A recently published paper, titled “Coastal proximity of populations in 22 Pacific Island Countries and Territories” details the methodology used to undertake the analysis and presents the findings. Purpose * This analysis aims to estimate populations settled in coastal areas in 22 Pacific Island Countries and Territories (PICTS) using the data currently available. In addition to the coastal population estimates, the study compares the results obtained from the use of national population datasets (census) with those derived from the use of global population grids. * Accuracy and reliability from national and global datasets derived results have been evaluated to identify the most suitable options to estimate size and location of coastal populations in the region. A collaborative project between the Pacific Community (SPC), WorldFish and the University of Wollongong has produced the first detailed population estimates of people living close to the coast in the 22 Pacific Island Countries and Territories (PICTs).

In 2024, Papua New Guinea had the largest population of the Pacific Island nations, with around *** million people. The second-highest population in the region was Fiji, with just over *** thousand people.

The Vatican City, often called the Holy See, has the smallest population worldwide, with only *** inhabitants. It is also the smallest country in the world by size. The islands Niue, Tuvalu, and Nauru followed in the next three positions. On the other hand, India is the most populous country in the world, with over *** billion inhabitants.

Proportion of population living in 1, 5 and 10km buffer zones for Pacific Island Countries and Territories, determined using most recent Population and Housing Census. Number of people living in 1,5 and 10km buffer zones determined by apportioning population projections.

Find more Pacific data on PDH.stat.

A collaborative project between SPC, the World Fish Centre and the University of Wollongong has produced the first detailed population estimates of people living close to the coast in the 22 Pacific Island Countries and Territories (PICTs). These estimates are stratified into 1, 5, and 10km zones. More information about this dataset: https://sdd.spc.int/mapping-coastal

In 2021, Fiji heritage was the most common cultural group of Pacific Island heritage living in Australia. The number of people of Fiji heritage in the country reached ***** thousand that year. In comparison, the number of individuals of Samoa heritage in Australia was approximately ** thousand in the same year.

Monaco is the country with the highest median age in the world. The population has a median age of around 57 years, which is around six years more than in Japan and Saint Pierre and Miquelon – the other countries that make up the top three. Southern European countries make up a large part of the top 20, with Italy, Slovenia, Greece, San Marino, Andorra, and Croatia all making the list. Low infant mortality means higher life expectancy Monaco and Japan also have the lowest infant mortality rates in the world, which contributes to the calculation of a higher life expectancy because fewer people are dying in the first years of life. Indeed, many of the nations with a high median age also feature on the list of countries with the highest average life expectancy, such as San Marino, Japan, Italy, and Lichtenstein. Demographics of islands and small countries Many smaller countries and island nations have populations with a high median age, such as Guernsey and the Isle of Man, which are both island territories within the British Isles. An explanation for this could be that younger people leave to seek work or education opportunities, while others choose to relocate there for retirement.

Proportion of population in Pacific Island Countries and Territories (PICTs) living in Low Elevation Coastal Zones (LECZ) of 0-10 and 0-20 meters above sea level. LECZ were delineated using the bathub method overlaid on the Advanced Land Observing Satellite (ALOS) Global Digital Surface Model (AW3D30). Populations within the LECZs were estimated using the Pacific Community (SPC) Statistics for Development Division’s 100m2 population grids.

Find more Pacific data on PDH.stat.

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

SM.POP.TOTL. International migrant stock is the number of people born in a country other than that in which they live. It also includes refugees. The data used to estimate the international migrant stock at a particular time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population--people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population--that is, people who are citizens of a country other than the country in which they reside--are used as estimates. After the breakup of the Soviet Union in 1991 people living in one of the newly independent countries who were born in another were classified as international migrants. Estimates of migrant stock in the newly independent states from 1990 on are based on the 1989 census of the Soviet Union. For countries with information on the international migrant stock for at least two points in time, interpolation or extrapolation was used to estimate the international migrant stock on July 1 of the reference years. For countries with only one observation, estimates for the reference years were derived using rates of change in the migrant stock in the years preceding or following the single observation available. A model was used to estimate migrants for countries that had no data. The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

Marshall Islands MH: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.300 % in 2019. Marshall Islands MH: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 11.300 % from Dec 2019 (Median) to 2019, with 1 observations. The data reached an all-time high of 11.300 % in 2019 and a record low of 11.300 % in 2019. Marshall Islands MH: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Marshall Islands – Table MH.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

The autonomous community of the Balearic Islands is comprised of four main islands – the largest and most populous of which is Mallorca, which had a population of over ******* inhabitants as of January 2025. Meanwhile, the second island on the list, Ibiza, was home to roughly ******* inhabitants. With its crystal-clear beaches, the autonomous community of the Balearic Islands attracts millions of domestic and international visitors each year. Which Balearic Island receives the most tourists? ******** received the lion's share of tourist arrivals in the Balearic Islands in 2022. That year, nearly ** percent of tourists in the Balearic Islands visited Mallorca, and this figure remained relatively consistent with the previous three years. The island is a particularly popular travel destination for Germans. In 2022, the number of German tourist arrivals in Mallorca was *** million. How many tourists visit Spain each year? Spain ranked ****** on the World Tourism Organization’s list of most visited countries in the world in 2023, with ** million foreigners having visited that year. The Mediterranean country is also one of Europe’s favorite holiday destinations. **************** and the ************** were some of the leading countries to visit Spain in 2023. That year, over ** million tourists came from the United Kingdom alone.

Faroe Islands FO: Urban Population: % of Total Population data was reported at 41.914 % in 2017. This records an increase from the previous number of 41.777 % for 2016. Faroe Islands FO: Urban Population: % of Total Population data is updated yearly, averaging 31.228 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 41.914 % in 2017 and a record low of 21.383 % in 1960. Faroe Islands FO: Urban Population: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Faroe Islands – Table FO.World Bank.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.; ; United Nations Population Division. World Urbanization Prospects: 2018 Revision.; Weighted average;

Abstract

Census of population and housing refers to the entire process of collecting, compiling, evaluating, analyzing, and publishing data about the population and the living quarters in a country. It entails the listing and recording of the characteristics of each individual person and each living quarter as of a specified time and within a specified territory. It is the source of information on the size and distribution of the population as well as its demographic, social, economic, and cultural characteristics. These information are vital for making rational plans and programs for national and local development.

The 2011 Census of Population and Housing, conducted in April 2011, was designed to take an inventory of the total population and housing units in the RMI and to collect information about their characteristics. The census of population is the source of information on the size and distribution of the population as well as information about the demographic, social, economic and cultural characteristics. The census of housing, on the other hand, provides information on the supply of housing units, their structural characteristics and facilities which have bearing on the maintenance of privacy, health and the development of normal family living conditions. These information are vital for making rational plans and programs for social and economic development.

Geographic coverage

National Coverage

Analysis unit

Individual Household

Universe

All de jure population of the Republic of the Marshall Islands on Census day.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data editing for the 2011 RMI Census used four phases of editing. The first phase of the data editing was the control phase which control clerks checked for completeness of the questionnaire. During this phase, items were verified by contacting the respondents either by phone or by home visit. The countries took advantage of enumerators still on the field to complete any missing information especially those pertaining to the head of the household, education and fertility questions.

The second phase of data editing was completed during data entry on items that had responses in places where no responses was expected and vice versa. Any information that was missing or incomplete in the questionnaire was substituted with a special code and keyed into the computer. Other than corrections to age, sex to name association and skip patterns no other information was edited during this phase.

The third phase utilized a standardized editing method called dynamic imputation. The method imputes missing or invalid items in the questionnaire with a person in the same geographical region that displays similar characteristics. The method used an approach called top-down to prevent circular and over editing of data.

The fourth phase was more of a quality control issue and refinements to the data edits. This was normally done with the production of tables and the interaction of subject-matter specialist.

Worldwide, the male population is slightly higher than the female population, although this varies by country. As of 2023, Hong Kong has the highest share of women worldwide with almost ** percent. Moldova followed behind with ** percent. Among the countries with the largest share of women in the total population, several were former Soviet-states or were located in Eastern Europe. By contrast, Qatar, the United Arab Emirates, and Oman had some of the highest proportions of men in their populations.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.

Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

"People Displaced" refers to the number of people living in displacement as of the end of each year.

"New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.

Contains data from IDMC's Global Internal Displacement Database.

Abstract

In 2010, the EU-SILC instrument covered 32 countries, that is, all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 6th version of the 2010 Cross-Sectional User Database as released in July 2015 is documented here.

Geographic coverage

The survey covers following countries: Austria; Belgium; Bulgaria; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Spain; Ireland; Italy; Latvia; Lithuania; Luxembourg; Hungary; Malta; Netherlands; Poland; Portugal; Romania; Slovenia; Slovakia; Sweden; United Kingdom; Iceland; Norway; Turkey; Switzerland

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

Solomon Islands SB: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 84.700 % in 2013. This records a decrease from the previous number of 87.300 % for 2005. Solomon Islands SB: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 86.000 % from Dec 2005 (Median) to 2013, with 2 observations. The data reached an all-time high of 87.300 % in 2005 and a record low of 84.700 % in 2013. Solomon Islands SB: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Solomon Islands – Table SB.World Bank.WDI: Poverty. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The main purpose of a HIES survey was to present high quality and representative national household data on income and expenditure in order to update Consumer Price Index (CPI), improve statistics on National Accounts and measure poverty within the country. These statistics are a requirement for evidence based policy-making in reducing poverty within the country and monitor progress in the national strategic plan "Te Kakeega 3".

The 2015-16 Household Income and Expenditure Survey (HIES) is the third HIES that was conducted by the Central Statistics Division since Tuvalu gained political independence in 1978. With great assitance from the Pacific Community (SPC) experts, the HIES was conducted over a period of 12 months in urban (Funafuti) and rural (4 outer islands) areas. From a total of 1,872 households on Tuvalu, an amount of 38 percent sample of all households in Tuvalu was selected to provide valid response.

Geographic coverage

National Coverage.

Analysis unit

Household and Individual.

Universe

The scope of the 2015/2016 Household Income and Expenditure Survey (HIES) was all occupied households in Tuvalu. Households are the sampling unit, defined as a group of people (related or not) who pool their money, and cook and eat together. It is not the physical structure (dwelling) in which people live. HIES covered all persons who were considered to be usual residents of private dwellings (must have been living in Tuvalu for a period of 12-months, or have intention to live in Tuvalu for a period of 12-months in order to be included in the survey). Usual residents who are temporary away are included as well (e.g., for work or a holiday).

Kind of data

Sample survey data [ssd]

Sampling procedure

Out of the total 1,872 households (HHs) listed in 2015, a sample 706 households which is 38 percent of the the total households were succesfully interviewed for a response rate of 98%.

SAMPLING FRAME: The 2010 (Household Income and Expenditure Survey (HIES) sample was spread over 12 months rounds - one each quarter - and the specifications of the final responding households are summarised below: Tuvalu urban: Selected households: 259 = 217 responded; Tuvalu rural: Selected households: 346 = 324 responded.

In 2010, 605 HHs were selected and 541 sufficiently responded. The 2010 HIES provided solid estimates for expenditure aggregates at the national level (sampling error for national expenditure estimate is 3.1%).

Similarly to the 2010 HIES, private occupied dwellings were the statistical unit for the 2015/2016 HIES. Institutions and vacant dwellings were removed from the sampling frame. Some areas in Tuvalu are very difficult to reach due to the cost of transportation and the remoteness of some islands, which is why they are excluded from the sample selection. The following table presents the distribution of the HHs according to their location (main island or outer islands in each domain) based on the 2012 Population and Housing Census: -Urban - Funafuti: 845 (48%); -Rural - Nanumea: 115 (7%); -Rural - Nanumaga: 116 (7%); -Rural - Niutao: 123 (7%); -Rural - Nui: 138 (8%); -Rural - Vaitupu: 226 (13%); -Rural - Nukufetau: 124 (%); -Rural - Nukulaelae: 67 (%); -Rural - Niulakita: 7 (%); -TOTAL: 1761 (100%).

The 2012 Population and Household Census (PHC) wsa used to select the island to interview, and then in each selected island the HH listing was updated for selection. For budget and logistics reasons the islands of Nui, Nukufetau, Nukulaelae and Niukalita were excluded from the sample selection. In total 19% of the HHs were excluded from the selection but this decision should not affect the HIES outputs as those 19% show similar profile as other HHs who live in the outer islands. This exclusion will be take into consideration in the sampling weight computation in order to cover 100% of the outer island HHs.

SAMPLE SELECTION AND SAMPLE SIZE: A simple random selection was used in each of the selected island (HHs were selected directly from the sampling frame). Based on the findings from the 2010 Tuvalu HIES, the sample in Funafuti has been increased and the one in rural remains stable. Within each rural selected atolls, the allocation of the sample size is proportional to its size (baed on the 2012 population census). The table below shows the number of HHs to survey: Urban - Funafuti: 384; Rural - Vaitupu: 126; Rural - Nanumea: 63; Rural - Niutao: 84; Rural - Nanumaga: 63; TUVALU: 720.

The expected sample size has been increased by one third (361 HHs) with the aim of pre-empting the non contacted HHs (refusals, absence….). The 2015/2016 HIES adopted the standardized HIES methodology and survey instruments for the Pacific Islands region. This approach, developed by the Pacific Community (SPC), has resulted in proven survey forms being used for data collection. It involves collection of data over a 12-month period to account for seasonal changes in income and expenditure patterns, and to keep the field team to a smaller and more qualified group. Their implementation had the objective of producing consistent and high quality data.

Sampling deviation

For budget and logistics reasons the islands of Nui, Nukufetau, Nukulaelae and Niukalita were excluded from the sample selection. In total 19% of the HHs were excluded from the selection but this decision should not affect the HIES outputs as those 19% show similar profile as other HHs who live in the outer islands. This exclusion will be take into consideration in the sampling weight computation in order to cover 100% of the outer island HHs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey contain 4 modules and 2 Diaries (1 diary for each of the two weeks that a household was enumerated). The purpose of a Diary is to record all the daily expenses and incomes of a Household as shown by its topics below; - DIARY
The Diary module contains questions such as "What did your Household buy Today (Food and Non-Food Items)?", "Payments for Services made Today", "Food, Non-Food and Services Received for Free", "Home-Produced Items Today", "Overflow Sheet for Items Bought This Week", "Overflow Sheet for Services Paid for This Week", "Overflow Sheet for Items Received for Free this Week", and an "Overflow Sheet for Home-Produced Items This Week".

The 4 modules are detailed below; - MODULE 1 - DEMOGRAPHIC INFORMATION The module contains individual demograhic questions on their Demographic Profiles, Labour Force status (Activities), Education status, Health status, Communication status and questions on "Household members that have left the household". - MODULE 2 - HOUSEHOLD EXPENDITURE The module contains household expenditure questions the housing characteristics, Housing tenure expenditures, Utilities and Communication, Land, Household goods and assets, Vehicles and accessories, Private Travel details, Household services expenditures, Cash contributions, Provisions of Financial support, Household asset insurance and taxes and questions on Personal insurance. - MODULE 3 - INDIVIDUAL EXPENDITURE This module contains individual expenditure questions on Education, Health, Clothing, Communication, Luxury Items, Alcohol, Kava and Tobacco, and Deprivation questions. - MODULE 4 - HOUSEHOLD & INDIVIDUAL INCOME
This module contains household and individual questions on their income, on topics such as Wages and Salary, Agricultural and Forestry Activities, Fishing, Gathering and Hunting Activities, Livestock and Aquaculture Activities, Handicraft/Home-processed Food Activities, Income from Non-subsistence Business, Property income, transfer income & other Receipts, and Remmitances and other Cash gifts.

Depending on the information being collected, a recall period (ranging from the last 7 days to the last 12 months) is applied to various sections of the questionnaire. The forms were completed by face-to-face interview, usually with the HH head providing most of the information, with other household (HH) members being interviewed when necessary. The interviews took place over a 2-week period such that the HH diary, which is completed by the HH on a daily basis for 2 weeks, can be monitored while the module interviews take place. The HH diary collects information on the HH's daily expenditure on goods and services; and the harvest, capture, collection or slaughter of primary produce (fruit, vegetables and animals) by intended purpose (home consumption, sale or to give away). The income and expenditure data from the modules and the diary are concatenated (ensuring that double counting does not occur), annualised, and extrapolated to form the income and expenditure aggregates presented herein.

Cleaning operations

The survey procedure and enumeration team structure allowed for in-round data entry, which gives the field staff the opportunity to correct the data by manual review and by using the entry system-generated error messages. This process was designed to improve data quality. The data entry system used system-controlled entry, interactive coding and validity and consistency checks. Despite the validity and consistency checks put in place, the data still required cleaning. The cleaning was a 2-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database, consisting of: 1. Person level record - characteristics of every HH member, including activity