).
96% of Niueans, 90% of Cook Islanders and 59% of Marshall Islanders live abroad.
And for the four countries at the top of the graph – slightly larger and clearly poorer than many of the others, and three of which are Melanesian – we see no significant diaspora relative to the home population.
Here is the code that creates this bar chart. Note that the data here is hand-entered (!!) from various sources – not something I would normally do, and would never recommend, except in these really “small data” situations. I have checked it as thoroughly as I reasonably can, and the version I used in my talk and am adapting here was also peer-reviewed by a colleague.
# This script draws some charts of the diaspora of Pacific island countries and territories.
# It's pretty rough and certainly incomplete. The approach was to use the census figures
# for resident population of Pacific islander ancestry currently living in USA, Australia
# and New Zealand; and compare that to populations resideing in the countries themselves.
#
# All sorts of known limitations which we are prepared to live with for these crude comparisons:
# - different reference years (2025 for populations, and census years are 2018, 2020 and 2021)
# - populations residing in the Pacific islands themselves are all ethnicities (e.g. will include
# Australian-descent people rsideing in those countries), haven't bothered to limit to just "true" Tongans, Samoans, etc
# - not comprehensive e.g. I know there are some Pitcairn-descended people in UK but haven't included them. And of course
# there must be many others of these people in countries other than Australia, NZ and USA
# - France not included at all. No ancestry data in French censuses so this would be tricky.
#
# Peter Ellis 2025-11
#---------------------Data prep-------------------------
library(tidyverse)
library(rsdmx)
library(ISOcodes)
# Current populations of PICTs:
pops <- rsdmx::readSDMX("https://stats-sdmx-disseminate.pacificdata.org/rest/data/SPC,DF_POP_PROJ,3.0/A..MIDYEARPOPEST._T._T?startPeriod=2025&endPeriod=2025&dimensionAtObservation=AllDimensions") |>
as_tibble() |>
left_join(select(ISOcodes::ISO_3166_1, Alpha_2, pict = Name), by = c("GEO_PICT" = "Alpha_2")) |>
select(pict, pop = obsValue) |>
drop_na()
# out of interest what is the total population of all PICTs, Austrlaia and NZ together? (about 47m):
picts_and_anz <- c(sum(pops$pop), 28.1e6, 5.3e6)
sum(picts_and_anz)
# https://tools.summaries.stats.govt.nz/ethnic-group/tongan and similar for 2023 NZ figure
# table builder for Australian 2021 figures - see `https://raw.githubusercontent.com/ellisp/blog-source/refs/heads/master/data/total%20by%20pacific.csv`
# Wikipedia for US figures, from 2020 census. Search for e.g. "Palauans in USA wikipedia"
diaspora <- tribble(~pict, ~dest, ~people,
"Tonga", "New Zealand", 97824,
"Niue", "New Zealand", 34944,
"Tokelau", "New Zealand", 9822,
"Cook Islands", "New Zealand", 94176,
"Samoa", "New Zealand", 213069,
"Tuvalu", "New Zealand", 6585,
"Fiji", "New Zealand", 25038 + 23808, # includes Fijian Indian
"Papua New Guinea", "Australia", 22668,
"Vanuatu", "Australia", 2380,
"Solomon Islands", "Australia", 2704,
"Kiribati", "Australia", 1263,
"Fiji", "Australia", 48354,
"Nauru", "Australia", 571,
"Cook Islands", "Australia", 27494,
"Tokelau", "Australia", 2544,
"Tonga", "Australia", 43469,
"Niue", "Australia", 6225,
"Samoa", "Australia", 98022,
"Tuvalu", "Australia", 995,
"Pitcairn", "Australia", 1123,
"Marshall Islands", "USA", 52624, # 47300 if just 'alone'
"Palau", "USA", 12202,
"Micronesia, Federated States of", "USA", 21596)
# Australia checked
# New Zealand checked
# USA checked
#--------------------------Bar chart------------------------
# data frame to inspect to get percentages.
pops_with_prop <- pops |>
inner_join(diaspora) |>
mutate(pict = gsub("Federated States of", "Fed. St.", pict)) |>
group_by(pict, pop) |>
summarise(Overseas = sum(people)) |>
ungroup() |>
mutate(prop = Overseas / (pop + Overseas)) |>
mutate(pict = fct_reorder(pict, prop))
pops_with_prop |>
select(-prop) |>
rename(`Origin country` = pop) |>
gather(variable, value, -pict) |>
ggplot(aes(x = variable, y = value, fill = variable)) +
geom_col(width = 0.8) +
facet_wrap(~pict, scales = "free_y") +
scale_y_continuous(label = comma) +
scale_fill_manual(values = c("steelblue", "brown")) +
theme(legend.position = "none",
panel.spacing = unit(2, "lines"),
plot.caption = element_text(colour = "grey50")) +
labs(x = "", y ="Number of people",
title = "Pacific Islander diaspora, arranged from lowest proportion overseas to highest",
subtitle = "Diaspora is a lower bound of full figure as it is based on just Australia, USA and New Zealand censuses.",
caption = "Source: PDH.Stat for populations; Australian, USA and New Zealand Censuses for diaspora.")An alternative (not so good) visualization
I used the same data to create this scatter chart as well:
But I don’t like it that much. It is difficult to interpret, and although it contains some additional information (which country the diaspora is in), this does not outweigh the problems of interpretation. it probably shouldn’t have a log scale, because we really want to add up the numbers; but using an untransformed scale makes it even more of a visual mess. I’m just including it here for the record and to illustrate that the first attempt at visualizing something isn’t always the best (and sometimes a modest bar chart is just what you want). Here is the code for the scatterplot:
pops |>
inner_join(diaspora) |>
ggplot(aes(x = pop, y = people, label = pict, colour = dest)) +
geom_abline(slope = 1, intercept = 0, colour = "grey") +
geom_point() +
geom_text_repel(size = 2.5) +
scale_x_log10(label = comma) +
scale_y_log10(label = comma) +
scale_colour_manual(values = c("blue", "black", "darkred")) +
labs(x = "People living in origin country in 2025",
y = "Diaspora in another country, recent census",
colour = "Disapora country",
title = "Pacific Island home population and diaspora in various countries")Related
#Pacific #Diaspora #ellis2013nz #bloggers