neutral measure of geographical distance, and obviously the lived experience of dispersion (or remoteness) is likely to be more impactful in Tuvalu than Fiji, given is vastly smaller population. Our dispersion-adjusted measures of population sparsity reflect this perspective. FSM and Kiribati stand out as being extremely sparsely distributed (with the sparsity of settlement patterns equivalent to 1 person per 20 km<sup>2</sup> ), as is Tuvalu (1 person per 5 km<sup>2</sup> ). Sparsity for the populations of Tonga, Solomon Islands and Vanuatu is somewhat more moderate (around 1-2 people per km<sup>2</sup> ), while the population of Fiji is by far the most highly concentrated across geographical space (16 people per km<sup>2</sup> ).

We also exploit the World Bank’s subnational geography database to estimate the geographical scale of population dispersion and dispersion-adjusted population sparsity for 110 other countries around the world. This exercise highlights the extreme degree of population sparsity in the PICs, relative to all other countries assessed. Across the global database, the geographical scale of population dispersion ranges from a maximum of 3,600km in the Russian Federation, to a minimum of 40km in Trinidad and Tobago. None of the PICs lie at the extremes of this range, but dispersion for FSM and Kiribati is at the higher end of the distribution, while Fiji and Tonga are at the lower end. On the other hand, after accounting for population size, the PICs stand out as having extremely low levels of dispersion-adjusted population density compared to other countries, and equivalently, an extremely high degree of population sparsity **.** This note proceeds as follows. In the first section we define our three key indicators which reflect, respectively: local-area community-level remoteness; the geographical scale of population dispersion across a country; and, country-wide, dispersion-adjusted population density, or inversely sparsity. The second section presents empirical estimates for each of the PICs and compares the results of our new measures to other spatial geography-related indicators. The third section contextualizes the results for the PICs in a broader global context, capitalizing on data available for countries included in the World Bank’s sub-national geographic database. A final section describes some extensions to our benchmark work and concludes.

# 1. Indicator definitions

Population settlement patterns vary considerably across countries, and sometimes in nuanced ways which are difficult to capture in a simple, empirical indicator. This section presents three related indicators which reflect variation in population settlement patterns: (i) an indicator of community remoteness within a country context (a local-area-level indicator), (ii) an indicator of the geographical scale of population dispersion (a country-level indicator), and (iii) an indicator of dispersion-adjusted population density, or inversely, sparsity (a country-level indicator).

These indicators are designed with practical evaluation in mind. They can be calculated using commonly available geo-coded population census data, disaggregated at a local-area level; e.g. by enumeration area, or jurisdictional district areas. In theory, all three of our indicators can be rooted in a conceptualization of remoteness at the individual level and evaluating the indicators at the individual-level would yield the most accurate picture of the depth and severity of remoteness of settlements across the country. However, census data are rarely made available geo-coded at the individual level (i.e. indicating where each individual in the country lives) for obvious reasons. As such, we focus the definitions and discussion below on indicator definitions at the more applicable, community level (which effectively assumes that all individuals in a local area are located at the centroid of that area). For readability, we use the term 3