Source document: https://docs-lawep.s3.us-east-2.amazonaws.com/1696591324465.pdf (Vanuatu)
VANUATU NATIONAL STATISTICS OffICE BUREAU NATlONAl DES STATISnaUfS 2009 National Population and Housing Census Analytical Report Volume 2 Vanuatu Bern i K aontern Yu Contents PREFACE .................................................................................................................................. v FOREWORD ............................................................................................................................ vi SUMMARY OF MAIN INDICATORS .................................................................................. vii EXECUTIVE SUMMARY ....................................................................................................... x MAP OF VANUATU .............................................................................................................. xv 1.
INTRODUCTION ................................................................................................................. 1 1.1 Geographic setting ........................................................................................................... 1 1.2 Background to report ....................................................................................................... 1 2.
POPULATION SIZE, TREND, DISTRIBUTION, STRUCTURE and URBANIZATION 2 2.1 Population size and trend ................................................................................................. 2 2.2 Population distribution ..................................................................................................... 6 2.3 Population density ............................................................................................................ 7 2.4 Population structure ......................................................................................................... 9 2.5 Urbanization ................................................................................................................... 18 2.5.1 Urban growth .......................................................................................................... 19 2.5.2 Process of urbanization ........................................................................................... 20 2.5.3 Degree of urbanization ............................................................................................ 20 2.5.4 Tempo of urbanization ............................................................................................ 22 3.
DEMOGRAPHIC COMPONENTS .................................................................................... 24 3.1 Fertility ........................................................................................................................... 24 3.1.1 National estimates ................................................................................................... 24 3.1.2 Sub national estimates............................................................................................. 32 3.1.3 Proximate determinants of fertility ......................................................................... 34 3.2 Mortality ........................................................................................................................ 40 3.2.1 National level estimates .......................................................................................... 40 3.2.2 Sub national estimates............................................................................................. 53 3.2.3 Proximate determinants of mortality ...................................................................... 58 3.3 Migration........................................................................................................................ 61 3.3.1 Internal migration .................................................................................................... 61 3.3.2 International migration ............................................................................................ 68 4.
SOCIAL CHARACTERISTICS .......................................................................................... 70 4.1 Marital status .................................................................................................................. 70 4.2 Religion .......................................................................................................................... 76 4.3 Ethnic origin................................................................................................................... 78 4.4 Health ............................................................................................................................. 80 ii 4.4.1 Disability ................................................................................................................. 80 4.4.2 Smoking and drinking habits .................................................................................. 84 4.5 Educational characteristics ............................................................................................. 89 4.5.1 School enrollment ................................................................................................... 89 4.5.2 Educational attainment ............................................................................................ 93 4.5.3 Literacy and language ability .................................................................................. 95 4.6 Internet use ................................................................................................................... 100 4.7 Labour market activity ................................................................................................. 101 4.7.1 Introduction ........................................................................................................... 101 4.7.2 Economic activity ................................................................................................. 103 4.7.3 Labour force participation rate, employment–population ratio, and unemployment rate.................................................................................................................................. 108 4.7.4 Employment status ................................................................................................ 117 4.7.5 Employed population by industry group ............................................................... 118 4.7.6 Employed population by occupational group ....................................................... 120 4.7.7 Population working overseas ................................................................................ 122 5.
HOUSEHOLD CHARACTERISTICS .............................................................................. 124 5.1 Household size ............................................................................................................. 124 5.2 Household composition ............................................................................................... 127 5.3 Household language ..................................................................................................... 127 5.4 Household income ....................................................................................................... 128 5.5 Amenities and capital goods ........................................................................................ 130 5.5.1 Private households by housing and land tenure .................................................... 130 5.5.2 Private households by living quarters ................................................................... 131 5.5.3 Private households by age of dwelling ................................................................. 132 5.5.4 Private households by number of rooms ............................................................... 133 5.5.5 Private households by construction material used for dwelling ........................... 134 5.5.6 Private households by water source for drinking and washing ............................. 136 5.5.7 Private households by main energy source for lighting and cooking ................... 138 5.5.8 Private households by main toilet facility ............................................................. 139 5.5.9 Private households by means of communication .................................................. 140 5.5.10 Private households by main means of waste disposal ........................................ 142 5.5.11 Private households involved in agricultural cash crop and fisheries activities ... 143 5.5.12 Private households owning or raising livestock .................................................. 147 5.5.13 Private households and availability of various household items ........................ 150 5.6 Household bednets ....................................................................................................... 159 6.
POPULATION PROJECTIONS ....................................................................................... 160 iii 6.1 National projections ..................................................................................................... 160 6.2 Sub-national projections .............................................................................................. 176 7.
IMPLICATIONS OF DEMOGRAPHIC TRENDS .......................................................... 186 7.1 Population dynamics .................................................................................................... 186 7.1.1 Growth rate ........................................................................................................... 186 7.1.2 Fertility .................................................................................................................. 186 7.1.3 Mortality ............................................................................................................... 187 7.1.4 Internal migration .................................................................................................. 187 7.1.5 International migration .......................................................................................... 188 7.1.6 Population projections .......................................................................................... 188 7.2 Crosscutting issues ....................................................................................................... 189 7.2.1 Vital statistics ........................................................................................................ 189 7.2.2 The environment ................................................................................................... 189 7.2.3 Households ............................................................................................................ 190 7.2.4 Health services and well-being ............................................................................. 190 7.2.5 Disabilities ............................................................................................................ 192 7.2.6 Education .............................................................................................................. 192 7.2.7 Economic activity and labour market ................................................................... 193 7.2.8 Communication and
 internet use .......................................................................... 194 7.2.9 Good governance .................................................................................................. 194 GLOSSARY .......................................................................................................................... 195 APPENDICES ....................................................................................................................... 197 iv Honorable Moana Carcasses Calosil Minister of Finance and Economic Management Republic of Vanuatu May, 2011 SUMMARY OF MAIN INDICATORS Vanuatu Urban Rural Torba Sanma Penama Malampa Shefa Tafea 234,023 119,091 114,932 57,195 29,618 27,577 176,828 89,473 87,355 2.3 19 57,195 24.4 3.5 3.5 1.9 - - - - - - 9,359 4,727 4,632 1.9 11 - - - 45,855 23,623 22,232 2.4 11 13,156 28.7 2.0 30,819 15,543 15,276 1.5 26 36,727 18,446 18,281 1.2 13 - - - - - - 78,723 40,550 38,173 3.7 52 44,039 55.9 4.1 32,540 16,202 16,338 1.1 20 - - - 47,373 11,606 35,767 1,766 9,213 6,620 7,991 15,930 5,853 Indicator Total population Males Females Average annual population growth people/km2) Urbanization Urban population Per cent urban (%) Urban growth rate (%) Households Number of private households Average household size (number of people per household) Number of institutions Population structure Number of children (<15 years) 4.8 209 4.8 73 4.8 136 90,973 18,065 72,908 Youth population (15-24 years) 45,423 13,646 31,777 Population aged 25-59 years 83,821 23,380 60,441 Older population (60 years and 13,806 2,104 11,702 Median age Dependency ratio (15-59) Sex ratio Mean age at first marriage (SMAM) Males Females Labour force Employed population (number) Males Females Subsistence workers (number) Males Females Unemployed (number) Males Females Labour force participation rate Males Females Employment–population ratio Males Females Unemployment rate (%) Males Females 20.5 81 104 23.9 25.5 22.5 42,295 25,916 16,379 41,877 21,942 19,935
vii SUMMARY OF MAIN INDICATORS (continued) Indicator Vanuatu Urban Rural Torba Sanma Penama Malampa Shefa Tafea Education School enrolment rates of 6–13 year- olds (%) Males Females Proportion (%) of population aged 15 and older with: No education (never been to Males Females Primary education Males Females Secondary education Males Females Tertiary education Males Females Vocational/professional Males Females Literacy rate (15+) Males Females Literacy rate (15-24) Males Females Language ability (as % of population 5 years and older) English French Bislama Other (local) language Internet use (as % of population 15 years and older) Total Males Females Disability (number of people) Blindness Males Females Deafness Males Females Lameness Males Females Senile and/or amnesic Males Females 85.9 85.7
Senile and/or amnesic Males Females viii SUMMARY OF MAIN INDICATORS (continued) Indicator Vanuatu Urban Rural Torba Sanma Penama Malampa Shefa Tafea Fertility Total Fertility Rate (TFR) Teenage Fertility Rate (ASFR, 15-19) Average number of children ever born to women aged 45-49 General Fertility Rate (GFR) Child-Woman Ratio (CWR) Mean age at childbearing of mothers (in years) Mean age at childbearing of fathers (in years) Annual number of births, 2009 Crude Birth Rate (CBR) Mortality Proportion of children ever born still alive (%) Males Females Proportion of population 60 years and older widowed (%) Males Females Proportion of population orphaned Fathers dead Mothers dead Infant mortality rate (IMR)
 Males Females Child mortality Males Females Under-five mortality Males Females Life expectancy at age 20 (e20) Males Females Life expectancy at birth (e0) Males Females - - Estimated annual number of deaths, Crude death rate (CDR) 1,260 5.4 Migration Annual net migrants Annual net migration rate 0 0.0 - - -60 -0.6 0 0.0 -260 -0.8 -400 -1.0 1,370 1.6 -650 -1.9 ix EXECUTIVE SUMMARY The aim of this report is to provide an analysis of the 2009 Vanuatu population census data with a strong emphasis on demographic trends, patterns and levels.
The 2009 census determined that the total population was 234,023.

This compares with 186,678 people in 1999, and represents an increase of 25.4% or 47,345 people.

This population increase represents an average annual growth rate of 2.3%, or an increase of 4,733 people per year.

The 2009 census enumerated 119,091 males and 114,932 females, representing a sex ratio of 104 males per 100 females.
The urban population was 57,195 people (24.4% of the total population), and includes the towns of Luganville in Sanma with 13,156 people, and Port Vila in the Shefa province with 44,039 people.

The average population density for Vanuatu was 19 people/km².

This varies widely between provinces.

For example, Shefa had 52 people/km², while Torba and Sanma had only 11people/km².

The census counted 47,373 private households with 228,883 household members, which represents 4.8 people per household on average.
More than 10% (25,451) of all people that live in private households live in households with 10 or more people.

The 2009 census data show a net flow of people towards Shefa province from all other provinces during the 5-year period 2004–2009.

However, the provinces Penama and Malampa lost the most people due to internal migration.

Vanuatu has a young population with a median age of 20.5 years.
More than one-third (39%) of the population was younger than 15 years of age, and only 6% were 60 years and older.

The age dependency ratio was calculated using the 15–59 year-old age group as the “working age population”.

For every 100 people of working age, 81 were in the age dependent category.

The number of births was estimated at 7,335 in 2009.

This accounts for a crude birth rate (CBR) of 31.3 per 1000.
The total fertility rate (TFR) — the average number of births per woman — declined from about 4.6 in 1999 to about 4.1 in 2009.

Based on census data for the number of children ever born and still alive, the infant mortality rate (IMR) was estimated at 21; 22 for males and 19 for females.

This estimate is lower than the x 1999 levels when the IMR was 27 and 26 for males and females – and is thus an improvement in infant mortality rates.
Based on the 2009 census data, life expectancies at birth were estimated to be 69.6 and 72.7 years for males and females, respectively, representing an increase compared to 1999 when it was 65.6 and 69.0 years for males and females.

Based on the derived life tables, a crude death rate (CDR) of 5.4 per 1,000 was calculated, which were approximately 1,260 deaths in 2009.
The estimated mortality indicators show more positive mortality indicators for females than for males, with females expected to live, on average, about three years longer than males.

Internal migration during the five year period 2004-2009 was primarily directed towards Shefa province and specifically to the capital Port Vila.

The largest numbers of migrants came from Tafea, Malampa and Penama.

Net international migration is estimated to be negligible during the intercensal period 1999– 2009.
Women marry at a younger age than men.

The average age at marriage was 25.5 and 22.5 years for males and females, respectively.

The Presbyterian religion is with 28% of the total population, the most dominant in Vanuatu.

The Anglican is the second largest, with 15%, followed by Seventh Day Adventist (SDA) and the Roman Catholic Church, each representing 12% of the Vanuatu population.

The 2009 census questionnaire included a question on smoking and drinking habits of the population aged 15 and older.
It was found that 25% of the population smoked cigarettes; 45% of males and 4% of females.

The age group that most likely smokes is 20–39 year-olds.

In general, more than half of all males aged 20–39 smoke, while only about 5% of females.

Furthermore, it was found that 10% of the population drinks alcohol; 17% of males and 3% are females.

The age group that most likely consumed alcohols is 20–34 year-olds.
In general, about one quarter of all males aged 20–34 drink alcohol, while less than 5% of females.

The proportion of the population consuming kava is much larger than that smoking or drinking alcohol.

Almost one third of the population consumed kava; 53% of males and 8% of females.

The age group that most likely consumed kava is 25–49 year-olds.

In general, about two thirds of all males aged 25–49 drink kava, compared to about 10% of females.
Data on disabilities indicate that about 12% of the total population reported a disability.

The proportion of the population with a disability increases with age, and there is very little difference xi in the proportion of males and females with a disability.

While about 6% of children younger than 5 years of age had a disability, it was more than half of the population at age 60 years and older.
Of those who reported disabilities, about 1,000 people could not walk at all, 800 people reportedly could not remember or concentrate, another 500 were deaf, and 400 people were blind.

School enrolment data show that 86% of children in the age group 6–13 years (compulsory school age) were enrolled in schools with female school enrollment rates slightly higher than male enrollment rates.
However, school enrollment rates declined rapidly after the age of 13, and about 25% of 14 year-olds were not attending school.

After the age of 16, male school enrollment rates were higher than female enrollment rates.

In general, enrollment rates were significantly higher in the urban than the rural areas.

Data on educational level completed indicate that in 2009, about half of the population 15 years and older had only a primary level education.
About one quarter had a secondary level education, and almost 4% of the population aged 15 and older had a tertiary level education.

Sixteen per cent had never been to school or only attended preschool.

Educational levels were significantly higher of the population in the urban area than in rural areas, and educational levels of males were higher than females.

Almost everyone (98%) older than 5 years of age living in the urban areas was literate.
This compares to only 80% of the population 5 years and older in rural areas.

Literacy rates were slightly higher for males than females.

Literacy in Bislama was, with 74% of the population, the highest followed by English (64%), and French (37%).

Half of the population is literate in a language other than Bislama, English or French.

Literacy was measured by a respondent’s ability to read and write a simple sentence in any language.
Literacy rates were over 90% for the population aged 10-34, then it gradually declines after that, and is less than 70% of the population at age 65 years and older.

The literacy rate of 15–25 year-olds was 92% and 93% for males and females, respectively.

The main language spoken in private households was a local language (63%), 34% speak Bislama, 2% English, and 1% French.
The internet was used by 7% of the population aged 15 years and older; this was 16% in the urban areas and only 3% in the rural areas.

Although a high percentage (71%) of Vanuatu’s population aged 15 and older was economically active, only a relatively small proportion (30%) received a regular paid income; this group consisted of 37% males and 23% females.
Subsistence work — such as growing or gathering produce or fishing to feed families — was the main activity of 32% of Vanuatu’s males and 28% females aged 15 and older.

About 39% of the population in rural areas was subsistence workers compared with 5% in the urban centre.

xii Only about 4,500 people were categorised as being unemployed, resulting in an unemployment rate of 4.6%; 4.1% for males and 5.2% for females.

The unemployment rates are 12% and 2% in the urban and rural areas respectively.
Fifty one people did not work because of poor weather conditions, or because they could not afford the transportation costs to work.

In addition, 897 people did not work and did not look for work, because they believed that no work was available.

Using the international definition of unemployment, these people were not classified as unemployed because they did not look for work and did not indicate that they were available for work.
However, if all of these people were included in the unemployed category, the unemployment rate would increase to 5.5%.

If subsistence workers were included as part of the unemployed — on the grounds that these people would look for work if they believed cash work was available in their labour market community — the total unemployment level would increase to 46,395 people, or an unemployment rate of 47% (43% for males and 51% for females, and 20% for the urban area and 55% in rural areas).
While this assumption would not apply to all individuals in this group, it would likely apply to a proportion of them.

Depending on the assumptions a user of these data may wish to use, the resulting unemployment rate would fall somewhere between 4.6% and 47%.

The main source of household income was, with 46% of all households, the sale of fish, crops, or handicrafts.
However, this was 60% of all rural households compared to only 3% of urban households, where 81% of all households’ main income was wages and/or salary.

Only 18% of rural household’s main source of income came from wages and/or salary.

Only 11% of urban household were involved in marine fishing activities; this was 39% of rural households.

Freshwater fishing activities were carried out by 4% and 21% of urban and rural households respectively.
While 81% of all rural households were involved in growing cash crops, only 17% of urban households grow cash crops.

Compared to rural households, where 80% of households raised chickens, 57% raised pigs and 39% cattle, only a small proportion of urban households raised any livestock.
Regarding the availability of household items, a higher proportion of households in urban areas (compared to rural households) used items such as motor vehicles, gas stoves, fridge or freezer, TV, radio, and computers, as well as DVD decks.

However, there are some items more commonly used in rural than in urban areas such as canoes and generators.
While 91% of urban households had at least one mobile phone compared to 71% in rural areas, there were 9% of urban households and 2% of rural households that had an internet connection.

Information on tenure reveals that 81% of all households owned their dwelling outright, while 12% rented their dwelling, and another 6% resided in their dwelling rent-free.

More than 90% of xiii households in the rural areas owned their dwelling, while 39% of urban households rented their dwelling.
Forty-six per cent of all households obtained their drinking water as piped water.

The second most important source was a tank (34%).

However, private piped water was only used by a significant proportion of households in the urban areas.

Otherwise, 14% of all rural households obtained their water from a river, lake or spring.
The most frequently recorded toilet facility used by 47% of all private households was a pit latrine, while 21% of all households used a flush toilet; this percentage was 65% in the urban areas and only 6% in the rural areas.

The main source of lighting in Vanuatu was a kerosene lamp, used by an average of 48% of all households, although this percentage was only 6% in the urban areas, compared to 62% in the rural areas.

Eighty per cent of urban households were connected to the electricity main grid.
This was only 11% of the rural households.

The main energy source for cooking for 85% of all households was wood and/or coconut shells.

It was almost universally used by the rural households and by slight more than half of the urban households, where 40% use gas as the main energy source for cooking.

About 52% of all households dispose of their waste by burning it.

In the urban areas two in three households dispose their waste using the authorized waste collection.
With respect to the use of insecticide treated bednets, 76% of all households had at least one bednet available; this was 88% of rural households compared to only 38% of urban households.

According to population projections prepared for this report, Vanuatu’s population in 2030 will increase to about 370 thousand people, and to 483 thousand in 2050.

The population will age, with a decreasing proportion of young people aged 15 and younger, and an increase in people aged 60 and older.
The working age population (aged 15–59) will be almost twice as high in 2030 compared to 2009, and will comprise of about 300 thousand people in 2050.

The school age population aged 6-13 years will increase from its current level of 40 thousand to 60 thousand.

Analysis of census data provides timely and accurate information about demographic trends, patterns and levels.
Through census data analysis, governments acquire comprehensive and consistent information about their country’s population structure, population processes and socio- economic characteristics.

The population data provided in this report can be an effective tool for planning and policy-making.

As policies are aimed at achieving goals in the future, knowledge about future population trends is required.
Understanding and anticipating population changes enables development planners to formulate effective programmes in areas as diverse as health, education, environment, poverty reduction, social progress, and economic growth.

xiv MAP OF VANUATU REPUBLIC OF VANUATU Hiu ,,.

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INTRODUCTION This report provides an analysis of the Vanuatu 2009 census data and, where data are available, presents comparisons with census data from earlier censuses.

1.1 Geographic setting Vanuatu consists of six provinces: Torba, Sanma, Penama, Malampa, Shefa, and Tafea, spread over an area of 612,300 km² in the South Pacific with a total land area of 12,281 km², stretching from Hiu Island up north to Mathew and Hunter Islands down south.
It includes 83 main islands, of which, about 63 are permanently inhabited.

Port Vila, the capital, is located on the island of Efate (province of Shefa) which is the most populous island although Santo Island is the biggest island in terms of land area, and is located in Sanma province.

Port Vila is 1,288 km due south east of Honiara, Solomon Islands; 1,071 km west of Suva, Fiji; and 2,394 km east of Cairns, Australia (see map).
1.2 Background to report This report is a collaborative effort between the Vanuatu National Statistics Office (VNSO) and the Statistics for Development Programme of the Secretariat of the Pacific Community (SPC).

For this purpose, Mr. Andreas Demmke visited the VNSO in Port Vila from 7 March 2011–15 April 2011.

Mr Simil Johnson, Vanuatu's Government Statistician, reviewed and commented on the final draft of this report.
The report is based on data collected during the population census enumeration, with 16 November 2009 being census day.

The detailed tabulations of the 2009 census report are published in ‘2009 National Population and Housing Census, Basic Tables Report, Volume 1, Vanuatu National Statistics Office, Port Vila, Vanuatu (2011).
The main purpose of this report is to:  provide a general overview of the vast amount of detailed information that is available from the 2009 census enumerations;  generate interest, curiosity, and a desire for more detailed information, especially for Vanuatu decision-makers and the general public; and enhance the decision-making process by policy-makers.

 Data users are encouraged to contact either the VNSO or SPC for further information.
Vanuatu National Statistics Office Private Mail Bag 9019 Port Vila Vanuatu Telephone: +678 22110 or 22111 E:mail: stats@vanuatu.gov.vu http://www.vnso.gov.vu Statistics for Development Programme Secretariat of the Pacific Community (SPC) BP D5, 98848 Noumea Cedex New Caledonia Telephone: +687 26 20 00 Email: Stats&Demog@spc.int http://www.spc.int/sdp/ 1 2.
POPULATION SIZE, TREND, DISTRIBUTION, STRUCTURE and URBANIZATION 2.1 Population size and trend The size, growth, and trend of the Vanuatu population are important considerations in the planning process.

Urban areas and areas of high population density need to be understood in order to analyze the population data in terms of its demographic dynamics.

The total enumerated population of Vanuatu stood at 234,023 in 2009.

This is an increase of 47,345 people since the 1999 population census.
Figures 1-3 show the population trend from 1967-2009.

Figure 1: Total population size, Vanuatu: 1967–2009 234 187 142 111 78 l e p o e p f o r e b m u N s d n a s u o h T 250 200 150 100 50 0 1967 1979 1989 Census year 1999 2009 It can be seen from Figure 1 that the population has continuously increased and tripled in size from 78 thousand in 1967 reaching to 234 thousand people in 2009.
Population change for the urban and rural and the 6 provinces of Vanuatu all show an increase in population size as presented in Figures 2 and 3.

In all these areas the population continues to grow although with various growth rates.

Shefa and Sanma province, where the urban centers of Port Vila and Luganville are located, had the fastest population increase.
2 Figure 2: Total Population size by urban rural residence, Vanuatu: 1967– Census year Urban Rural 1999 2009 Figure 3: Population size by province, Vanuatu: 1967–2009 Torba Sanma Penama Malampa Shefa Tafea Province 1967 1979 1989 1999 2009 3 The fertility, mortality and migration are the three demographic processes that continuously affect the population composition.
A closed population, which is a population not affected by migration, experiences change only in the form of natural increase; only births and deaths, affect the population size.

However, population growth is usually also shaped by migration.

Between the inter-censual periods 1989-1999 the population growth rate was 2.6 percent that declined to 2.3 percent for the period 1999-2009 as shown on Table 1.

Despite declining growth rates the population continued to increase faster (Figs.4 and 5).
While the average annual population increase during the period 1989-1999 was 4,212 people with a 2.6% growth rate, it increased to 4,729 people annually during the period 1999-2009 despite a lower growth rate of only 2.3%.

There were lower growth rates in all provinces during the period 1999-2009 compared to the period 1989-1999, except for Shefa province which recorded a growth rate of 3.7% in 1999-2009 compared to 3.4% growth during 1989-1999.
Shefa province had the highest growth rate of all provinces (Fig.6 and Table 1) – a clear sign of internal migration flows towards Shefa and the capital Port Vila.

With a 2.3% growth rate, the population of Vanuatu would double in 31 years.

Doubling time is an indication about future population size if assuming that the current population growth rate remains constant over time.

In such a case, the population would reach 468 thousand people in the year 2040.
In that situation, Tafea and Malampa will have their populations doubling in 60 years.

Shefa would have the lowest doubling time of just 19 years caused by its growing urban area of Port Vila.
Table 1: Population size, growth rate and doubling time by place of residence, Vanuatu: 1989, 1999 and 2009 Region Total population size (in numbers) (in %) Annual growth rate Population change Doubling Time 1989 1999 2009 1989-1999 1999-2009 1989-1999 1999-2009 1989-1999 1999-2009 1999 2009 Vanuatu 142,419 186,678 234,023 Urban Rural 25,870 116,549 40,094 146,584 57,195 176,828 4,212 1,354 2,859 4,729 1,708 3,021 3.0 5.2 2.5 2.8 Torba Sanma1 3.9 1.9 Penama 1.5 Malampa Shefa1 4.1 2.8 Tafea 1Shefa and Sanma include the urban areas of Port Vila and Luganville Figure 4: Population change, average annual increase in numbers, Vanuatu: 1967–2009 4,729 4,212 2,857 3,014 l e p o e p f o r e b m u N 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 1967-1979 1979-1989 1989-1999 1999-2009 Intercensal period Figure 5: Average annual population growth rate (%), Vanuatu: 1967–2009 % 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 3.1 2.4 2.6 2.3 1967-1979 1979-1989 1989-1999 1999-2009 Intercensal period 5 Figure 6: Average annual population growth rate (%) by province, Vanuatu: 2009 ) % ( e t a r h t w o r g l a u n n a e g a r e v A 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 3.7 2.3 1.9 2.4 1.5 1.2 1.1 Vanuatu Torba Sanma Penama Malampa Shefa Tafea 2.2 Population distribution Information obtained on the place of enumeration was used to describe the distribution of population.
Figure 7 displays the proportion of Vanuatu population by province.

In 2009, Shefa province had the biggest share of Vanuatu’s population, comprising 34% of the total population.

This represents an increase from 1999 when only 29% people had lived there.

Hosting the country’s capital urban center is the main reason for this bigger population share in Shefa.

Sanma province had the second highest proportion of the total population.
In Sanma the second largest urban centre of Vanuatu is located (Luganville).

6 Figure 7: Population distribution by province (%), Vanuatu: 2009 4% 14% 19% 34% 13% 16% Torba Sanma Penama Malampa Shefa Tafea 2.3 Population density Vanuatu has a total land area of 12,281km2.

According to the 2009 census, the average population density for Vanuatu was 19 people/km2 an increase from 15 people/km2 in 1999 (Table 2).

Population density varied widely throughout the provinces in Vanuatu.
Shefa province is the most densely populated due to urbanization.

Having just over 50 people/km2 the density represents an increase from 1999 when it had 36 people per square km.

Sanma and Malampa both have low densities despite having high populations because of large land areas.
Table 2: Population density (number of people/km2) by province, Vanuatu: 1999 and 2009 Province Land area (km2 ) Total Population Population Density 1989 1999 2009 1989 1999 2009 Vanuatu 12,281 142,419 186,678 234,023 Torba Sanma Penama Malampa Shefa Tafea Information on the above crude population density has been used to calculate a summary measure - the Gini Concentration (or coefficient) Ratio1 - which indicates how evenly or unevenly the population is distributed over the entire territory of Vanuatu.
If the population were evenly distributed in Vanuatu, a given proportion of the country’s area would have the same proportion of its population; that is, 20 percent of Vanuatu’s area would have 20 percent of the population.

In reality, a country’s population is never evenly distributed over the land surface area, hence, the cumulative proportion of land area and population will differ one from the other.
The Gini Coefficient Ratio can be used to analyze the historical population concentration in Vanuatu as a whole or the population concentration in each province.

The higher the value of the index, the higher is the concentration of the population within the specified areas of the country.

The ratio can range from 0 to 1, or sometimes multiplied by 100 to range between 0 and 100.
A low Gini coefficient indicates a more equal distribution, with 0 corresponding to complete equality, while higher Gini coefficients indicate more unequal distribution, with 1 corresponding to complete inequality.

This index is affected by the size of the areas used in the calculation (Table 3-4).
Table 3: Rank of Crude Population Densities by province and Gini Concentration ratio at the Vanuatu 1999 Census Area ai Population pi Density cumulative cumulative % cumulative products |pi-ai| Rank Province (km²) Vanuatu 12,281 4,262 Sanma 1 867 Torba 2 2,808 3 Malampa 1,632 Tafea 4 1,204 Penama 5 1,507 Shefa 6 2009 (per k㎡) Area ai Pop Pi Area Ai Pop Pi ai(pi+1) ai+1(pi) =abs(pi-ai) Gini Concentration: 815 1,713 3,656 5,520 8,773 20,476 0.29 807 1,518 3,195 4,962 7,084 17,566 31,822 38,712 68,608 96,023 121,465 174,397 Table 4: Rank of Crude Population Densities by province and Gini Concentration ratio at the Vanuatu 2009 Census Area ai Population pi Density cumulative cumulative % cumulative products |pi-ai| Rank Province (km²) Vanuatu 12,281 4,262 1 Sanma 867 Torba 2 2,808 3 Malampa 1,632 Tafea 4 1,204 Penama 5 1,507 Shefa 6 2009 (per k㎡) Area ai Pop Pi Area Ai Pop Pi ai(pi+1) ai+1(pi) =abs(pi-ai) Gini Concentration: 819 1,641 3,438 5,171 8,773 19,841 0.31 818 1,525 3,061 4,666 6,636 16,707 41,593 50,085 84,003 114,911 144,526 221,742 1From Wikipedia, the free encyclopedia: The Gini coefficient is a measure of statistical dispersion developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper "Variability and Mutability".
The Gini coefficient is a measure of the inequality of a distribution, a value of 0 expressing total equality and a value of 1 maximal inequality.

8 Although it is a useful measure for certain purposes, this index of population concentration must be interpreted with caution.

If for example, provinces could be defined in such a way that all uninhabited land areas were excluded, then all inhabited land areas would have high population densities, and the index value would be close to its maximum.
Figure 8: Lorenz curve for measuring population concentration in Vanuatu, 2009 ) % ( n o l i t a u p o P 100 80 60 40 20 0 0 20 40 60 80 100 Area (%) As can be seen by the Lorenz graph there is uneven distribution of land in Vanuatu (Fig.8).

With a Gini concentration ratio of 0.31 in 2009, Vanuatu is two thirds away from reaching total equality.

This is an increase from the 0.29 Gini concentration ratio in 1999.
2.4 Population structure The enumerated 2009 resident population consisted of 119,091 males and 114,932 females.

Males out-numbered females by 4,157, resulting in a sex ratio of 104, which means that there were 104 males per 100 females.

However, sex ratios varied by province as can be seen in Figure 9.
A sex ratio of 100 means that there are equal numbers of males and females while a sex ratio lower than 100 means there are more females than males and a sex ratio higher than 100 meaning more males than females.

Figure 9 shows there were significantly more males than females in Vanuatu and in all provinces except Tafea, which had more females than males.
9 o i t a r x e S ) s e l a m e f 0 0 1 r e p s e a m l f o r e b m u n ( 110 105 100 95 90 Figure 9: Sex ratios by province, Vanuatu: 2009 Province a f e h S a e f a T A population’s age-sex structure may be considered as a map of its demographic history.

Persons of the same age constitute a cohort of people who were born during the same year (or period); they have been exposed to similar historical events and conditions.
The age-sex structure of the whole population at a given moment may be viewed as an aggregation of cohorts born in different years.

A graphic representation of the age structure of the population such as an “age pyramid” shows the different surviving cohorts of people of each sex in Vanuatu.
A population pyramid shows the number of males and females in five-year age groups (Fig.11) or single years (Figs.10, and Figs.12 to 17), starting with the youngest age group at the bottom, and increasing with age towards the top of the pyramid.

The number of males is depicted to the left and the number of females to the right of the pyramid’s center.

The shaded area in Figure 11 shows the population count of the 1999 census, while the thickly outlined area shows the population count of the 2009 census.
10 Figure 10: Population pyramid by single years, Vanuatu: 2009 Males Females e g A 90+ Number of people Figure 11: Population pyramid by 5-year age groups, Vanuatu: 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 20,000 16,000 12,000 8,000 4,000 0 4,000 8,000 12,000 16,000 20,000 Number of people 11 At first sight, Vanuatu’s population pyramid (Fig.11) has the distinct features of a classical pyramid: it has a wide base, meaning that a large percentage of people are in the younger age groups, with increasingly narrow bars towards the top of the pyramid, representing decreasing age groups at older ages2.
The pyramids of Malampa, Penama, Tafea and Torba have a very similar shape, characterized by the extreme narrow bars at roughly ages 20-34.

It is evident that these provinces are losing people aged 20-34 years as they migrate into the urban centers in search for employment, education and for other reasons.

Shefa’s population pyramid presents a very different picture compared to the other provinces.

It shows a high number of people aged 15-25 years.


As stated above, internal migration flows directed particularly into Port Vila town explain the high number of people at that age group.

Port Vila, being the gateway to modernization and globalization, presents opportunities that people look for.

Opportunities in employment, better services for health and education, and appropriate infrastructure will encourage more people to migrate and use them.
Figure 12: Population pyramid by single years, Torba: 2009 Males Females e g A 90+ 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 200 150 100 50 0 50 100 150 200 Number of people 2 For a discussion on the accuracy of age reporting, and calculation of age accuracy indices, please refer to Appendix 1 12 Figure 13: Population pyramid by single years, Sanma: 2009 Males Females 800 700 600 500 400 300 200 100 0 100 200 300 400 500 600 700 800 Number of people Figure 14: Population pyramid by single years, Penama: 2009 Males Females e g A e g A 90+ 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 90+ Number of people 13 Figure 15: Population pyramid by single years, Malampa: 2009 Males Females 800 700 600 500 400 300 200 100 0 100 200 300 400 500 600 700 800 Number of people Figure 16: Population pyramid by single years, Shefa: 2009 Males Females e g A e g A 90+ 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 90+ 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 1,500 1,200 900 600 300 0 300 600 900 1,200 1,500 Number of people 14 Figure 17: Population pyramid by single years, Tafea: 2009 Males Females e g A 90+ Number of people In accordance with the overall population structure as illustrated by the population pyramids, several indicators can be calculated such as the median age and the age dependency ratio.
Vanuatu population has a relatively young age structure, with 39 % of the population younger than 15 years of age; 55% are in the so called working age groups 15-59, and 6% were older than 60 years (see Table 5 and Fig.18).

There is a direct link between the size and proportion of young people, and the median age.

The age structure is also illustrated by the median age of 20.5 years (Fig.19), meaning that half of the Vanuatu’ population was younger and the other half older than 20.5 years.
The median age in 1999 was only 18.8 years, indicating that the population structure was older in 2009 compared to 1999.

Shefa had over 60% of its population in the age group 15-59 (Fig.18), caused by the influx of migrants from the other provinces.

With a median age of 22.6 years, Shefa had the highest in the country.

On the other hand, Shefa, like Sanma, had the lowest proportion of people aged over 60 years.

Figure 19 shows a comparison of the median age by province which varies widely.
While the median age was within the range of 19-22 years for all provinces, it was much less for Tafea at only about 17 years.
15 Table 5: Population distribution by broad age group, dependency ratio, median age, and sex ratio, Vanuatu: 1999 and 2009 Province Year Proportion of population by broad age group (in %) 0–14 15–24 25–59 60+ Age dependency ratio (15–59) Median age (years) Sex ratio (males per 100 females) Vanuatu Torba Sanma Penama Malampa Shefa Tafea Figure 18: Population by broad age groups (in %) by province, Vanuatu: 2009 6 6 5 8 8 5 6 55 51 55 51 52 62 39 43 40 41 40 33 48 46 100 90 80 70 60 % 50 40 30 20 10 0 Vanuatu Torba Sanma Penama Malampa Shefa Tafea Province 60+ 15-59 0–14 16 Figure 19: Population by median age and province, Vanuatu: 2009 u t a u n a V a b r o T a m n a S a m a n e P Province a p m a a M l a f e h S a e f a T e g a n a i d e M 25 20 15 10 5 0 A common way to describe a population’s age structure is via the age dependency ratio, which compares the dependent component of a country’s population with its economically productive component.
This is conventionally expressed as the ratio of young people (0–14 years) plus the old (60+ years), to the working age population (15–59 years) as shown in Figure 20.

In 2009, Vanuatu had a dependency ratio of 81, meaning that for every 100 people of working age, 81 people were in the age dependent category.

The higher the dependency ratio, the higher the number of people that needs to be cared for by the working age population.

The dependency ratio has decreased since the 1999 census when it was 91.
Based on the population structure of the different provincial populations, the age dependency ratios of the different provinces vary accordingly.

The most favorable dependency ratio can be found in Shefa with only 61 dependent people per 100 people of working age.

Dependency ratios were much higher in Tafea, Penama and Torba.

Tafea registered the highest dependant population of 108 meaning there were more people of the old and young ages than people in the working age groups.
Looking at its broad age group Tafea has the highest number of people in the age group 0-14 compared to the other provinces and also the least number of people in the working age group (15-59) paving the way for a higher dependant population.

For detailed information on population trends and age structure of the different provinces please refer to Appendix 27.
17 Figure 20: Population by age dependency ratio and province, Vanuatu: 2009 o i t a r y c n e d n e p e d e g A 120 100 80 60 40 20 0 u t a u n a V a b r o T a m n a S a m a n e P Province a p m a a M l a f e h S a e f a T 2.5 Urbanization According to Pranati Datta 3 ’s paper on urbanization in India, urbanization is an index of transformation from traditional rural economies to a modern industrial one.
Davis Kingsley describes it as a progressive concentration of population in urban areas and it is a process through which a nation passes as they evolve from agrarian to industrial society (in Pranati Datta, 2006).

Urbanization occurs as a result of people’s choice of wanting to reside in the urban areas.

More people are changing residence from rural to urban areas, and increasing proportions of these people are selecting large cities and towns.
These events produce two aspects of urbanization whose measurements should be differentiated ‐ an increase in the proportion of people selecting urban areas of residence and natural increase in cities and towns.

3 Pranati Datta, 2006, Urbanization in India, pg.2 [online] 18 2.5.1 Urban growth Despite declining growth rates (Fig.

21) the population continues to increase (Fig.

22).
22).

While the average annual population increase during the period 1989-1999 was 1,354 people with a 4.2% growth rate, it increased to 1,708 people annually during the period 1999-2009 despite a lower growth rate of only 3.5%.
Figure 21: Urban average annual population growth rate (%), Vanuatu: 1967–2009 6.1 % 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 4.8 4.2 3.5 1967-1979 1979-1989 1989-1999 1999-2009 Intercensal period Figure 22: Urban population change, average annual increase in numbers, Vanuatu: 1967– 2009 1,708 1,354 975 688 l e p o e p f o r e b m u N 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 1967-1979 1979-1989 1989-1999 1999-2009 Intercensal period 19 2.5.2 Process of urbanization Kingsley mentions three stages in the process of urbanization.
Stage one is characterized by a rural traditional society with predominance in agriculture and dispersed pattern of settlements.

Stage two refers to an acceleration stage where basic restructuring of the economy and investments in social overhead capitals including transportation and communication take place.

In addition the proportion of urban population gradually increases from 25% to 40%, 50%, 60% and so on.

The third stage is when urban population exceeds 70% or more.
At this stage the level of urbanization remains more or less the same or constant and the rate of growth of the urban population and total population becomes similar.

Since the year 1967 the Vanuatu population and the urban population have been increasing.

Given an increase from 8 thousand people in 1967 to 57 thousand people in 2009, Vanuatu’s urban centers gained 49 thousand people since 1967.

Figure 23 illustrates this comparison and trend.


Figure 23: Process of urbanization, Vanuatu: 1967-2009 s d n a s u o h T n i s n o l i t a u p o P 250 200 150 100 50 0 Total Population Urban Population Rural Population 111 95 16 78 70 8 234 177 57 187 147 40 142 117 26 1967 1979 1989 1999 2009 Censual Year 2.5.3 Degree of urbanization The degree or level of urbanization is defined as the relative number of people who live in urban areas4.
Several indices have been drawn to measure particular aspects of degree of urbanization at a given time.

These measures are percent urban [UP/TP*100], percent rural [RP/TP*100] and the urban/rural ratio [UP/RP*100].
4 Pranati Datta, 2006, Urbanization in India, pg.5 [online] 20 Table 6: Degree/level of urbanization, Vanuatu: 1967-2009 Census Year 1967 1979 1989 1999 2009 Population Total (TP) Urban (UP) Rural (RP) 7,772 15,784 25,870 40,094 57,195 77,988 111,251 142,419 186,678 234,023 70,216 95,467 116,549 146,584 176,828 Percent Rural 90.0 85.8 81.8 78.5 75.6 Percent Urban 10.0 14.2 18.2 21.5 24.4 Urban/Rural Ratio 11.07 16.53 22.20 27.35 32.34 The trend shown in Table 6 and Figure 24 is that urbanization is constantly increasing.
Since 1967, the share of the urban population increased by more than ten percent.

Obviously as the proportion of the urban population increases the proportion of the rural population must decrease at the same time.

Based on the three stages of urbanization described by Kingsley, Vanuatu would be ranked in the first stage, because the proportion of urban is still less than 25%.
In addition, Vanuatu’s rural traditional society is still characterized by predominance in agriculture and a dispersed pattern of settlements.
Figure 24: Process of urbanization, Vanuatu: 2009 o i t a r l a r u r / n a b r u d n a n a b r u / l a r u r t n e c r e p 100 90 80 70 60 50 40 30 20 10 0 1967 1979 1989 1999 2009 Censual Year Percent Rural Percent Urban Urban/Rural Ratio 21 2.5.4 Tempo of urbanization The tempo of urbanization refers to the speed of urbanization and measures the change in the level of urbanization by analyzing changes in the indices used for measuring the degree of urbanization.
The measurement of urbanization tempo indicates the pace at which a specific area is urbanizing.

If a degree of urbanization in a country or province is known for two or more dates, the tempo is measured by the annual change in the index used for measuring the level of urbanization.

Although potentially useful, this procedure of measuring the tempo may require some caution depending on the index used for measuring the level of urbanization.
Table 7: Tempo of urbanization, Vanuatu: 1967-2009 Census Year 1967-1979 1979-1989 1989-1999 1999-2009 Population Growth Rate Rural 2.6 1.9 2.2 1.9 Urban 6.1 4.8 4.2 3.5 Total 3.1 2.4 2.6 2.3 Percent urban Percent Rural urban/rural ratio growth rate 3.0 2.4 1.6 1.3 growth rate -0.4 -0.5 -0.4 -0.4 growth rate 3.5 2.9 2.0 1.6 One quick index for measuring the tempo of urbanization is the difference between the annual population growth rates of urban and rural areas.
For example, in Vanuatu the annual population growth rates during the most recent census period (1999‐2009) for urban and rural areas were 3.5 percent and 1.9 percent, respectively; the urbanization tempo is 1.6 percent per year (Table 2.7).

The other index for measuring the tempo of urbanization is by using the “urban/rural ratio”.

It is also related to the difference between the two mentioned rates (annual population growth rates of urban and rural areas).
If the urban/rural ratio is known for more than one date, the annual exponential growth rate of the urban/rural ratio is also the difference between the urban and rural annual population growth rates.

For Vanuatu, in 2009, the annual exponential growth rate of “urban/rural population ratio” is 1.6, which is equal to the difference between the urban population growth rate of 3.5 percent and rural population growth rate of 1.9 percent.
According to Pranati Datta5, the main advantage of using this method compared to others for measuring tempo of urbanization is that it does not drop to zero when the country approaches the 100 percent level of urbanization; however it does regress toward the growth rate of the urban population.

Measuring the tempo of urbanization, not only makes sense for determining how fast one area is growing in relation to the other, but is also related to the indices for measuring the level of urbanization.
Thus by using the urban/rural ratio we can be sure to have an appropriate measuring index.
5 Pranati Datta, 2006, Urbanization in India, pg.9 [online] 22 Figure 25: Annual growth rate of “Urban/Rural Ratio” measuring the tempo of urbanization, Vanuatu: 1967-2009 3.5 urban/rural ratio growth rate 2.9 2.0 1.6 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 e t a r h t w o r G 1967-1979 1979-1989 1989-1999 1999-2009 Census Period Figure 25 presents the trend of annual growth rates relating to the “urban/rural ratio” during the period 1967‐2009.

It shows the declining pace of urbanization in Vanuatu.
One reason for this decline could be related to the timing of the population censuses.

The 1989, 1999 and 2009 censuses were conducted at the time of school holidays, when most urban residents (parents and children) may have returned to “home islands” for Christmas and school holidays.

Another reason is maintaining the same Enumeration Areas (EA) overtime may contribute to the decline.
Some EA’s that are currently still classified as rural should probably be classified as urban since these areas are showing characteristics of urban sprawl.

23 3.

DEMOGRAPHIC COMPONENTS 3.1 Fertility 3.1.1 National estimates In order to determine the level and pattern of fertility in Vanuatu, women 15 years of age and older were asked the following questions:  how many children they had born alive; and  when was their last child born.
The total number of children born alive to 71,486 women aged 15 and older was 189,333; 98,638 males and 90,695 females (Table 8).

The average number of children born alive to all women (average parity) was 2.6 children per woman.
Table 8: Female population aged 15 and older by number of children ever born alive, Vanuatu: 2009 Age of women Number of women Number of children ever born Average number of children ever born Males Females Total Males Females Total 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70+ Total Average parity increases with the age of women.
While women aged 15–19 had only very few children, women aged 45–49 had 4.4 children, and women older than 70 had on average 5 children.

The average parities of women over 49 years of age is also called the completed fertility rate, a cohort measure demonstrating how many children a certain cohort of women who have completed their childbearing actually produced during those years.

Figure 26 shows a comparison of the reported average number of children ever born of the last three censuses.
A fertility decline is apparent as the average number of children per woman at every age declined from one census to the next.

While the average number of children of women aged 45-49 years was 5.5 in 1989, it declined to 4.8 and 4.4 in 1999 and 2009.
24 Figure 26: Female population aged 15-49 by average number of children ever born alive, Vanuatu: 1989, 1999, and 2009 1989 1999 2009 n r o b r e v e n e r d l i h c f o r e b m u N 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 15–19 20–24 25–29 30–34 35–39 40–44 45–49 Age of women The census also included questions on whether mother’s children lived in her household or elsewhere, or whether they have died (Fig.27).
The proportion of children living in their mother’s household decreased with the age of the mother, because as children grow older they leave their parents’ home and form their own household.
25 Figure 27: Proportion of children ever born by age of mother and whether living in the same household as their mother, Vanuatu: 2009 100% 90% 80% 70% 60% 50% 40% 30% Proportion of children ever born (%) 20% 10% 0% 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group of mother in mother's household elsewhere children dead From the question on date of birth of the last born child, the number of births per year or period can be calculated (Table 9).
Table 9: Reported number of births during the one-year period before the census (17 November 2008 – 16 November 2009) by age group of women, Vanuatu: 2009 Age group of women Number of women Number of children ASFR* 15–19 20–24 25–29 30–34 35–39 40–44 45–49 11,855 11,126 9,291 7,903 7,095 5,709 5,175 516 2003 1763 1155 692 257 79 Total 58,154 ASFR = Age-Specific Fertility Rate TFR = Total Fertility Rate 6,465 0.044 0.180 0.190 0.146 0.098 0.045 0.015 TFR = 3.6 26 Responses from women during the 2009 census indicated that 6,465 children were born during the one-year period prior to the census, between November 2008 and November 2009 (Table 9).
However, this count compares with 7,105 children younger than one year of age enumerated during the census.

This mismatch of counts suggests that a sizeable number of women did not report the birth of their child during the year prior to the census, or did not accurately report the exact date of birth of their children.

Unfortunately, the number of registered deaths (from Vanuatu’s vital registration system) is not available, and a comparison of census data is not possible.
Figure 28 shows a comparison of the above data of the last 3 censuses.

Again it can be seen that the fertility level of women of all ages declined since 1989.

Fertility levels have especially decreased of women aged 25-39 years, while fertility levels of women aged 15-19, and 45-49 have only marginally changed.
Figure 28: Reported age-specific-fertility-rates (ASFR), Vanuatu: 1989, 1999, 2009 1989 1999 2009 R F S A ) n e m o w 0 0 0 1 r e p s h t r i b f o r e b m u N ( 300 250 200 150 100 50 0 15–19 20–24 25–29 30–34 35–39 40–44 45–49 Age group of women In order to estimate Vanuatu’s fertility level, this analysis relies on indirect estimation techniques.
First, the own-children method was applied to the census data, which is a procedure deriving ASFRs for a 10- or 15-year period from a special census tabulation of children classified by age, and age of mother, both ages being given in single years at the time of the census.

Age of mother can be determined only for those children who are enumerated in the same household as their 27 mother (i.e.

who are “own children” of a woman present in some enumerated household, hence the name of the method).
The results of the own-children method were kindly prepared and provided Mr. Michael Levin of the Harvard University Center for Population and Development Studies.

Secondly two variants of the P/F-Ratio method were applied, using census data on the number of children ever born by age of women, and the number of children born during the year prior to the census by age of women as reported in the census.
The demographic indicator most commonly used to describe a country’s fertility situation is called the total fertility rate (TFR).

This measure is an indication of the average number of children a woman gives birth to during her reproductive life (from ages 15–49 years).

It is calculated from the number of live births by age of women in a given year — the age-specific fertility rates (ASFRs).
Fertility estimates derived using the own-children method from the 2009 censuses show two distinct periods; the period 1995-2000 with a fertility level (TFR) of well above 4, and the period 2001-2009 when the level has remained constant of the 8-year period at a level of about 4 children per woman (Fig.29).
Figure 29: Estimates of TFR based on "own-children method", Vanuatu: 1995–2009 R F T ) ( 6 5 4 3 2 1 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Source: Michael Levin, Harvard University Center for Population and Development Studies 28 Results of the own-children method were compared with estimates derived by applying the Arriaga6 method — which measures fertility based on data in one or two points in time.
The difference between both methods is that the method using two points in time assumes changing (declining) fertility, while the method using only one point in time assumes constant fertility.

The software MORTPAK 4.1, procedure FERTPF (from the United Nations) was used (Apps.2 to 4).

Table 10 compares the estimated fertility levels derived by the different methods.
Table 10: Comparison of TFR estimates derived by various methods, Vanuatu: 1999 and 2009 Own-children method¹ Arriaga Method, using 1 point in time² Arriaga Method, using 2 points in time: 1999 and 2009² Trussell P/F Ratio Technique³ Relational Gompertz method* 4.6 4.1 4.7 4.1 4.4 3.8 4.7 4.1 4.6 4.1 Year 1999 2009 11999 estimates refer to 3-year period 1998-2000 and estimate for 2009 refers to period 2007-9 2using adjustment factors of women aged 20-29 3 using adjustment factors of women aged 25-34 *using average of age group 20-39 of 2+2 points based on ASFR and CEB Since the own-children estimates show that fertility levels remained relatively constant during the period 2001-2009 (Fig.29), the underlying assumptions of the Arriaga method using two points in time of changing fertility does not comply with the Vanuatu demographic situation, and therefore its results should be rejected.
On the other hand, the estimated TFRs derived by the own-children method and the Arriaga method using only one point in time which assumes constant fertility, show consistent results.

Furthermore the Trussel P/F Ratio method (App.5-6) as well as the Relational Gompertz method (App.7) confirms values produced by the own-children method.
6 Many censuses and surveys include questions related specifically to fertility, for example, the numbers of children women have ever had, and whether they had a birth in the year preceding the inquiry.
The method seeks to adjust the level of observed age-specific fertility rates, which are assumed to represent the true age pattern of fertility, to agree with the level of fertility indicated by the average parities (average number of children ever born) of women in age groups under 30 or 35, which are assumed to be accurate.
During successful application of this method, the age pattern of the period fertility rates is combined with the level implied by the average parities of younger women to derive a set of fertility rates that is generally more reliable than either of its constituent parts.

Responses to such questions can be used to estimate fertility indirectly.
Some techniques for doing this include the parity/fertility (P/F) ratio method developed by Brass, based on the average number of children ever born to women in five-year age groups, and women’s age pattern of fertility derived from births in the year preceding the census or survey; and the Arriaga technique, which is similar to the P/F ratio method but links data for more than one date.

While the Brass P/F ratio method assumes constant fertility in the past, the Arriaga method does not.
29 According to these estimates the TFR in and around 2009 was 4.1, only about half a child lower than 10 years ago in 1999 when the TFR was about 4.6.

This is a very moderate fertility decline during the intercensal period 1999-2009.
The following analysis is based on results derived by the own-children method, as it produces the most detailed information on levels and trend of fertility in Vanuatu by geographic and socio- economic background information of women of the 15-year period before the 2009 census.

The estimated fertility levels by age group of mother (Fig.30) again shows that the fertility levels of women of all ages decreased since the 1990s, but changed very little since the beginning of the new millennium.
Women aged 20-29 produced the most children with about 200 children per 1000 women in that age group.

The so-called teenage fertility rate, the number of children of women aged 15-19 years, was 66, which means that 66 children were born per 1000 women aged 15-19.
Figure 30: Estimated and adjusted age-specific fertility rates (ASFRs), Vanuatu: 1995 to 2009 R F S A ) n e m o w 0 0 0 1 r e p s h t r i b f o r e b m u N ( 250 200 150 100 50 0 66 2007-2009 2001-2003 1995-1997 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Source: Michael Levin, Harvard University Center for Population and Development Studies Age group of women The number of births by age of women, and therefore the total number of births during the one- year period around the 2009 census can be calculated by multiplying the adjusted ASFR by the 30 enumerated number of women by age group in the census, and summing the number of births by the age group of women (Table 11).
Table 11: Estimated age-specific fertility rate (ASFR), annual number of births, total fertility rate (TFR), and mean age at childbearing (MAC), Vanuatu: 2009 Age group of women Number of women Estimated ASFR Estimated number of births (ASFR x number of women) 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total TFR MAC# 11,855 11,126 9,291 7,903 7,095 5,709 5,175 58,154 0.0656 0.1979 0.2014 0.1656 0.1066 0.0538 0.0217 4.1 children per woman 29.3 years 778 2,202 1,871 1,309 756 307 112 7,335 *Adjusted ASFRs are based on estimates derived using the own-children method of the period 2007-2009 #Mean age at childbearing The estimated number of births of 7,335 in 2009 seems consistent with the enumerated population aged younger than one year of age of 7,105 children.
Finally the national crude birth rate (CBR) can then be calculated by dividing the estimated number of births (7,335) by the total 2009 census population (234,023), multiplied by 1,000.

CBR = 7,335/234,023 x 1,000 = 31.3 (there were 31 births/1,000 population) 31 3.1.2 Sub national estimates This section contains some fertility estimates by urban-rural distinction, and by province.

All estimates are based on results derived through application of the own-children method.
The summary of main indicators in the front of the report summarizes various fertility indicators by place of residence.

Not surprisingly, the fertility level in the urban areas was lower than in the rural areas.

Furthermore, the province Tafea and Penama had, with 5.2 and 4.7 children per woman, the highest fertility of all provinces, and Shefa with the location of the capital Port Vila has the lowest TFR of 3.4 (Fig.31).
Figure 31: TFR by place of residence, Vanuatu: 2007-2009 4.4 4.5 4.7 4.2 4.2 5.2 3.4 4.1 3.2 6 5 4 R F T 3 2 1 0 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Source: Michael Levin, Harvard University Center for Population and Development Studies Vanuatu national average Figures 32-33 show the fertility trend throughout the period 1995-2009 by urban-rural residence and by province.
Interestingly the fertility level in the urban areas and the province of Shefa where the capital Port Vila is located, show a slightly increasing trend since 2001-2003.

An explanation could be the migration of ‘high fertility’ rural women to the urban centers.

Furthermore, Figure 34 presents the adolescent (or teenage) fertility rate – the number of births per 1000 women aged 15-19.
The rate was, with 40 much lower in the urban than the rural areas (77), and it is the highest in the province of Torba, where the teenage fertility is, with 116 births per 1000 women aged 15-19, very high.
32 R F T R F T 7 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 Figure 32: Fertility trend by urban-rural residence, Vanuatu: 1995-2009 Vanuatu Urban Rural 2007-9 2004-6 2001-3 Year 1998-00 1995-97 Source: Michael Levin, Harvard University Center for Population and Development Studies Figure 33: Fertility trend by province, Vanuatu: 1995-2009 Vanuatu Torba Sanma Penama Malampa Shefa Tafea 2007-9 2004-6 2001-3 Year 1998-00 1995-97 Source: Michael Levin, Harvard University Center for Population and Development Studies 33 Figure 34: Adolescent fertility rate (number of births per 1000 women aged 15-19 years), Vanuatu: 2007-2009 116 77 78 74 68 67 50 e t a r y t i l i t r e f t n e c s e o d A l ) 9 1 - ( 120 100 80 60 40 20 0 66 40 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Vanuatu national average Source: Michael Levin, Harvard University Center for Population and Development Studies 3.1.3 Proximate determinants of fertility This section does not attempt to provide a comprehensive analysis of variables that determine the level of fertility in Vanuatu.
It merely touches on some aspects of it, mainly the educational levels of females.

It hopefully encourages research into a more detailed analysis of the proximate determinates of Vanuatu’s fertility levels and trends.

Comparing the fertility levels of women with the level of educational attainment shows a very straight forward and clear trend: the higher the level of women’s education, the lower the number children per woman (Fig.35).
The importance of education is furthermore illustrated by plotting the data of TFR and literacy rates of women aged 15 years and older by place of residence (Fig.36).

Literacy is measured as the proportion of the population who are able to read and write a simple sentence.
The coefficient of determination (R2) or correlation coefficient is with 0.914 very high, meaning that 91.4% of the variation of the two variables can be explained by the value of each variable: places with high literacy rate show a low TFR.

34 Equally high correlations were found when comparing the proportion of women without education and women with secondary education to data of women by fertility level.
Data show that the higher the proportion of women without education, the higher the level of fertility, and vice versa: the higher the proportion of women with secondary education, the lower the level of fertility (Figs.37-38).

Figure 39 shows the correlation between the proportion of women employed (the female employment-population ratio) and fertility levels by place of residence.

Data show that the higher the proportion of females employed, the lower the level of fertility.
Finally data also show a correlation between the levels of fertility and mortality (Figs.40-41).

There seems to be a positive correlation between the level of fertility (TFR), and the infant mortality rate (IMR): the higher the TFR, the higher the IMR.

Similarly is the correlation between the TFR and life expectancy at birth (e)) of females: the higher the average number of children per woman, (TFR), the shorter is a woman’s life span (e0).
However, please note that other intermediate factors can influence the degree of correlation shown in the graphs such as the age structure of the population, age at marriage, traditional values, religious affiliation, labor market, access and availability of contraceptives and reproductive health services etc.
35 Figure 35: Fertility level (TFR) by educational attainment, Vanuatu: 2009 4.6 4.0 3.6 4.1 3.4 R F T 5 4 3 2 1 0 Less than Primary School Finished Primary School Secondary School University entrance and above Source: Michael Levin, Harvard University Center for Population and Development Studies Vanuatu national average 36 Figure 36: Total fertility rate (TFR) and proportion of women aged 15 years and older literate by place of residence, Vanuatu: 2009 Urban Shefa Malampa Sanma Vanuatu Rural Penama Torba Tafea R² = 0.914 ) % ( e t a r y c a r e i L 100 90 80 70 60 50 40 30 20 10 0 3 4 5 6 TFR Figure 37: Total fertility rate (TFR) and proportion of women aged 15 years and older without education (never been to school) by place of residence, Vanuatu: 2009 R² = 0.8609 Tafea Torba Rural Penama Vanuatu Sanma Malampa Shefa Urban ) % ( 50 45 40 35 30 25 20 15 10 5 0 3 4 5 6 TFR 37 Figure 38: Total fertility rate (TFR) and proportion of women aged 15 years and older with secondary education by place of residence, Vanuatu: 2009 Urban Shefa ) % ( 50 45 40 35 30 25 20 15 10 5 0 R² = 0.7867 Vanuatu Sanma Penama Malampa Rural Torba Tafea 3 4 5 6 TFR Figure 39: Total fertility rate (TFR) and female employment-population ratio by place of residence, Vanuatu: 2009 ) % ( s e l a m e f f o o i t a r n o i t a l u p o p - t n e m y o l p m E 45 40 35 30 25 20 15 10 5 0 Urban Shefa Vanuatu Sanma Malampa Penama Rural R² = 0.7562 Torba Tafea 3 4 5 6 TFR 38 Figure 40: Total fertility rate (TFR) and Infant mortality rate (IMR) by place of residence, Vanuatu: 2009 Sanma Rural Vanuatu Malampa Torba Urban Shefa Tafea Penama R² = 0.6326 R M I 35 30 25 20 15 10 5 0 3 4 5 6 TFR Figure 41: Total fertility rate (TFR) and life expectancy at birth of females by place of residence, Vanuatu: 2009 74.0 73.5 73.0 72.5 72.0 71.5 ) s r a e y n i ( s e l a m e
73.0 72.5 72.0 71.5 ) s r a e y n i ( Urban Shefa Vanuatu Malampa Sanma Rural Tafea R² = 0.6386 Penama 71.0 3 4 Torba TFR 5 6 39 The questions relating to mortality in the 2009 census were: 3.2 Mortality  how many live births a woman has ever had, and how many of those born were still alive and/or had died;  whether a respondent’s mother and father was still alive (orphanhood);  whether a respondent’s marital status was "widowed" (widowhood); and  whether any residents of the household died during the last 12 months prior to the census.
3.2.1 National level estimates Based on the reported number of deaths by age and sex derived from the household question on number of deaths of household residents who died during the last 12 months before the census, 998 persons had died during the year before the census; 624 males, and 374 females (Table 12).
Table 12: Number of deaths of household residents during the 12 months preceding the census by age and sex, Vanuatu: 2009 Age group 0 1 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Total Total 258 65 24 19 20 31 21 23 29 37 40 52 58 64 54 203 998 Males Females Both the Brass Growth Balance Equation Method7and the Preston-Coale Method8 were applied to the collected data, and it appears that the reported number of household deaths is significantly underreported.
If these data were directly used to calculate a life table (by for example using the 7 Population Analysis Spreadsheets (PAS), procedure GRBAL, US Census Bureau, Washington, USA 8 Population Analysis Spreadsheets (PAS), procedure PRECOA, US Census Bureau, Washington, USA 40 PAS procedure LTPOPDTH) life expectancy at birth for males and females would calculate at 76.1 and 93.7 years, which is obviously much too high.
Interestingly the reported number of infant deaths (population younger than 1 year) seems considerably overstated, probably due to age misreporting, or coding errors.

On the other hand, male and female IMRs would calculate at about 39 and 31 per 1000 for males and females, which seem too high.
However, the data on reported household deaths by age and sex was used to determine which of the different Coale-Demeny and United Nations model life tables compares best to the empirical Vanuatu mortality pattern using MORTPAK’s procedure COMPAR.

The assumption was made that possible under-registration of deaths is not age specific and therefore does not affect the overall pattern of mortality.
It was found that the West pattern of the Coale-Demeny model life tables resembles most closely the empirical mortality pattern of the Vanuatu population.

Early age mortality From all children that were ever born to women aged 15 and older (189,333), 96.6% (182,885) were still alive, and 6,448 children had died (Table 13).

The proportion of surviving females was higher than that of males (Table 14).
While 96.7% of all female children ever born were still alive, only 95.5% of all male children had survived.

The proportion of surviving children decreases with the age of mothers (Table 14 and Fig.42).

While 98.2% of all children that were ever born to women now aged 20–24 were still alive, only 97.4% of children born to women now aged 45–49 were still alive, and only 88% of children born to women now aged 75 and older remained alive.
This general trend is explained by the fact that as the age of mothers increases, so does the age of her children; the proportion of birth cohorts that have died rises with an increase in the age of mothers.
41 Table 13: Female population aged 15 and older by number of children ever born, number of children dead, and number of children still alive, Vanuatu: 2009 Age of women 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Total Total number of women 11,855 11,126 9,291 7,903 7,095 5,709 5,175 3,626 3,102 2,127 1,772 2,705 Total number of children ever born alive Total number of children dead Total 1,144 9,865 17,632 23,136 25,790 23,461 22,863 16,716 15,263 10,658 9,298 13,507 Males Females 600 5,120 9,209 12,072 13,405 12,227 11,807 8,746 7,941 5,583 4,912 7,016 544 4,745 8,423 11,064 12,385 11,234 11,056 7,970 7,322 5,075 4,386 6,491 Total 32 213 344 466 587 599 635 592 658 591 630 1,101 Males Females 20 116 197 253 322 329 343 300 357 307 382 605 12 97 147 213 265 270 292 292 301 284 248 496 Total number of children still alive Males Females Total Table 14: Female population aged 15 and older by proportion of children ever born and still alive, and proportion now dead, Vanuatu: 2009 Age of women 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Total Total number of women 11,855 11,126 9,291 7,903 7,095 5,709 5,175 3,626 3,102 2,127 1,772 2,705 71,486 Proportion of children ever born still alive (%) Proportion of children ever born now dead (%) Total 97.2 97.8 98.0 98.0 97.7 97.4 97.2 96.5 95.7 94.5 93.2 91.8 96.6 Males 96.7 97.7 97.9 97.9 97.6 97.3 97.1 96.6 95.5 94.5 92.2 91.4 96.4 Females 97.8 98.0 98.3 98.1 97.9 97.6 97.4 96.3 95.9 94.4 94.3 92.4 96.8 Total 2.8 2.2 2.0 2.0 2.3 2.6 2.8 3.5 4.3 5.5 6.8 8.2 3.4 Males 3.3 2.3 2.1 2.1 2.4 2.7 2.9 3.4 4.5 5.5 7.8 8.6 3.6 Females 2.2 2.0 1.7 1.9 2.1 2.4 2.6 3.7 4.1 5.6 5.7 7.6 3.2 42 Figure
 42: Proportion of children ever born and still alive by age of mother, Vanuatu: 2009 ) % ( 100.0 98.0 96.0 94.0 92.0 90.0 88.0 86.0 Males Females 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group of mother A comparison of data on children ever born and still alive from the 1989, 1999 and 2009 census data (Fig.43) show continues improvements in the survival of children of women of all age groups.
Especially the proportion of children of older women, who themselves are older children at the time of the respective censuses, have significantly increased, which points to a general improvement in the (child) mortality levels.
43 Figure 43: Proportion of children ever born and still alive by age of mother, Vanuatu: 1989, 1999 and 2009 ) % ( 100.0 95.0 90.0 85.0 80.0 1989 1999 2009 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 Age group of mother Using the above census data on children ever born and children still living (by age group of mother), the following mortality indices have been obtained using the United Nations software package MORTPAK4.1, procedures CEBCS, and the assumption that the Coale-Demeny West model life tables resembles most closely the empirical mortality pattern of the Vanuatu population (see above).
Table 15: Child mortality indicators, Vanuatu: 2009 Indicator 1999 2009 Total Males Females Total Males Females Infant mortality rate (IMR)¹ Child mortality rate (4q1)² Under 5 mortality rate (q5)³ 1 = the number of deaths of children under one year of age per 1,000 live births 2 = the probability of dying between age 1 and age 5 (per 1000) 3 = the probability of dying between birth and age 5(per 1000) 27 6 32 26 5 31 27 6 33 21 4 24 22 4 26 19 3 22 IMR in 2009 was estimated at 22 and 19 for males and females, respectively, which is a significant improvement compared to 1999 when the IMR was estimated at 27 and 26 for males and females (Table 15).
44 Child mortality, the probability of dying between age 1 and age 5, was estimated at 4 male deaths and 3 female deaths per 1,000 people of that age in 2009.

Under 5 mortality, the probability of dying between birth and age 5, was estimated at 26 and 22 for males and females, respectively per 1,000 people in 2009.
Adult mortality Adult mortality levels can be estimated from responses to the question - whether a respondent’s mother or father was still alive (orphanhood), and - whether a respondent’s marital status was "widowed" (widowhood).

Orphanhood From Table 17 and Figure 44 it can be seen that the number and proportion of respondent’s mother still alive is higher than that of fathers.
There are 2 explanations for it: 1. females (mothers) usually live longer lives than males (fathers); and 2. fathers are usually older than mothers, because of their age difference at marriage.

In chapter 4, section on marital status, it was calculated that the average age at marriage (SMAM) is about 25.5 and 22.5 years for males and females respectively; an age difference of 3 years between spouses.
The data on orphanhood were used to calculate adult mortality rates, specifically the life expectancy at age 20 (Table 16).

The software package MORTPAK, procedure ORPHAN, was used to calculate the adult mortality rates.

Please note that the mean age at childbearing (MAC), a required data input for this method, was calculated from the adjusted ASFR produced by the own- children method.

The MAC-value for males was adjusted by the age difference of the calculated SMAMs.
Table 16: Life expectancy at age 20, using the orphanhood method, MORTPAK's procedure ORPHAN, Vanuatu: 2009 Life expectancy at age 20 (e20) 52.1 54.7 Males Females Total 53.4 45 Table 17: Population* by 5 year age group and whether biological father or mother is still alive, Vanuatu: 2009 Age group Number of respondents 0 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Total 33,083 29,476 26,682 21,734 21,229 18,245 15,586 14,016 11,423 10,144 7,245 6,314 4,284 3,790 5,632 228,883 Father still alive Mother still alive Yes 32,725 28,900 25,744 20,310 18,676 14,768 10,927 8,126 4,845 3,130 1,535 834 378 292 439 171,629 No 358 576 938 1,424 2,553 3,477 4,659 5,890 6,578 7,014 5,710 5,480 3,906 3,498 5,193 57,254 Yes 32,867 29,037 26,100 20,803 19,707 16,113 12,695 10,132 6,907 4,893 2,464 1,493 564 367 460 184,602 No 216 439 582 931 1,522 2,132 2,891 3,884 4,516 5,251 4,781 4,821 3,720 3,423 5,172 44,281 *refers to population living in private households only Figure 44: Proportion of respondent’s father or mother still alive, Vanuatu: 2009 100.0 90.0 80.0 70.0 60.0 % 50.0 40.0 30.0 20.0 10.0 0.0 Mother Father 0 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group of respondent 46 Widowhood From Table 18 and Figure 45 it can be seen that the number and proportion of females widowed is higher than that of males.
There are 2 explanations for it: 1. females usually live longer lives than males (her spouse); and 2. males are usually older than females, because of their age difference at marriage, as described above (orphanhood).

An attempt was made to use the data on widowhood to calculate adult mortality rates, specifically the life expectancy at age 20, by applying the software package MORTPAK, procedure WIDOW.
Unfortunately, the data do not allow the calculation of female values, because the proportion of male widowers is too small to calculate any reasonable indicators.

There are 2 explanations for this: 1. males did incorrectly state their marital status; and 2. a high proportion of males who lost their spouse remarried, and although widowed once, is tabulated as ‘married’.
Table 18: Population 15 years and older by sex and widowed, Vanuatu: 2009 Age group 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Total Total 23,882 21,541 18,415 15,693 14,171 11,523 10,241 7,415 6,363 4,319 3,826 5,661 143,050 Total Males 12,027 10,415 9,124 7,790 7,076 5,814 5,066 3,789 3,261 2,192 2,054 2,956 71,564 Females 11,855 11,126 9,291 7,903 7,095 5,709 5,175 3,626 3,102 2,127 1,772 2,705 71,486 Widowed Males 3 6 13 22 28 35 52 71 96 102 173 416 1,017 Total 22 63 85 99 170 210 297 368 438 477 582 1,442 4,253 Females 19 57 72 77 142 175 245 297 342 375 409 1,026 3,236 47 Figure 45: Proportion of population 15 years and older by sex and widowed, Vanuatu: 2009 Males Females ) % ( d e w o d w n o i i t r o p o r P 50 45 40 35 30 25 20 15 10 5 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Nevertheless, the data on marital status (widowhood) provides interesting and valuable insights into mortality differentials between males and females, as the large difference in widowed males and females points to lower mortality rates (higher life expectancies) for females than males.
However, since the widowhood method cannot be applied to both males and females, it was decided to rely on the orphanhood method to calculate consistent data for males and females, i.e.

using the same method for both sexes.

Complete life table In order to construct a complete life table for males and females, the estimated child and adult mortality indicators need to be combined (Table 19).
As mentioned above, it was decided to use the adult mortality indicators produced by the orphanhood method as the final adult mortality estimates together with the child mortality estimates as presented above.

Once again, the UN software package MORTPAK, procedure COMBIN, was used to calculate a complete life table for males and females.
The following inputs were used: 48 Table 19: Child and adult mortality indicators used to calculate complete life table, Vanuatu: 2009 Indicators Males Females Infant mortality rate (q0) Child mortality(1q4) l(1) l(5) E(20) 22 4 97800 97409 52.1 19 3 98100 97806 54.7 l (1) = The probability of surviving to age 1 (times 100,000) in the population under study = 100000 * [ 1 – q(0) ] l(5) = The probability of surviving to age 5 (times 100,000) in the population under study = 100000 * [ 1 – q(0) ] * [ 1 – 1q4) ] Tables 20 and 21 show the complete life tables for males and females.
The life expectancies at birth of 69.6 and 72.7 years for males and females is an improvement compared to those calculated based on the 1999 census, when life expectancies at birth were only 65.6 and 69.0 for males and females respectively.

Life tables for males and females for each province are presented in Apps.

8-19.
49 Table 20: Abridged life table for Vanuatu males: 2009 m(x,n) 0.0224 0.0010 0.0006 0.0006 0.0011 0.0014 0.0014 0.0016 0.0021 0.0031 0.0051 0.0082 0.0134 0.0213 0.0339 0.0545 0.0875 0.1680 q(x,n) 0.0220 0.0040 0.0032 0.0028 0.0055 0.0072 0.0072 0.0081 0.0105 0.0155 0.0251 0.0403 0.0652 0.1015 0.1569 0.2407 0.3594 ... l(x) 100,000 97,800 97,409 97,094 96,825 96,291 95,602 94,915 94,145 93,158 91,710 89,407 85,804 80,210 72,067 60,758 46,131 29,554 d(x,n) 2,200 391 315 269 534 689 687 770 987 1,448 2,303 3,603 5,594 8,144 11,309 14,627 16,577 29,554 L(x,n) 98,033 390,256 486,257 484,796 482,893 479,768 476,309 472,709 468,389 462,428 453,232 438,697 415,992 381,900 333,472 268,450 189,464 175,923 S(x,n) 0.9766 0.9958 0.9970 0.9961 0.9935 0.9928 0.9924 0.9909 0.9873 0.9801 0.9679 0.9482 0.9180 0.8732 0.8050 0.7058 0.4815 ... Age 0 1 70 75 80 T(x) 6,958,969 6,860,936 6,470,680 5,984,423 5,499,627 5,016,734 4,536,966 4,060,656 3,587,947 3,119,559 2,657,130 2,203,898 1,765,201 1,349,209 967,309 633,837 365,387 175,923 Note: Highlighted are the input values as displayed in Table 15, as well as the life expectancy at birth (e0) Table 21: Abridged life table for Vanuatu females: 2009 Age 0 1 70 75 80 m(x,n) 0.0193 0.0008 0.0003 0.0003 0.0006 0.0008 0.0008 0.0009 0.0012 0.0019 0.0034 0.0059 0.0104 0.0171 0.0283 0.0470 0.0774 0.1572 q(x,n) 0.0190 0.0030 0.0017 0.0014 0.0028 0.0040 0.0039 0.0044 0.0058 0.0093 0.0167 0.0289 0.0507 0.0822 0.1328 0.2114 0.3251 ... l(x) 100,000 98,100 97,806 97,641 97,501 97,229 96,838 96,461 96,038 95,477 94,587 93,005 90,314 85,739 78,693 68,240 53,812 36,316 d(x,n) 1,900 294 165 140 273 391 377 423 561 890 1,582 2,691 4,575 7,046 10,452 14,428 17,496 36,316 L(x,n) 98,285 391,693 488,618 487,857 486,884 485,193 483,253 481,282 478,875 475,353 469,346 458,896 441,052 412,302 368,920 306,737 226,163 231,020 S(x,n) 0.9800 0.9972 0.9984 0.9980 0.9965 0.9960 0.9959 0.9950 0.9926 0.9874 0.9777 0.9611 0.9348 0.8948 0.8314 0.7373 0.5053 ... T(x) 7,271,729 7,173,444 6,781,751 6,293,133 5,805,275 5,318,392 4,833,199 4,349,946 3,868,664 3,389,789 2,914,436 2,445,090 1,986,195 1,545,143 1,132,840 763,920 457,183 231,020 Note: Highlighted are the input values as displayed in Table 15, as well as the life expectancy at birth (e0) e(x) 69.6 70.2 66.4 61.6 56.8 52.1 47.5 42.8 38.1 33.5 29.0 24.7 20.6 16.8 13.4 10.4 7.9 6.0 e(x) 72.7 73.1 69.3 64.5 59.5 54.7 49.9 45.1 40.3 35.5 30.8 26.3 22.0 18.0 14.4 11.2 8.5 6.4 50 Brief explanation of a life table (Tables 20 and 21) A life table is used to simulate the lifetime mortality experience of a population.
It does so by taking that population’s age-specific death rates and applying them to a hypothetical population of 100,000 people born at the same time.

For each year on the life table, death inevitably thins the hypothetical population’s ranks until, in the bottom row of statistics, even the oldest people die.

Column “m(x,n)” shows the proportion of each age group dying in each age interval.

These data are based on the observed mortality experience of a population.
Column “l(x)” shows the number of people alive at the beginning of each age interval, starting with 100,000 at birth.

Column “d(x,n)” shows the number who would die within each age interval.

Column “L(x,n)” shows the total number of person-years that would be lived within each age interval.

Column “T(x)” shows the total number of years of life to be shared by the population in the age interval and in all subsequent intervals.
This measure takes into account the frequency of deaths that will occur in this and all subsequent intervals.

As age increases and the population shrinks, the total person- years that the survivors have to live necessarily diminish.

Life expectancy is shown in Column “e(x)” — the average number of years remaining for a person at a given age interval.

The first value in column “e(x)” represents life expectancy at birth.
The first value in column “q(x,n)” is an approximation of the infant mortality rate (IMR).

The second value in column “q(x,n)” is an approximation of the child mortality rate.
m(x,n) = age-specific death rate q(x,n) = the probability of dying between two exact ages l(x) = the number of survivors at exact age x d(x,n) = the number of deaths between two exact ages, x and x+n L(x,n) = the number of person-years that would be lived within the indicated age interval (x and x+n) by the cohort of 100,000 births assumed.

S(x,n) = probability of surviving between two exact ages, x and x+n T(x) indicated age interval by the cohort of 100,000 births assumed.
e(x) = total number of person-years that would be lived after the beginning of the = expectation of life from age x 51 Finally the annual number of deaths by age and sex can be calculated by multiplying the age- specific-death rates – the m(x) values in column 2 of tables – by the male and female population size of each respective age group.

The results are displayed in Table 22.
Table 22: Estimated number of deaths, and crude death rates (CDR) based on life table's age-specific-death rates [m(x)] and enumerated population size, Vanuatu: 2009 Age group 0 1 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 Population size Females 3,374 12,683 14,230 13,159 11,855 11,126 9,291 7,903 7,095 5,709 5,175 3,626 3,102 2,127 1,772 983 799 923 114,932 Males 3,731 13,579 15,455 14,762 12,027 10,415 9,124 7,790 7,076 5,814 5,066 3,789 3,261 2,192 2,054 1,085 941 930 119,091 Total 7,105 26,262 29,685 27,921 23,882 21,541 18,415 15,693 14,171 11,523 10,241 7,415 6,363 4,319 3,826 2,068 1,740 1,853 234,023 m(x,n) Males Females Males Females Estimated number of deaths Total - 69 70 - 74 75 - 79 80+ Total CDR* CDR: crude death rate The crude death rate (CDR) is calculated as follows: CDR = 1,260 / 234,023 x 1,000 = 5.4 (5 deaths per 1,000 population in 2009) 52 Table 23: General mortality indicators, Vanuatu: 2009 Indicator Life expectancy at birth, E(0) Crude death rate (CDR) Males Females Total 69.6 6.0 72.7 4.7 71.1 5.4 The above mortality indicators clearly show more positive mortality indicators for females than for males, with females living longer, on average about three years longer, than males (Table 23).
The findings are supported by the following data: the proportion of surviving female children was higher than males (Fig.42);   more mothers than fathers survive to older ages (Fig.44); and  the proportion of widowed females was considerably higher than that of widowed males (Fig.45), indicating earlier death of male spouses.

3.2.2 Sub national estimates This section contains some mortality estimates by urban-rural distinction, and by province.
All estimates are based on results derived through application of the same data and methodology as those used deriving the national estimates (data on children ever born and still alive, and on orphanhood – father/mother still alive).

A general observation is that all mortality indicators show better values in the urban than the rural areas, and that females are in general better off than males, although there are some exceptions to the general trend which is shown below.
The summary of main indicators in front of the report summarizes various mortality indicators by sex and place of residence, and figures show the results visually.

Children of mothers living in urban areas and/or the province of Shefa have a higher probability of survival than children of women living in rural areas (Fig.46).

Compared to the national average, children of mothers living in Torba or Tafea had the lowest probability of survival.
A higher proportion of female children ever born have survived than male children.

Of the population aged 60 years and older, almost 3 times more females (27.4%) were widowed than males (9.6%).

The proportion males and females 60 years and older who are widowed was the highest in the province of Torba followed by Penama (Fig.

47).

The proportions widowed were considerably higher in the rural than the urban areas.
However, when interpreting the results it needs to be mentioned that males are usually older than their spouses, in Vanuatu by about 3 years.

Figure 48 shows the proportions of the population orphaned, which means that either their biological father or mother had died.

On average a quarter of the population responded that their father had died, compared to 19% of their mothers.

Clearly mothers survive to older ages than 53 fathers.
However, as mentioned before, fathers are usually older than mothers, because of their age difference at marriage.

In general, the proportion of the population orphaned was higher in the rural than urban areas, and it was particularly high in Penama and Malampa.

Fortunately one of the most important mortality indicators, the infant mortality rate (IMR) has decreased since the last census in 1999, and stands at 22 and 19 infant deaths per 1000 live births for males and females respectively (Fig.49).
In general the IMR of males is higher than that of females, with the exceptions of the provinces Torba, Penama, and Tafea.

These are also the provinces with the highest IMR in general.

Infant mortality rates are significantly lower in the urban than the rural areas.

One likely important factor is the better accessibility of (reproductive) health services.

Figures 50 and 51 show the life expectancies at age 20 (e20) and life expectancy at birth (e0).

The pattern of both indicators is very similar.


In general females live on average 3 years longer than males.

Life expectancies in the rural areas are considerably lower than in the urban areas, where the difference between male and female life expectancies is much lower than in the rural areas.

The lowest life expectancies were calculated for Penama and Torba.

Life tables for males and females for each province are presented in Apps.8-19.
Figure 46: Proportion of children ever born and still alive by sex and place of residence, Vanuatu: 2009 ) % ( 100 99 98 97 96 95 94 93 92 91 90 96.8 96.4 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Males Females National average-Males National average-Females 54 Figure 47: Proportion of population 60 years and older widowed by sex and place of residence, Vanuatu: 2009 ) % i ( d e w o d w + 0 6 n o l i t a u p o p f o n o i t r o p o r P 50 45 40 35 30 25 20 15 10 5 0 27.4 9.6 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Males Females National average-Males National average-Females Figure 48: Proportion of population with father or mother dead (orphaned) by place of residence, Vanuatu: 2009 ) % ( 30 25 20 15 10 5 0 25.0 19.3 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Father Mother National average-Father National average-Mother 55 Figure 49: Infant mortality rate (IMR) by sex and place of residence, Vanuatu: 2009 40 35 30 25 R M I 20 15 10 5 0 22 19 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Males Females National average-Females National average-Males Figure 50: Life expectancy at age 20 (e20) by sex and place of residence, Vanuatu: 2009 54.7 52.1 ) s r a e y n i ( ) 0 2 e ( 0 2 e g a t a y c n a t c e p x e e f i L 60 55 50 45 40 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Males Females National average-Males National average-Females 56 Figure 51: Life expectancy at birth (e0) by sex and place of residence, Vanuatu: 2009 72.7 69.6 ) s r a e y n i ( ) 0 e ( h t r i b t a y c n a t c e p x e e f i L 75 70 65 60 55 50 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Males Females National average-Males National average-Females 57 3.2.3 Proximate determinants of mortality This section does not attempt to provide a comprehensive analysis of variables that determine the level of mortality in Vanuatu.
It merely provides some observations of possible correlations of variables, and hopefully entices research into a more detailed analysis of factors that determine Vanuatu’s mortality levels and trends.

The interdependency between the levels of fertility and mortality has been shown in the previous section on fertility (Figs.40-41).

Both graphs show that there is a correlation between the level of fertility and mortality: the higher the level of fertility, the higher the level of mortality.
Again, the importance of education is highlighted in figures where the correlation between the IMR and life expectancy at birth with various educational indicators is illustrated.
The data seem to indicate the following correlations: - - - the higher the literacy rate, the lower is the IMR (Fig.52), and the higher is the life expectancy at birth (Fig.53); the higher the proportion of the population that has never been to school, the higher the IMR (Fig.54); and the higher the proportion of the population with secondary education, the higher the life expectancy at birth (Fig.55).
However, please note that other intermediate factors can influence the degree of correlation shown in the graphs such as the age structure of the population, environmental factors, access and availability of quality health services etc.
58 Figure 52: Infant mortality rate (IMR) and literacy rate of population aged 15 years and older (%) by place of residence, Vanuatu: 2009 Penama Tafea Urban Shefa Malampa Vanuatu Sanma Rural Torba R² = 0.4827 ) % ( + 5 1 n o i t a l u p o p f o e t a r y c a r e t i L 100 90 80 70 60 50 40 30 20 10 0 16 18 20 22 24 IMR 26 28 30 32 Note: R² is 0.84 if the outlier Torba is omitted Figure 53: Life expectancy at birth (e0) and literacy rate of population aged 15 years and older (%) by place of residence, Vanuatu: 2009 Penama Torba Rural Malampa Vanuatu Sanma Tafea Shefa Urban R² = 0.418 ) % ( + 100 90 80 70 60 50 40 30 20 10 0 68.5 69.0 69.5 70.0 70.5 71.0 71.5 72.0 72.5 73.0 Life expectancy at birth (e0) (in years) 59 Figure 54: Infant mortality rate (IMR) and proportion of population never been to school (%) by place of residence, Vanuatu: 2009 ) % ( l o o h c s o t n e e b r e v e n + 5 1 n o l i t a u p o p f o n o i t r o p o r P 45 40 35 30 25 20 15 10 5 0 R² = 0.5306 Tafea Rural Sanma Vanuatu Malampa Penama Torba Shefa Urban 16 18 20 22 24 IMR Note: R² is 0.83 if the outlier Torba is omitted 26 28 30 32 Figure 55: Life expectancy at birth (e0) and proportion of population 15 years and older with secondary education (%) by place of residence, Vanuatu: 2009 R² = 0.6556 Urban Shefa Penama Torba Vanuatu Sanma Rural Malampa Tafea 50 45 40 35 30 25 20 15 10 5 0 ) % ( n o i t a c u d e y r a d n o c e s h t i w + 5 1 n o i t a k l u p o p f o n o i t r o p o r P 68.5 69.0 69.5 70.0 70.5 71.0 71.5 72.0 72.5 73.0 Life expectancy at birth in years) 60 3.3 Migration 3.3.1 Internal migration Internal migration — the movement of people from one island or region of Vanuatu to another — can be estimated by comparing:  place of usual residence with the place of residence during the census enumeration; and/or  place of residence five years prior to the census with the place of residence during the census enumeration; and/or  place of birth with the place of
 residence during the census enumeration; and  the population size of geographic units from one census count to the next.
Usual place of residence Based on the question regarding place of usual residence, 96% of the total population answered that their place of enumeration was also their place of usual residence, and only about 4% were enumerated at a place different from their usual place of residence (Table 24).

Less than 0.2% of the population had their usual place of residence overseas of which most were enumerated in the urban areas of Shefa (Port Vila).
Table 24: Total population by place of enumeration and usual place of residence, Vanuatu: 2009 Place of residence at time of census Urban Rural Torba Sanma Penama Malampa Shefa Tafea Vanuatu 57,195 176,828 9,359 45,855 30,819 36,727 78,723 32,540 234,023 Usual place of residence Same as place of enumeration 54,354 170,981 9,133 43,545 29,997 35,555 75,501 31,604 225,335 eslsewhere in country 2,541 5,685 225 2,240 815 1,152 2,868 926 8,226 overseas 300 162 1 70 7 20 354 10 462 Residence five years prior to the census Based on the question regarding place of residence five years prior to the census (in 2004), 84% of the total population aged 5 and older answered that they had not moved from their current (November 2009) place of residence, 15% (29,000 people) said that they lived elsewhere in Vanuatu, and 1,616 people (0.8%) said that they were overseas 5 years ago (Table 25).
Please 61 note that ‘elsewhere in Vanuatu’ includes places in the same province as place of enumeration, i.e.

people moved inside their own province.
Table 25: Population* 5 years and older by place of enumeration and usual residence five years ago (in 2004), Vanuatu: 2009 Usual place of residence 5 years ago Place of residence at time of census Same as place of enumeration 33,023 132,159 6,965 31,542 23,597 28,498 49,538 25,042 165,182 *population living in private households 49,045 146,755 7,792 37,617 25,362 30,921 67,201 26,907 195,800 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Vanuatu eslsewhere in country 14,803 14,197 817 5,918 1,714 2,397 16,311 1,843 29,000 overseas 1,219 399 10 157 51 26 1,352 22 1,618 Data on provincial migration are displayed in Table 26.
Here movement is measured in terms of changing a province, and excludes movements inside one and the same province.

Shefa had a net gain of 158 people from Torba province (247 minus 89), a net gain of 1,371 people from Sanma (2,177 minus 806), a net gain of 1,877 people from Penama (2,194 minus 317), a net gain of 1,784 from Malampa (2,511 minus 727), and a net gain of 631 people from Tafea (1,396 minus 765).
Overall Shefa gained 5,821 people from all other provinces during the five-year period prior to the census (Table 26).

Torba on the other hand had a net loss of 274 people to all other provinces, Sanma a net loss of 587 people, Penama a net loss of 2,255 people, Malampa lost 2,038, and Tafea had a net loss of 667 people (Table 27 and Fig.56)).

Clearly internal migration during the 5-year period 2004-2009 was primarily directed towards Shefa province, and certainly to the capital Port Vila.
62 Table 26: Population* 5 years and older by place of enumeration and province of usual residence five years ago (in 2004), Vanuatu: 2009 Torba Usual place of residence 5 years ago Place of residence at time of census Province 8 Torba 139 Sanma 30 Penama 69 Malampa 1,396 Shefa 25,910 Tafea 27,552 Vanuatu = non-movers (i.e.
those people who did not change their residence during the reference period *population living in private households Malampa 20 671 100 29,556 2,511 75 32,933 Total 7,792 37,617 25,362 30,921 67,201 26,907 195,800 Penama 33 712 24,464 132 2,194 31 27,566 Sanma 171 34,867 380 363 2,177 89 38,047 89 806 317 727 57,324 765 60,028 7,461 265 20 48 247 15 8,056 Tafea Shefa Overseas 10 157 51 26 1,352 22 1,618 Table 27: Interprovincial migration during the five-year period prior to the 2009 census, Vanuatu 2009 Province Torba Sanma Penama Malampa Shefa Tafea Vanuatu In-Migrants Out-Migrants Net Migrants 321 2,593 847 1,339 8,525 975 595 3,180 3,102 3,377 2,704 1,642 14,600 14,600 -274 -587 -2,255 -2,038 5,821 -667 0 63 Figure 56: Interprovincial net migration during the five-year period prior to the 2009 census, Vanuatu 2009 5,821 -667 Tafea Shefa Malampa -2,038 Penama -2,255 Sanma -587 Torba -274 -3,000 -2,000 -1,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Number of people Place of birth (lifetime migration) Sixty-six per cent (152,193) of Vanuatu’s population was living at the same place where they were born, 32% (73,741 people) were born in Vanuatu but not at their current (November 2009) place of residence, and just over 1% (2,949 people) of the population was born overseas (Table 28).
Table 28: Population by place of residence and place of birth, Vanuatu: 2009 Place of residence at time of census Urban Rural Torba Sanma Penama Malampa Shefa Tafea Vanuatu 56,061 172,822 9,189 44,287 29,926 36,060 77,047 32,374 228,883 Same as place of enumeration 19,730 132,463 7,260 26,558 24,503 28,568 38,100 27,204 152,193 Place of birth eslsewhere in country 34,114 39,627 1,904 17,377 5,366 7,440 36,534 5,120 73,741 overseas 2,217 732 25 352 57 52 2,413 50 2,949 64 Almost one quarter (56,146) of the population was born in Shefa province, 20% in Malampa (45,868), almost 18% (40,262) in Sanma, about 16% each in Penama and Tafea, and just over 4% were born in Torba (Table 29).
The vast majority of the overseas born population lived in Shefa.

It is interesting to note that 34% of the population lives in Shefa today, while only 25% were born there.

Overall only two-thirds (51,503) of Shefa’s population was born in Shefa, while more than 90% of the population in Torba, Penama, Malampa, and Tafea were also born there.

Only 77% of the 2009 population of Sanma was also born in Sanma.
Therefore data on lifetime migration (number of people by place of residence and place of birth) indicate that the direction of internal migration flows was mainly towards Shefa province.

Table 29: Population by provincial place of residence in 2009 and province of birth (lifetime migration), Vanuatu: 2009 Torba Usual place of birth Place of residence at time of census Province Torba 24 Sanma 510 Penama 83 Malampa 223 Shefa 4,309 30,775 Tafea 35,924 Vanuatu = non-movers (i.e.
those people who did not change their residence during the reference period Penama Malampa 69 3,234 473 33,054 8,785 252 45,868 Total 9,189 44,287 29,926 36,060 77,047 32,374 228,883 Sanma 328 34,257 597 780 4,168 132 40,262 133 1,864 381 1,271 51,503 993 56,146 177 3,002 28,240 521 5,289 132 37,361 8,433 1,067 95 159 579 40 10,373 Tafea Shefa Overseas 25 352 57 52 2,413 50 2,949 Based on the above data, it can be seen that Shefa had a net gain of 18,488 people, mainly from Malampa.
The only other province that had a net gain of people was Sanma with 3,673 people, mainly from Malampa and Penama (Table 30 and Fig.57).
Table 30: Interregional lifetime migration, Vanuatu: 2009 In-Migrants Out-Migrants Net Migrants 731 9,678 1,629 2,954 23,131 1,549 39,672 1,940 6,005 9,121 12,814 4,642 5,149 39,672 -1,209 3,673 -7,492 -9,860 18,488 -3,600 0 Province Torba Sanma Penama Malampa Shefa Tafea Vanuatu 65 Figure 57: Interregional lifetime net migration, Vanuatu: 2009 18,488 Tafea Shefa -3,600 Malampa -9,860 Penama -7,492 Sanma Torba 3,673 -1,209 -15,000 -10,000 -5,000 0 5,000 10,000 15,000 20,000 Number of people Comparing the population size of geographic areas in 1999 and 2009 While the data above in general showed that the provinces of Sanma and Shefa with its urban centers Luganville and Port Vila where the main recipients of migrants, a closer look at the population growth rates of villages (App.20) show that the growth rates of provincial geographic units varied widely.
It can be safely assumed that places with a population growth rate significantly above the national average of 2.3%, benefitted from a net inflow of migrants, whereas places significantly below the national average suffered from a population loss due to migration.
While the province of Shefa with an annual growth rate of 3.7% had the highest growth rate of all provinces, there were a number of places (islands) in Shefa that did not benefit from the overall growth such as Buninga, Emae, Emau, Ifira, Makira, Mataso, and Tongoa who all registered a decrease in population size between 1999 and 2009.
Population balancing equation Estimates derived by using the balancing equation reveal the following (Table 31): While Shefa gains 1,370 people annually, Tafea loses 650, Malampa loses 400, and Penama and Torba lose 260 and 60 people annually respectively.

Sanma’s annual net migration is zero (in- and out migration balances each other (refer also to chapter 6, section 6.2, Table 56).
66 In proportion to the population size of each province, the migration rates are as follows: while Shefa gains 1.6% of its population through migration, Tafea loses 1.9% of its population; Malampa loses 1%, Penama 0.8%, and Torba 0.6% annually.
Table 31: Estimates of annual net migration by province, Vanuatu: 2009 Torba Sanma Penama Malampa Annual net migrants Annual net migration rate -60 -0.6 0 0.0 -260 -0.8 -400 -1.0 Shefa 1,370 1.6 Tafea Vanuatu -650 -1.9 0 0.0 67 3.3.2 International migration International migration refers to people who cross national boundaries to move to another country.

In addition to this spatial consideration, time also plays a major role in the analysis of migration.
People are usually regarded as migrants only after spending a minimum period of time in their country of destination.

Usually the minimum time required to qualify as a migrant is half a year in-country, and sometimes even a full year.

Someone coming for a short visit is not considered to be a migrant — he or she is considered to be a visitor or tourist.
Intent is also of crucial importance, as migration usually involves a change of a person’s permanent residential address in pursuit of employment or educational opportunities.

The need to consider time and intent highlights one of the key problems concerning migration.
Whether or not a particular person qualifies as a migrant can only be established after a certain period of time, usually at least six months, in order to determine whether the arriving and departing person qualifies as a visitor or migrant.

The net impact of migration flows (net migration) is measured as the difference between the number of arrivals (immigrants) and departures (emigrants) during a certain time period.
Net migration = Arrivals (immigrants) minus Departures (emigrants) Therefore, if net migration was positive it means that the number of arrivals (immigrants) was higher than the number of departures (emigrants); if net migration was negative, the number of departures (emigrants) was higher than the number of arrivals.

The 2009 census included three questions that provide an indication of the level of immigration.

Questions were asked about a respondent's:  usual place of residence;   place of birth.
residence five years prior to the census; and Regarding respondent’s usual place of residence, only 462 answered that they usually live overseas; all other persons (233,561) had their usual residence in Vanuatu in 2009 (Table 24).

Regarding residential address five years prior to the census, 1,618 people (or less than 1%) of the population five years and older answered that they lived overseas (Table 25).
Regarding place of birth, 2,949 people (or just over 1% of the population) answered that they were born overseas (Table 28).

However, these questions only give an indication on the level of immigration The only indirect method for deriving at a crude indication of Vanuatu’s net migration level would be to apply the balancing equation to the intercensal 1999–2009 population growth rate.
68 Balancing equation Population growth = Births minus Deaths plus Net migration Net migration rate can be estimated as Net migration = Population growth minus Births plus Deaths The intercensal population growth rate was 2.3%, and the estimated CBR and CDR are 31.3 per 1000 and 5.4 per 1000.
The derived net migration rate would be: 2.3 – 3.13 + 0.54 = -0.29% However, there are strong indications that the 2009 census suffered from a slight under count in some age groups, as described in more detail in chapter 6, which would adjust the intercensal growth rate to about 2.6%.

In this case, the calculated net migration rate would be zero, and no significant international migration had occurred during the intercensal period 1999-2009.

69 4.
69 4.

SOCIAL CHARACTERISTICS 4.1 Marital status During the 2009 census, 50% of males (35,555) and 53% of females (37,704) aged 15 and older were legally married and another 13% of males and females were living in a de facto relationship (Fig.58).

The proportion never married (single), were 35% of males (25,255) and 27% of females (19,438).

A higher proportion of females (5%) were widowed than males (1%).
Figure 58: Population aged 15 and older by marital status, Vanuatu: 2009 l e p o e p f o r e b m u N 40,000 35,000 30,000 25,000 25,255 20,000 15,000 10,000 5,000 0 19,438 37,704 35,555 Males Females 9,987 9,049 3,236 1,017 463 790 225 331 Never married Legally married Defacto married Widowed Seperated Divorced Marital status The age at marriage is an important proximate determinant of fertility.

Women who marry at an early age often have more children than those marrying later.
The higher proportion of young married women compared with men of the same age indicates that women generally marry at younger ages than men (Table 32 and Fig.59).

The average age at marriage was 25.5 and 22.5 years for males and females, respectively, and was calculated based on the proportion of those never married/single by age.

There were notable differences in the age at marriage between provinces (Fig.61).
While the age at marriage was oldest in Malampa and 70 Shefa, it was very young in Tafea where the age difference between spouses was the smallest (1.8 years).

While only 3% of males were married at ages 15-19, it was almost 12% of females (Table 32 and Figs.60 and 62).

At age 20-24 more than half of all women were already married compared with 29% of males.
Compared to earlier censuses, the percentage of males and females married at young ages has declined, while the age difference between males and females has slightly increased compared to the 1989 and 1999 censuses.
Table 32: Singulate mean age at marriage (SMAM9) and percentage married at young ages by sex, Vanuatu: 1967, 1979, 1989, 1999, and 2009 Average age at first marriage Percentage ever married by age group (%) Year 1967 1979 1989 1999 2009 SMAM* Age difference Females males - females Males 5.4 3.9 2.6 2.3 3.0 21.2 22.2 22.6 23.0 22.5 2.8 3.4 2.6 3.2 15-19 20-24 Females Males Females 10.3 12.8 14.0 11.5 32.1 30.9 28.9 28.6 56.4 58.0 59.0 54.7 Males 26.6 26.1 25.2 25.3 25.5 9 1983.

United Nations.
United Nations.

Manual X, indirect techniques for demographic estimation.

New York: United Nations.


304 p. 71 Figure 59: Singulate mean age at marriage (SMAM) by sex, Vanuatu: 1967-2009 e g A 30 25 20 15 Males Females 1967 1979 1989 Census year 1999 2009 Figure 60: Population married at young ages by sex (%), Vanuatu: 1979, 1989, 1999, and 2009 60 55 50 45 40 35 % 30 25 20 15 10 5 0 Males Females Males Females age 15-19 years age 20-24 years 1979 1989 1999 2009 72 Figures 63 and 64 display the proportion of males and females married/never married by age.
Clearly these two figures complement each other.

When the proportion of the population married at a certain age is low, it is high for the proportion of the population never married at the same age, and vice versa.

Furthermore, the proportion of females in a married status is higher than that of males until age 34.

Then the proportion of married females is steadily declining because an increasing number of females become widows (Fig.65).
The discrepancy between the proportion of widowed males and widowed females, at ages 40 and older, increased continuously (Fig.

65).

Between ages 40–45, only 1% of males were widowed, compared with 3% of females.

At age 60 and older, only 10% of males were widowed, compared with 27% of females.
The higher proportion of widowed females is explained by:  lower female mortality rates, and therefore longer life expectancies of female spouses; and  older age at marriage of males compared with their female partners as expressed in the average age at marriage (SMAM) above.

Therefore, male spouses usually die before their female partners.
Figure 61: Average age at marriage (SMAM) by sex and province, Vanuatu: 2009 ) s r a e y n i ( M A M S 26 25 24 23 22 21 20 25.5 25.6 25.1 25.9 25.9 22.7 23.1 22.8 22.1 21.9 23.9 25.5 22.5 22.1 Torba Sanma Penama Malampa Shefa Tafea Males Females National average - Males National average - Females 73 Figure 62: Population married at age 15-19 years by sex and province (%), Vanuatu: 2009 % 18 16 14 12 10 8 6 4 2 0 11.5 3.2 0.8 15.8 14.5 10.0 9.8 9.8 11.1 3.3 2.4 2.8 2.5 4.0 Torba Sanma Penama Malampa Shefa Tafea Males Females National average - Males National average -Females Figure 63: Population aged 15 and older by sex and proportion married, Vanuatu: 2009 ) % ( d e i r r a m n o i t r o p o r P 100 90 80 70 60 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group Males Females Note: ‘Married’ include legally married and de facto relationships 74 Figure 64: Population aged 15 and older by sex and proportion never married (single), Vanuatu: 2009 Males Females 100 90 80 70 60 50 40 30 20 10 0 ) % ( d e i r r a m r e v e n n o i t r o p o r P 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group Figure 65: Population aged 15 and older by sex and proportion widowed, Vanuatu: 2009 Males Females i ) % ( d e w o d w n o i t r o p o r P 50 45 40 35 30 25 20 15 10 5 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group 75 4.2 Religion The question on religion was not compulsory.
Nevertheless, only 0.2% or 484 people refused to respond or did not respond to this question.

The Presbyterian Church of Vanuatu (PCV) continued to be the dominant religious denomination in Vanuatu, although its share had decreased from 36% in 1989 to 28% or 65,345 persons in 2009 (Table 33 and Fig.66).
Table 33: Population by religious affiliation, Vanuatu: 1989, 1999 and 2009 Religion Anglican Presbyterian Catholic SDA Church of Christ Assemblies of God Neil Thomas Minsitry Apostolic Customary beliefs No religion Refuse to answer Others 1989 19,949 50,951 20,613 11,737 6,745 - - - Total 142,419 Note: ‘Others’ refers to newly formed religions 186,678 234,023 The next largest group was the Anglican Church with 35,256 members, with a share of 15% of all denominations, followed by the Seventh Day Adventist Church (SDA) and the Catholic Church with a share of 12% each.
Other denominations (Church of Christ, Assemblies of God, Neil Thomas Ministry and Customary beliefs) had less than 5% of the population as members and persons with no religion comprised of 1% of the Vanuatu population.

The category “Others” comprises of 88 different religions ranging from 1 member to more than 2 thousand members.

The compositions of the different religious denominations were markedly different between the provinces (Fig.67).
While Penama and especially Torba were dominated by the Anglican Church, the Presbyterian Church was the main religion in Malampa and Shefa.

While the Presbyterian Church was also strong in Sanma, this province showed the most diverse mix of religions of all provinces.

76 One in five people in Tafea stated Customary beliefs as their religious affiliation.
Figure 66: Population by religious affiliation, Vanuatu: 2009 65,345 e l p o e p f o r e b m u N 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 35,256 29,251 28,933 29,475 11,078 10,593 8,600 7,223 2,554 5,231 484 Presbyterian Anglican SDA Catholic Assemblies of God Church of Christ Customary beliefs Neil Thomas Minsitry Apostolic No religion Refuse to answer Others Religious denomination Note: ‘Others’ refers to newly formed religions 77 Figure 67: Population by religious affiliation by province, Vanuatu: 2009 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Anglican Presbyterian Catholic SDA Church of Christ Assemblies of God Neil Thomas Minsitry Apostolic Customary beliefs No religion Refuse to answer Others Note: ‘Others’ refers to newly formed religions 4.3 Ethnic origin Based on information on the number of people by ethnic origin, Vanuatu has a very homogenous population composition, with 98% or 223,394 persons being Ni-Vanuatu, 1% or 2,617 persons were Part Ni-Vanuatu and 1% or 2,872 persons were of foreign descent (Table 34 and Figure 68).
The largest single groups of foreign descent were of Australian, New Zealander and European origin (1,566), followed by Melanesians other than N-Vanuatu (507), and Asians (496).
78 Table 34: Population living in private households by ethnic origin, Vanuatu: 2009 Ethnic origin Ni-Vanuatu Part Ni-Vanuatu Other Melanesian Polynesian Micronesian EU/Aust/NZ Asian African Total Number of people 223,394 2,617 507 183 82 1,566 496 38 228,883 % 97.6 1.1 0.2 0.1 0.0 0.7 0.2 0.0 100 Note: ‘EU (European), Aust (Australian), NZ (New Zealander) Figure 68: Population by ethnic origin and place of residence (%), Vanuatu: 2009 4.4 3.7 91.9 99.1 1.3 1.1 97.6 100% 98% 96% 94% 92% 90% 88% 86% 84% 82% 80% VANUATU URBAN PORT VILA LUGANVILLE RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Ni-Vanuatu Part Ni-Vanuatu All other ethnicities Eighty per cent of people of foreign descent lived in the province of Shefa, and the vast majority in Port Vila.
More than 99% of the population in the provinces Torba, Penama, Malampa and Tafea were Ni- Vanuatu.
79 4.4 Health 4.4.1 Disability Vanuatu is a signatory to a United Nations convention to uphold the rights of people with disabilities; and is therefore obliged to: “Promote, protect and ensure the full and equal enjoyment of all human rights and fundamental freedoms by all persons with disabilities and to promote respect for their inherent dignity.” For the 2009 Census the VNSO was requested by the Government and stakeholders to collect information on disabilities in Vanuatu.
The question on disabilities included in the 2009 Census were whether a person had any difficulties or health problems in seeing, hearing, walking, and/or remember or concentrating – regardless of the severity of the difficulties experienced (Table 35).

It was also asked whether a person cannot see, hear, walk or remember or concentrate at all – in other words, whether a person is blind, deaf, lame or senile and/or amnesic (Table 36).
Overall, about 12% of the total population reported a disability, and the proportion of females with a disability was slightly higher than that of males.

The disability that was most commonly mentioned were difficulties with seeing (17,584 people) followed by difficulties with walking (12,565), remembering and/or concentration (9,259), and hearing (7,827).
Table 35: Population* reporting a disability regardless of the severity of the disability, Vanuatu: 2009 Disability Vision Hearing Walking Remembering or concentrating Total Males Females 9,011 8,573 17,584 3,889 3,938 7,827 6,827 5,738 12,565 4,975 4,284 9,259 *Population living in private households About 1,000 people reported that they could not walk at all (lameness), about 800 people were senile and/or amnesic, another 500 people were deaf, and almost 400 people were blind.
80 Table 36: Population* reporting a severe disability, Vanuatu: 2009 Disability Blindness Deafness Lameness Senile and/or amnesic Total Males Females 190 207 220 284 531 479 402 408 397 504 1,010 810 *Population living in private households The proportions of the population with a disability were notably higher in Malampa compared to the national average, and it was lower in Sanma and Tafea (Fig.69).
Figure 69: Proportion of the population by sex and place of residence reporting a disability regardless of the severity of the disability, Vanuatu: 2009 20 18 16 14 12 % 10 8 6 4 2 0 13 12 TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Total Males Females National average-Males National average-Females As can be expected, the proportion of the population with a disability increased with age (Figs.70-74).
While about 6% of children younger than 5 years of age had a disability, it was below 5% for young people aged 5-24 years.

From age 40 and onwards, the proportion of the population with a disability increased continuously.

More than half of the population older than 60 years reported a disability (Fig.70).

The difficulty that was most commonly mentioned by the older population was vision (Fig.71) and walking (Fig.39).
81 Figure 70: Proportion of the population by age and sex reporting a disability regardless of the severity of the disability, Vanuatu: 2009 Males Females 80 70 60 50 % 40 30 20 10 0 0 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Figure 71: Proportion of the population by age and sex reporting difficulties seeing, Vanuatu: 2009 Males Females 80 70 60 50 % 40 30 20 10 0 0 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group 82 Figure 72: Proportion of the population by age and sex reporting difficulties hearing, Vanuatu: 2009 Males Females 80 70 60 50 % 40 30 20 10 0 0 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group Figure 73: Proportion of the population by age and sex reporting difficulties walking, Vanuatu: 2009 Males Females 80 70 60 50 % 40 30 20 10 0 0 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group 83 Figure 74: Proportion of the population by age and sex reporting difficulties remembering or concentrating, Vanuatu: 2009 Males Females 80 70 60 50 % 40 30 20 10 0 0 - 4 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group 4.4.2 Smoking and drinking habits Following a request from the Ministry of Health and other data user groups, the 2009 census questionnaire included several questions on smoking and drinking habits.
The questions were particularly aimed at collecting information on a person’s consumption habits with respect to smoking cigarettes or tobacco, and drinking alcohol and/or kava during the week before the census.

Questions on the frequency and quantity/volume of the substance consumed were not asked.
With respect to smoking cigarettes or tobacco, about 45% of males and 4% of females reported to be smokers (Figs.75 and 76); 17% of males and 3% of females reported to drink alcohol (Figs.77 and 78); and 53% and 8% of males and females drank kava (Figs.

79 and 80).

Smoking cigarettes or tobacco The proportion of male smokers was higher in the rural than the urban areas, and the highest in Torba, where more than half of all males smoke.
Interestingly, the proportion of female smokers was higher in the urban than the rural areas (Fig.75).

84 The highest proportion of male smokers were aged 20-39 years, at which age more than half of all males smoke cigarettes or tobacco.

However, the highest prevalence of smoking was among the 25-29 year olds with 60% of smokers.

From the age of 30 years the proportion of smokers continuously decreases with increasing age (Fig.76).
Female smoking was at a constant low level of about 4% at any age of women, except for a slightly higher rate at age 20-24 years.

Drinking alcohol The consumption of alcohol is significantly higher in the urban than the rural areas, although overall the proportion of the population that drinks alcohol is significantly lower than those that smoke or drink kava.

The consumption of alcohol is very low in Torba and Penama (Fig.77).
The highest proportion of male drinkers was aged 20-34 years, at which age about one-quarter of all males drink alcohol.

However, from the age of 30 years the proportion of alcohol drinkers continuously decreases with increasing age (Fig.78).

Female drinkers of alcohol was at a constant low level of about 3% at any age of women, except for a slightly higher rate at age 20-29 years.
Drinking kava The highest proportion of kava consumption is found in Torba were two-third of all males drink kava and about 20% of females.

Kava consumption was also high in Penama.

Overall the proportion of male kava drinkers was lower in the urban than the rural areas, but it was the opposite for females; more female kava drinkers were found in the urban than the rural areas (Fig.79).
The highest proportion of male kava drinkers were aged 25-39 years, at which age more than two-third of all males drink kava.

However, from the age of 40 years the proportion of kava drinkers continuously decreases with increasing age (Fig.80).

Female kava drinking peaked at ages 35-49, when slightly more than 10% of women drink kava.

From the age of 50 the proportion of female kava drinkers continuously decreases with increasing age.
Kava drinking among women was lowest among teenage women and women older than 70 years.
85 Figure 75: Proportion of the population 15 years and older by sex and place of residence who smoke cigarettes or tobacco (%), Vanuatu: 2009 100 90 80 70 60 % 50 40 30 20 10 0 45 4 TORBA SANMA PENAMA MALAMPA SHEFA TAFEA URBAN RURAL Males Females National average-Males National average-Females Figure 76: Proportion of the population 15 years and older by age and sex who smoke cigarettes or tobacco (%), Vanuatu: 2009 Males Females 100 90 80 70 60 % 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group 86 Figure 77: Proportion of the population 15 years and older by sex and place of residence who drink alcohol (%), Vanuatu: 2009 100 90 80 70 60 % 50 40 30 20 10 0 17 3 TORBA SANMA PENAMA MALAMPA SHEFA TAFEA URBAN RURAL Males Females National average-Males National average-Females Figure 78: Proportion of the population 15 years and older by age and sex who drink alcohol (%), Vanuatu: 2009 Males Females 100 90 80 70 60 % 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group 87 Figure 79: Proportion of the population 15 years and older by sex and place of residence who drink kava (%), Vanuatu: 2009 100 90 80 70 60 % 50 40 30 20 10 0 53 8 TORBA SANMA PENAMA MALAMPA SHEFA TAFEA URBAN RURAL Males Females National average-Males National average-Females Figure 80: Proportion of the population 15 years and older by age and sex who drink kava (%), Vanuatu: 2009 Males Females 100 90 80 70 60 % 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75+ Age group 88 4.5 Educational characteristics The Ministry of Education has made considerable progress towards achieving universal primary education.
As a result of broad consultation processes and the SWA (Sector wide approach) used in developing the Vanuatu Education Sector Strategy 2007-2016, the Ministry of Education realigned its priorities towards universal primary education and literacy.
Constraints for universal primary education identified during this process were the costs of maintaining the dual education system with separate streams for ‘English’ and ‘French’ as the language of instruction and the need to achieve an integrated system of bilingual schools.

A concerted effort with development partners to provide ‘fee free’ primary level education up to Year 6 in Government and Government-assisted schools began in some areas in 2009 and achieved full coverage in 2010.
Compulsory primary school contributions have been phased out and replaced by grants paid directly to the schools.

This was in direct response to declining primary enrolment rates which were around 95% in 2005 but decreased to 80% in 2008; and subsequent research highlighted rising parental contributions (school fees) as one of the main reasons why enrolment rates were falling.
The education system consists of preschool (aged 3 to 5 years), primary (aged 6-13 years or Year 1 to 8) and secondary school (Year 9 to 13 or Year 14 in some French schools).

There are still some junior secondary schools offering Years 7 and 8 that will be phased out and absorbed into primary level education.

4.5.1 School enrollment At the time of the census, 61,931 people of the total enumerated population 5 years and older were enrolled in schools; 32,337 males and 29,594 females.
Of these, 58,989 people were enrolled full time and 2,942 were part time enrolled in an educational institution.

The distribution of those attending a school by school level is shown in Table 37.

This Table excludes students boarding in dormitories.
Table 37: Population* 5 years and older by sex and enrolled in school by school level attending, Vanuatu: 2009 School level Pre school Primary Secondary Tertiary Vocational Other Total Total 5,953 42,323 6,800 1,527 413 332 57,348 Males 3,131 22,317 3,424 791 235 167 30,065 Females 2,822 20,006 3,376 736 178 165 27,283 *refers to population living in private households only 89 Three quarters of all students (42,323) were enrolled in primary schools, 12% in secondary schools (6,800) and 10% in Pre schools (5,953).
Only 413 students attended a vocational institution.

With respect to the main language spoken at the educational institutions, slightly more than half (51%) of all students attended English speaking schools (29,357), and 23% attended French speaking schools (13,259).

Unfortunately about 20% (11,750) of all students did not state or know the main language spoken at their school (Fig.

81).
81).

Figure 81: Population* 5 years and older by sex and enrolled in school by main language of educational institution, Vanuatu: 2009 s l i p u p f o r e b m u N 30,000 25,000 20,000 15,000 10,000 5,000 0 29,357 13,259 11,750 927 1,861 194 English French English and French Local Language Other NS Language *refers to population living in private households only There were insignificant differences between male and female enrollment rates for students aged 5-17.
From the age of 18 school enrollment rates for males were slightly higher than females’ (Fig.82).

Not at any age were more than 90% of children enrolled in schools, which means that more than 1 in 10 children has never been to school.

The highest school enrollment rates were for the 8-11 year olds when almost 90% of children were in school.

From the age of 12, school enrollment rates rapidly decrease, and at age 16 years only just over half of children were still in school.
Apart from the relatively large proportion of young people that had never been to school of about 5% of all teenagers (Fig.83), it is a worry that even at young ages of 8-12 years children start leaving school, and at age 13 almost 15% of children had already left school (Fig.

84).

90 With respect to the population aged 6-13 years, 86% were enrolled in school, 6% had already left school, and 8% had never been in school.

The percentage distribution is about the same for males and females.
However, there were marked differences in school enrollment rates by place of residence (Fig.85).

School attendance was significantly higher in the urban (91%) than the rural areas (85%), and Tafea had by far the lowest enrollment rates of the 6-13 year olds.

Only three- quarter were enrolled in school, and 20% had never been to school.

On the other hand, Malampa had the highest enrollment rates of 6-13 year olds with 91%, and only 4% had never been to school.
Figure 82: Proportion of the population aged 5-24 years by age and sex enrolled in school (%), Vanuatu: 2009 Total Males Females l o o h c s n i d e l l o r n e % Age 91 Figure 83: Proportion of the population aged 5-24 years by age and sex who left school (%), Vanuatu: 2009 Total Males Females ) % ( l o o h c s t f e l Age Figure 84: Proportion of the population aged 5-24 years by age and sex who have never been to school (%), Vanuatu: 2009 Total Males Females ) % ( l o o h c s o t n e e b r e v e n Age 92 Figure 85: Proportion of the population aged 6-13 years by sex and school attendance status (%), Vanuatu: VANUATU Urban Rural Torba Sanma Penama Malampa Shefa Tafea attending school Left school Never been to school 4.5.2 Educational attainment Based on data on the highest level of education completed, 26% of males and 24% of females 15 years and older responded that they had completed secondary education (Form 3 certificate, Year 10 leaving certificate, Senior secondary certificate or University entrance).
About 48 % completed only primary level and 16% of the population 15 years and older had never been to school or only visited preschool; 14% of males and 18% of females.

Four per cent of males and three per cent of females had tertiary education (Figs.86 and 87).

As can be expected, educational levels were much higher in the urban than the rural areas.
The proportion of the population 15 years and older living in the urban areas that completed secondary education was 43% compared to only 18% in rural areas.

On the other hand, the proportion of the population with no education (never been to school or only preschool level) was 20% in the rural areas compared to 5% in the urban areas.

The proportion of the population with no education was particularly high in Tafea (39%), followed by Torba (25%).
Shefa with the urban centre Port Vila had the highest proportion of the population (35%) with secondary education, followed by Sanma with 24%.
93 Figure 86: Population 15 years and older by sex and highest level of education completed, Vanuatu: 2009 35,000 30,000 25,000 e l p o e p f o r e b m u N 20,000 15,000 12,746 10,000 10,337 5,000 0 Never been to school 34,545 34,624 Males Females 18,488 17,023 2,924 2,155 1,004 576 426 328 3,840 4,034 Primary Secondary Tertiary Vocational Others Not stated Educational level Figure 87: Population 15 years and older by sex, place of residence and highest level of education completed, Vanuatu: l a t o T l s e a M l a t o T l s e a M s e l a m e F 29 s e l a m e F 16 15 17 19 16 l a t o T l s e a M l a t o T l s e a M s e l a m e F 21 s e l a m e F 13 12 14 VANUATU Urban Rural Torba Sanma Penama Malampa Shefa Tafea never been to school Primary Secondary Tertiary Vocational 94 4.5.3 Literacy and language ability Literacy was measured by a respondent’s ability to read and write a simple sentence in one or more of the following languages: English, French, Bislama, or any other language including local languages.
Between ages 10-34 years, slightly more than 90% of the population was literate.

From the age of 35 literacy rates gradually decline with increasing age of the population.

While only 80% of the population aged 55-59 were literate, it was only 60% of the population 70 years and older (Fig.88).
The literacy rate of 15–24 year-olds was 92% and 93% for males and females, respectively (Fig.89) While almost everybody of the 15-24 year olds in the urban areas was literate, it was less than 90% in the rural areas.

The provinces of Torba and especially Tafea had significantly lower literacy rates than the national average.

Literacy in terms of language abilities are shown in Figures 90-94.
Not surprisingly Bislama was most widely spoken by 74% of the population 5 years and older; it was followed by English with 64%, other (local) languages (50%), and French with 37%.

Language abilities varied extensively by place of residence.

Language abilities in any language were much higher in the urban than the rural areas.

Otherwise Bislama was most widely spoken in Shefa, Malampa and Sanma.
English was also popular in Shefa, followed by Sanma and Penama, local languages were common in Shefa and Penama, and French speakers were proportionately more widespread in Shefa and Malampa than in other provinces.
All languages shared a common feature which is that the proportion of young speakers aged 5-14 was relatively low, the ability to speak any language decreased sharply after the age of 50 years, and language abilities of males – especially older males – was higher than that of females (Figs.91-94).
95 Figure 88: Literacy rate of the population 5 years and older by sex (%), Vanuatu: 2009 100 90 80 70 60 % 50 40 30 20 10 0 Total Males Females 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 + Age group 96 Figure 89: Literacy rate of the population aged 15-24 years by sex and place of residence (%), Vanuatu: 2009 93 92 100 90 80 70 60 % 50 40 30 20 10 0 Urban Rural Torba Sanma Penama Malampa Shefa Tafea Males Females National average-Males National average-Females Figure 90: Language ability of the population 5 years and older by sex (%), Vanuatu: 2009 100 90 80 70 60 % 50 40 30 20 10 0 74 64 50 37 Urban Rural Torba Sanma Penama Malampa Shefa Tafea English French Bislama Other (local) language National average-English National average-French National average-Bislama National average-Other 97 Figure 91: English language ability of the population 5 years and older by sex (%), Vanuatu: 2009 Males Females 100 90 80 70 60 % 50 40 30 20 10 0 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 + Age group Figure 92: French language ability of the population 5 years and older by sex (%), Vanuatu: 2009 Males Females 100 90 80 70 60 % 50 40 30 20 10 0 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 + Age group 98 Figure 93: Bislama language ability of the population 5 years and older by sex (%), Vanuatu: 2009 Males Females 100 90 80 70 60 % 50 40 30 20 10 0 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 + Age group Figure 94: Other language ability of the population 5 years and older by sex (%), Vanuatu: 2009 Males Females 100 90 80 70 60 % 50 40 30 20 10 0 5 - 9 10 - 14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 + Age group 99 4.6 Internet use The 2009 census questionnaire included a question on internet use during the week before the census of the
 population 15 years and longer.
Only 9,290 (6.7%) of all respondents said that they used the internet; 5,063 males and 4,227 females.

It was mainly the 20-49 year old population who used the internet, although the highest proportions of users were among the 40-44 year old males and 20-24 year old females (Fig.95).

As can be expected, internet use was much more common in the urban areas where 16.3% of the population used the internet compared to 3% of the rural population (Fig.

96).
96).

The highest use of the internet was in the province of Shefa where almost 14% used the internet, and the province with the lowest use was Torba, where internet use was less than 1%.
Figure 95: Proportion of the population 15 years and older by sex using the Internet, Vanuatu: 2009 12 10 8 % 6 4 2 0 Males Females 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group of respondent 100 Figure 96: Proportion of the population 15 years and older by place of residence using the Internet, Vanuatu: 2009 16.3 20 18 16 14 12 % 10 8 6 4 2 0 13.6 6.7 2.3 3.0 0.7 3.5 3.2 1.8 URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA National average 4.7 Labour market activity 4.7.1 Introduction In Vanuatu, the 2009 census included a question on labour market activity.
Enumerators were instructed to ask each respondent aged 15 and over whether they worked during the last week.

Work was defined as any activity concerned with providing the necessities of life.

It did not matter whether or not the person had a job or was paid for what they did.
Based on these criteria, respondents were coded on the questionnaire into the four mutually exclusive categories of:  Work for pay;  Work to support the household by producing goods mainly for sale;  Work to support the household by producing goods mainly for own consumption;  Voluntary work or unpaid family work.

A person who “works for pay” is someone who worked for wages, salary, commission, or had a contract, or was operating a business.
The person was either a government or private employee, an employer, or was self-employed.

It also includes persons that did “work to support the household by producing goods mainly for sale” performing a variety of tasks such as farming, gardening, fishing or producing handicrafts mainly for sale.
101 A person that did “work to support the household by producing goods mainly for own consumption”, performed a variety of tasks such as farming, gardening, fishing or producing handicrafts for their own consumption and are subsequently described as subsistence workers.

A person that did “voluntary work” or “unpaid family work” is someone who worked but did not receive a wage, salary, commission, and did not have a contract.
The UN publication “Principles and Recommendations for Population and Housing Censuses, Revision 2”, recommends that “persons engaged in economic activities in the form of own- account production of goods for own final use within the same household should be considered to be self-employed.” Certainly, those selling their products should also be classified as employed.

According to this definition, all people classified as subsistence workers are considered to be employed.
The "non-labor force" category applies to those people who did nothing in the reference week (i.e.

the week prior to the census) to provide for themselves or their families or household.

This includes people engaged in home duties, who were retired, disabled, students, the unemployed and those who did “not want to work” or did not work because “the weather or transport problems” prevented them from working, or for they did not work for “other” reasons.
People classified as unemployed:  did not work in the week prior to the census (other than those who had a job but were not at work during the reference week), but spent some time looking for work, and   were available to work if a job was offered to them.

If the person did not work and did not spend some time looking, or looked for work but was not available for work, they were then classified as economically inactive (not in the labor force).
Based on the above, data collected from the Vanuatu census have been assigned to the three categories of:  employed (those that “work for pay” or “work to support the household by producing goods mainly for sale” or “work to support the household by producing goods mainly for own consumption”, and those doing “voluntary work”, or “unpaid family work”);  unemployed (see definition above);  not in the labour force (those not employed or unemployed).
Optional definitions of unemployment are also provided below.

102 4.7.2 Economic activity The total labor force of 98,978 people is defined as those being employers (1,369) or self employed (15,920), employees (25,006), those that did unpaid work (10,288), subsistence work (41,877) and the unemployed (4,518) (Figs.97 and 98, and App.21).

The paid employed people are defined as those who “work for pay” and “work to support the household by producing goods mainly for sale”.
The total number of paid employed people consisted of 42,295 people, 25,916 (61%) males and 16,379 (39%) females.

From an urban–rural perspective, 18,016 (43%) of the paid workers were in the urban areas (Port Vila and Luganville), and 24,279 (57%) held paying jobs in rural areas.
The non-labor force of 40,664 people is defined as those being full time students (9,008), those engaged in home duties (17,840), the retired (4,480), the disabled (581), and all those who did not work and were not unemployed (did not look for and were not available for work) for various reasons (8,755).

From the urban-rural divide (Fig.98) it is apparent that most employees for wage or salary can be found in the urban areas, while the overwhelming majority of subsistence workers live in the rural areas.
This pattern is furthermore clearly illustrated by comparing the number of people by labor market activities of the different provinces (Figs.99-104).

The only province with more people being employees than people in the substance sector is the province Shefa, with the location of the main urban and administrative center Port Vila.
Another general pattern is the fact that there are more males than females in the labor force categories, while there are almost 5-times more females engaged in home duties than males.
103 Figure 97: Population aged 15 and older by sex and labour market activity, Vanuatu: 2009 Males Females e l p o e p f o r e b m u N 25,000 20,000 15,000 10,000 5,000 0 Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Un-employed Full time student Home duties Retired/Old age Disabled Other* Activity Figure 98: Population aged 15 and older by region and labour market activity, Vanuatu: 2009 e l p o e p f o r e b m u N 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Urban Rural Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Un-employed Full time student Home duties Retired/Old age Disabled Other* Activity 104 Figure 99: Population aged 15 and older by sex and labour market activity, Torba: 2009 Males Females e l p o e p f o r e b m u N 1,600 1,400 1,200 1,000 800 600 400 200 0 Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Unemployed Full time student Home duties Retired/Old age Disabled Other* Activity Figure 100: Population aged 15 and older by sex and labour market activity, Sanma: 2009 e l p o e p f o r e b m u N 6,000 5,000 4,000 3,000 2,000 1,000 0 Males Females Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Unemployed Full time student Home duties Retired/Old age Disabled Other* Activity 105 Figure 101: Population aged 15 and older by sex and labour market activity, Penama: 2009 Males Females e l p o e p f o r e b m u N 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Unemployed Full time student Home duties Retired/Old age Disabled Other* Activity Figure 102: Population aged 15 and older by sex and labour market activity, Malampa: 2009 e l p o e p f o r e b m u N 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Males Females Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Unemployed Full time student Home duties Retired/Old age Disabled Other*
 Activity 106 Figure 103: Population aged 15 and older by sex and labour market activity, Shefa: 2009 Males Females e l p o e p f o r e b m u N 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Unemployed Full time student Home duties Retired/Old age Disabled Other* Activity Figure 104: Population aged 15 and older by sex and labour market activity, Tafea: 2009 e l p o e p f o r e b m u N 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Males Females Employer Self employed Employee for wage/salary Unpaid worker Subsistence work Unemployed Full time student Home duties Retired/Old age Disabled Other* Activity 107 4.7.3 Labour force participation rate, employment–population ratio, and unemployment rate The labour force participation rate (LFPR) is the number of people in the labour force by a given age and sex and/or place of rural–urban residence, divided by the corresponding total population with the same characteristics, multiplied by 100.
The employment–population ratio (EPR) is the number of people employed in cash work by a given age and sex and/or place of rural–urban residence, divided by the corresponding total population with the same characteristics, multiplied by 100.

The unemployment rate is the number of people unemployed by a given age and sex and/or place of rural–urban residence, divided by the population in the labor force with the same characteristics, multiplied by 100.

The LFPR is calculated at 70.9% in Vanuatu in 2009.
At the same time the EPR was only 30.3%, and the unemployment rate for Vanuatu was 4.6% (Fig.105-107 and Table 38).

The LFPRs were higher for males (80.4) than for females (61.4), so was the employment- population ratio with 37.2 and 23.4 for males and females respectively.

The pattern of higher male than female LFPRs and EPRs can be observed in all regions of Vanuatu.
From an urban–rural perspective, the LFPR were higher in the rural than urban areas, and the EPR were about twice as high in the urban than the rural areas.

Torba was the province with the lowest EPR and Shefa had the highest EPR.

The unemployment rates were higher for females than males, and considerably higher in the urban than the rural areas.

The lowest unemployment rates were in Torba and Penama, and the highest in Shefa.
108 Figure 105: Population aged 15 and older by sex and labour force participation rate: Vanuatu: 2009 Total Males Females 80.4 70.9 70.4 61.4 61.5 52.3 84.3 86.2 86.3 87.2 84.1 81.3 79.6 74.4 77.1 73.0 68.4 75.6 73.0 73.5 72.3 64.8 64.5 64.7 63.7 61.6 53.5 ) % ( e t a r n o i t a p i c i t r a p e c r o f r u o b a L 100 90 80 70 60 50 40 30 20 10 0 VANUATU Urban Rural Torba Sanma Penama Malampa Shefa Tafea Place of residence Figure 106: Population aged 15 and older by sex and employment–population ratio: Vanuatu: 2009 ) % ( o i t a r n o i t a l u p o p - t n e m y o l p m E 100 90 80 70 60 50 40 30 20 10 0 Total Males Females 55.5 47.1 37.2 38.2 30.3 23.4 30.1 24.0 18.0 19.5 13.7 8.1 50.7 43.0 34.9 35.8 33.7 28.5 28.0 27.9 21.1 22.4 21.8 16.0 17.7 13.2 9.3 VANUATU Urban Rural Torba Sanma Penama Malampa Shefa Tafea Place of residence 109 Figure 107: Population aged 15 and older by sex and unemployment rate: Vanuatu: 2009 ) % ( e t a r t n e m y o l p m e n U 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 14.7 Total Males Females 11.9 9.9 9.4 7.7 6.5 6.6 6.4 6.8 5.2 4.6 4.1 4.1 4.4 3.8 2.3 2.2 2.4 0.2 0.3 0.1 2.2 1.9 1.7 0.5 0.5 0.5 VANUATU Urban Rural Torba Sanma Penama Malampa Shefa Tafea Place of residence 110 Table 38: Population aged 15 and older by sex, place of residence, labour force participation rate, employment–population ratio, and unemployment rate, Vanuatu: 2009 Residence/Sex Labour force participation rate Employment- population ratio Vanuatu Males Females Urban Males Females Rural Males Females Torba Males Females Sanma Males Females Penama Males Females Malampa Males Females Shefa Males Females Tafea Males Females Unemployment rate a 4.6 Unemployment rate b 5.5 4.1 5.2 11.9 9.9 14.7 2.3 2.2 2.4 0.2 0.3 0.1 4.1 3.8 4.4 0.5 0.5 0.5 1.9 1.7 2.2
a = the unemployed are strictly defined as persons who did not work in the week prior to the census (other than those who had a job but were not at work during the reference week), but spent time looking for work, and were available to work if a job was offered to them.
b = the unemployed include people that did not work, but did not look for work because they believed that no work was available, or because of poor weather conditions, or because they could not afford transportation costs or had other problems with transportation to work The LFPR, EPR and unemployment rates by age and sex and urban-rural residence are presented in Figures 108-116.
The general pattern is low participation rates for the population 15-19 years when many of the teenagers are still attending school, or struggling to enter the labor market, 111 before the rates sharply increase.

They reach a plateau at ages 30-54, after which the rates gradually decrease.
It is interesting to see that a large proportion of the population aged 60 years and older was still in the labour force indicating that many older people keep providing economically for themselves and their household/families.

Not surprisingly, the labour force participation rate and the employment–population ratio were higher for males than for females at all ages.

The labour force participation rate for females did not exceed 71% at any age, while that of males was more than 90% at ages 30–49.
In terms of the employment–population ratio, almost 50% of all males at aged 30–49 were employed as paid workers.

In contrast, the age groups with the highest percentage of females in paid employment never exceeded 30%.

The unemployment rates show a very different pattern than the LFPR and EPR with the highest unemployment rate for the young job seekers aged 15-19 years.

Unemployment rates rapidly decrease with increasing age.
The unemployment rates are considerably higher at all ages in the urban than the rural areas, and they are higher for females than for males.
112 Figure 108: Population aged 15 and older by age, sex and labour force participation rate (LFPR), Vanuatu: 2009 ) % ( R P F L 100 90 80 70 60 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females Figure 109: Population aged 15 and older by age, sex and labour force participation rate (LFPR), Urban areas: 2009 ) % ( R P F L 100 90 80 70 60 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females 113 Figure 110: Population aged 15 and older by age, sex and labour force participation rate (LFPR), Rural areas: 2009 ) % ( R P F L 100 90 80 70 60 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females Figure 111: Population aged 15 and older by age, sex and employment–population ratio (EPR), Vanuatu: 2009 ) % ( R P E 100 90 80 70 60 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females 114 Figure 112: Population aged 15 and older by age, sex and employment–population ratio (EPR), Urban areas: 2009 ) % ( R P E 100 90 80 70 60 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females Figure 113: Population aged 15 and older by age, sex and employment–population ratio (EPR), Rural areas: 2009 ) % ( R P E 100 90 80 70 60 50 40 30 20 10 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females 115 Figure 114: Population aged 15 and older by age, sex and unemployment rate (%), Vanuatu: 2009 ) % ( e t a r t n e m y o l p m e n U 12 10 8 6 4 2 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females Figure 115: Population aged 15 and older by age, sex and unemployment rate (%), Urban
 areas: 2009 ) % ( e t a r t n e m y o l p m e n U 40 35 30 25 20 15 10 5 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females 116 Figure 116: Population aged 15 and older by age, sex and unemployment rate (%), Rural areas: 2009 ) % ( e t a r t n e m y o l p m e n U 40 35 30 25 20 15 10 5 0 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70+ Age group Total Males Females 4.7.4 Employment status About 47% of all people in paid employment were working for a private enterprise (19,886) which is comprised of 18,517 private employees and 1,369 employers.
Another 24% produced goods for sale (10,046), 15% were working in the public service (6,489), and 14% were self employed (5,874) (Fig.55).

However, this pattern is very different for the urban and rural areas, and per province.

While only 3% of all people in paid employment in the urban areas produced goods for sale, it was 39% in the rural areas.

On the other hand, 64% of urban paid workers worked as private employees, it was only 28% in the rural areas.
Almost half of all paid workers in the provinces of Torba, Penama, and Malampa produced goods for sale.

The highest proportion of government employees were found in Torba (26%), and the lowest proportion in Penama with only 12% of all people in paid employment.

The highest proportions of employees in the private sector were found in Shefa (58%) and the lowest proportion with only 10% in Penama.

However, 30% of all paid workers there were self employed.
117 Figure 117: Population in paid employment by employment status and place of residence, Vanuatu: VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Employee - government Employee - private Employer Self employed Producing goods for sale 4.7.5 Employed population by industry group The vast majority of employed people in Vanuatu were employed in the agriculture, forestry, and fishing sector with 57,191 people and 61% of all employed people.
The second and third largest groups were those in wholesale and retail trade, repair of motor vehicles with 7,746 employees, and public administration and defense, compulsory social security with 7,075 workers (Fig.118).

While household activities were dominated by females, most of all other industry groups employed more males than females.
Comparing the urban-rural areas, the employment situation is completely different; while three- quarter of all employed people in the rural areas were in the agriculture, forestry, and fishing sector (55,816), this was only 7% of all employed people in the urban areas (1,375).
On the other hand, while 19% of employed persons in the urban areas were working in wholesale and retail trade, repair of motor vehicles, another 15% in public administration and defense, compulsory social security, and 12% in Construction, the proportions employed in these specific sectors in the rural areas were very small (Fig.119).
118 Figure 118: Employed population by sex and industry, Vanuatu: 2009 Males Females y r t s u d n I Activities of extraterritorial organizations and bodies Others Household activities* Other services Arts entertainment and recreation Human health and social work Education Public administration and defense, compulsory social security Administrative and support services Professional, scientific, and technical activities Real estate Financial and insurance Information and communication Accomodation Transport and storage Wholesale and retail trade, repair of motor vehicles Electricity, gas, steam and air conditioning supply Construction Manufacturing Agriculture, forestry, and fishing *Activities of households as employers, undifferentiated goods-and service-producing activities of household for own use 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 Number of people 119 Figure 119: Employed population by urban-rural residence and industry, Vanuatu: 2009 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 18 7 15 9 8 19 12 4 7 5 5 5 All other sectors Household activities* Public administration and defense, compulsory social security Accomodation Transport and storage 76 Wholesale and retail trade, repair of motor vehicles Construction Manufacturing Agriculture, forestry, and fishing Urban Rural 4.7.6 Employed population by occupational group The largest numbers of employed workers were in the category of skilled agricultural forestry and fishery workers with 49,769 people or 53% of all employed people (Figs.120-121).
The second largest occupational group were with 13% the elementary occupations (12,314), followed with 9% by the service and sales workers (8,172), and with 8% of the craft and related trades workers (7,649).

While the elementary occupations, the service and sales workers, and clerks, service workers were dominated by females, all other categories were dominated by males.

This was especially so for plant and machine operators and assemblers, as well as the craft and related trades.
As with industry, the difference between the urban and rural areas in terms of occupational groups is evident: while two-third of all occupations were skilled agricultural forestry and fishery workers in the rural areas (48,843), there were only 4% in the urban areas (926).

On the other hand, while 24% of all occupations in the urban areas were service and sales workers, and another 18% were craft and related trades workers, these categories were small percentage-wise in the rural areas.
It is worth noting that 16% of all occupations in the urban areas were professionals and managers compared to just 5% in the rural areas.
120 Figure 120: Employed population by sex and occupation, Vanuatu: 2009 Elementary occupations Plant and machine operators and assemblers Craft and related trades workers Skilled agricultural forestry and fishery workers n o i t a p u c c O Service and sales workers Clerical support workers Technicians and associate professionals Professionals Managers Armed forces Females Males 0 10,000 20,000 30,000 40,000 50,000 Number of people Figure 121: Employed population by place of residence and occupation, Vanuatu: 2009 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 14 7 18 4 24 8 6 10 6 Urban 13 5 66 4 4 Rural Not Stated Elementary occupations Plant and machine operators and assemblers Craft and related trades workers Skilled agricultural forestry and fishery workers Service and sales workers Clerical support workers Technicians and associate professionals Professionals Managers 121 4.7.7 Population working overseas The census included a question on whether a person worked for money overseas during the last 12 months before the census.
Almost 6% (7,895) of the population 15 years and older answered yes, 4,430 males and 3,465 females.

The numbers of overseas workers were unevenly divided among the different provinces, since about half of all workers were from Shefa province alone.

Equal numbers were from Tafea and Sanma (Fig.122).


Figure 122: Population 15 years and older who worked overseas during the last 12 months before the census by place of residence, Vanuatu: 2009 126 1,198 1,187 3,974 521 889 TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Vanuatu enjoys the benefits of migrant labour with Government agreements with New Zealand and Australia.

The most popular country was New Zealand where 86% of all people worked.
The New Zealand Recognized Seasonal Employer (RSE) programme began in 2007 with a pilot and larger numbers followed thereafter.

The Australian pilot was in 2009.

The workers are mostly unskilled and are mainly involved in agricultural work (Fig.123).

122 Figure 123: Destination countries of overseas workers (%), Vanuatu: 2009 7% 3% 4% New Zealand Australia New Caledonia Other countries 86% 123 5.
HOUSEHOLD CHARACTERISTICS 5.1 Household size The number of (private) households increased from 27 thousand in 1989 to 47 thousand in 2009, an overall increase of more than 20 thousand households (Table 39).

there were 209 non-private dwellings (institutions) In addition, including accommodations such as hotels and hostels for short-term visitors, as well as hospitals and a prison.

in 2009, The overall average household size decreased from 5.2 to 4.8 people per household between 1989 and 2009.
Household growth rates also decreased, from 2.8% to 2.6% between 1989-99 and 1999-2009 (Table 40).
Table 39: Population in private households, number of private households and average household size, by place of residence, Vanuatu: 1989, 1999 and 2009 Place of residence Num ber of people in private households Num ber of private household Average Household size 1989 1999 2009 1989 1999 2009 1989 1999 2009 VANUATU 142,419 186,678 228,883 27,167 36,415 47,373 URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Table 40: Number of private households and household change, Vanuatu: 1989, 1999 and 2009 Place of residence Num ber of private household in num bers Household Change in (%) 1989 1999 2009 1989-1999 1999-2009 1989-1999 1999-2009 1989-1999 Annual Grow th Rate 1999-2009 VANUATU 27,167 36,415 47,373 URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Doubling Tim e 2009 1999 25.1 12.5 33.4 33.4 19.4 41.0 58.8 15.2 37.1 26.6 20.6 29.3 25.3 25.1 33.5 33.5 18.4 80.3 124 Figure 124: Average household size (number of people per household) by place of residence, Vanuatu: 2009 5.2 4.8 4.8 4.8 4.8 4.8 4.5 4.5 5.5 l e z i s d o h e s u o h e g a r e v A 6 5 4 3 2 1 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Torba and Tafea province had the highest average household size with 5.2 and 5.5 persons per household respectively.
Average household size for Penama and Malampa were the lowest with 4.5 people per household (Fig.124).

In 2009, the most common household size was 4, accounting for 17 % of all private households (Table 41 and Fig.125).

The highest proportion of people, however, lived in households with 6 people, which accounted for 18 % of all people.
Five percent of the population lived in households with 12 or more people, while 1 % of the population lived in single-person households, which accounted for 6% of all households.
125 Table 41: Number of private households by household size and people per household (%), Vanuatu: 2009 Household size Private Households People per household size 13+ Total Number 2,997 4,851 7,199 8,253 7,735 6,994 3,454 2,398 1,347 789 491 377 488 47,373 % 6.3 10.2 15.2 17.4 16.3 14.8 7.3 5.1 2.8 1.7 1.0 0.8 1.0 100.0 Number 2,997 9,702 21,597 33,012 38,675 41,964 24,178 19,184 12,123 7,890 5,401 4,524 7,636 228,883 % 1.3 4.2 9.4 14.4 16.9 18.3 10.6 8.4 5.3 3.4 2.4 2.0 3.3 100.0 Figure 125: Distribution of households and people living in private households by household size (%), Vanuatu: 2009 % of household % of people e g a t n e c r e P 20 18 16 14 12 10 8 6 4 2 0 13+ Household Size 126 5.2 Household composition Data on household composition were established by identifying a head of household who served as a reference person to whom all other people in the household, in terms of family membership, are related (Table 42).
Approximately 4 out of 5 heads of household (79%) in Vanuatu were men (37,442) with one-in- five (9,931) of households headed by women.

Sons and daughters made up the most household members with 44%.

Spouses of head of households comprised of 15% of the total household members.

Eight per cent of all household members were other relatives or not related to the head of household.

Interestingly there were only 2,427 male spouses recorded in the census, while there were 9,931 female heads of households.
Apparently a woman is in most cases only the head of household if she has no spouse, or if he is not present (overseas).
Table 42: Population by household composition (relationship to head of household), Vanuatu: 2009 Relationship Head of household Spouse of head Son/Daughter Adopted son/daughter Son in law/daughter in law Grandchild Parent/Parents in law of head Brother/Sister (including in laws) Other relatives Not related/friend Total Total 47,373 35,127 99,710 1,967 2,261 14,261 3,396 6,048 17,731 1,009 228,883 in numbers in percentage Male 37,442 2,427 52,531 995 750 7,683 1,075 3,371 9,393 615 116,282 Female 9,931 32,700 47,179 972 1,511 6,578 2,321 2,677 8,338 394 112,601 Total 21 15 44 1 1 6 1 3 8 0 100 Male 32 2 45 1 1 7 1 3 8 1 100 Female 9 29 42 1 1 6 2 2 7 0 100 5.3 Household language Sixty-three per cent of all households in Vanuatu spoke local languages and 34% used Bislama as the main spoken language in their households.
Only 2% used English and 1% used French as the main spoken languages in their households.

(Fig.126) Bislama was more dominant in the urban areas while local languages were more commonly spoken in the rural areas.
127 Figure 126: Proportion of private households by main language spoken, by place of residence (%), Vanuatu: 2009 TAFEA SHEFA MALAMPA PENAMA SANMA TORBA RURAL 40 74 91 92 53 52 47 86 77 URBAN 21 71 VANUATU 63 34 8 4 2 8 25 14 22 5 2 2 0 10 20 30 40 50 60 70 80 90 100 percentage Local language Bislama English French Others 5.4 Household income In 2009, 46% of the main source of household income, in Vanuatu, was from the sale of fish or crops or handicrafts.
Another 34% of the main household income was from wages or salary, 7% of the household income was from own business activities and 5% household income from other sources.

Six percent of all households in Vanuatu do not have any income.

When comparing the urban and rural areas for income distribution it is clearly shown in Figure 127 that 81% of all urban households stated that their main source of income was wages or salary and only 18% of all rural household who stated these main sources of income.
In contrast, 60% of all rural households stated the sale of that fish/crops/handicrafts which compares to 3% of urban households.

their main source of is from income When comparing the household income distribution by province it is evident that the sales of fish/crop/handicraft and wages or salaries were significant contributors in terms of household income (Fig.

128).
128 Figure 127: Proportion of private households by main source of household income and urban and rural residence (%), Vanuatu: 2009 % 100 90 80 70 60 50 40 30 20 10 0 7 5 46 34 6 VANUATU 7 3 81 4 URBAN 7 6 60 18 6 RURAL Own business Others Sale of fish/crops/ handicrafts House Rent Remittances Land lease Wages/Salary No Income Figure 128: Proportion of private households by main source of household income and by province (%), Vanuatu: 2009 100 90 80 70 60 % VANUATU TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Own business Others Sale of fish/crops/ handicrafts House Rent Remittances Land lease Wages/Salary No Income 129 5.5 Amenities and capital goods Please note that the following data for this section are presented as percentages of all private households by place of residence.
5.5.1 Private households by housing and land tenure The majority of households in Vanuatu (81%) owned their dwelling outright (Fig.129), 12% rented their dwelling and 6 % stayed in rent free dwelling.

The proportion of households renting was highest in the urban areas with 39%.

Shefa province had the highest proportion of people renting (28%) compared to the other provinces.
Figure 129: Proportion of private households by place of residence and housing tenure (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Owned Rent free Rented Others The majority of households in Vanuatu (72%) resided on customary land (Fig.130), 11% lived on occupied land with no informal arrangement and 7% and 6% lived on urban and rural land leases.
Forty eight percent of households in the urban areas had urban leases, 18% rural leases, another 18% customary leases and 13% occupied land with informal arrangement.

Rural areas had 82% of households living on customary land, 11% occupying land with an informal arrangement and 4% living with rural lease.

130 Shefa and Sanma have similar patterns of land tenure distribution because urban areas are part of these two provinces.
130: Proportion of private households by place of residence and land tenure (%), Vanuatu: 2009 TAFEA SHEFA MALAMPA PENAMA SANMA TORBA RURAL 54 53 93 82 91 88 82 15 18 8 14 17 4 12 15 5 8 5 7 4 11 13 URBAN 18 18 48 VANUATU 72 6 7 11 0 10 20 30 40 50 % 60 70 80 90 100 Customary Rural Lease Urban Lease Occupy with informal arrangements Others 5.5.2 Private households by living quarters The majority, 80% of Vanuatu households had living quarters of one family house detached from any other house, 13% of households were one family house attached to one or more houses and 3% of households were living in buildings with 2 or more apartments (Fig.131).
Living quarters of one family house attached to one or more houses was much higher in the urban areas (32%) than in the rural areas (7%).

Also it was much higher in Shefa province compared to the other provinces.
131 Fig 131: Proportion of private households by place of residence and living quarters (%), Vanuatu: 2009 7 9 11 6 5 26 88 95 87 94 85 86 64 13 80 7 32 56 % 100 90 80 70 60 50 40 30 20 10 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Others Lodging house Dwelling attached to a shop or other non-residential building Building with 2 or more households which share a kitchen/toilet Building with 2 or more apartments One family house attached to one or more houses One family house detached from any other house 5.5.3 Private households by age of dwelling The distribution of dwellings by age of dwelling is displayed in Figure 132.
It shows that about 40% of all dwellings in Vanuatu were between 1 and 5 years old, 22% between 6 to 9 years, 17% between 10 to 19 years, 13% 20 years or older and 7% were recently built, and were less than 1 year old.

Most urban dwellings were 10 years and older while most dwellings in the rural areas were younger than 10 years.
132 Figure 132: Proportion of private households by place of residence and age of dwelling (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Less than 1 year 1 to 5 years 6 - 9 years 10 - 19 years 20+ years 5.5.4 Private households by number of rooms The distribution of dwellings by number of rooms is displayed in Figure 133.
It shows that the majority (39%) of all dwellings in Vanuatu had 2 rooms, and another 24% had 3 rooms, 18% had 1 room and 12% had 4 rooms.

The average number of rooms was about 2.6 rooms per dwelling (Table 43).

The average number of rooms was higher in urban than in rural areas, and higher in Shefa than the other provinces.
Table 43: Average number of rooms per dwelling by place of residence, Vanuatu: 2009 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA 2.6 2.8 2.5 2.0 2.3 2.6 2.5 2.8 2.4 133 Figure 133: Proportion of private households by place of residence and number of rooms (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA 5.5.5 Private households by construction material used for dwelling Forty three percent of the material used for the walls of private dwellings was traditional materials, followed by concrete (25%), 19% were metal walls, wood or timber (10%) and makeshift or improvised materials are 3% (Fig.134).
Concrete walls are more popular in urban areas compared to traditional materials in the rural areas.

Roofing metal accounted for 48% of the material used for roofs (Fig.135) and 45% traditional material roofing.

Metal roofing was commonly used in urban areas compared to traditional material roofing in the rural areas.
Sixty percent of all dwellings in Vanuatu had concrete (cement) floors (Fig.136), which was the preferred material used for the floors in all province except in Torba and Tafea which had traditional materials as the preferred floor material.
134 Figure 134: Proportion of private households by place of residence and main type of material used for the walls of dwellings (%), Vanuatu: 2009 100 3 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Traditional materials Concrete Metal Makeshift or improvised materials Wood Others Figure 135: Proportion of private households by place of residence and main type of material used for the roofs of dwellings (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Metal Traditional materials Makeshift or improvised materials Concrete Wood Others 135 Figure 136: Proportion of private households by place of residence and main type of material used for the floors of dwelling (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Concrete Traditional materials Metal Makeshift or improvised materials Wood Others 5.5.6 Private households by water source for drinking and washing The distribution of Vanuatu dwellings by main source of drinking water is displayed in Figure 137.
It shows that the majority of dwellings (27%) used a shared pipe, another 19% used a village tank, 18% used private pipe, 15% used household tanks and 11% used the river or lake or spring.

Private piped water was widely used in the urban areas (43%) compared to in the rural areas.

The main source of drinking water varies widely between provinces.

The distribution of dwellings by main source of washing water is displayed in Figure 138.
It shows the great variation by main source of washing water in the provinces.
136 Figure 137: Proportion of private households by place of residence and main source of drinking water (%), Vanuatu: 2009 100 90 80 70 60 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Piped private Piped shared Village standpipe Well - protected Well - unprotected Household tank Village tank Bottled water River lake spring Others Figure 138: Proportion of private households by place of residence and main source of washing water (%), Vanuatu: 2009 100 90 80 70 60 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Piped private Piped shared Village standpipe Well - protected Well - unprotected Household tank Village tank Sea River lake spring Others 137 5.5.7 Private households by main energy source for lighting and cooking The main source of energy for lighting in Vanuatu was the kerosene lamp used by 48% of all households (Fig.139).
A further 28% of all households used electricity - main grid.

Not surprisingly the urban areas and Shefa province shared the highest usage of electricity main grid.

Torba province had with 37%, the highest number of households using a Coleman lamp as their main energy source of lighting.

The main source of energy for cooking was wood/coconut shells and used by 85% of all households in Vanuatu (Fig.140).

Thirteen percent of all households used gas.


However, gas usage was more dominant in the urban areas and Shefa province compared to the other provinces where the use of wood/coconut shells was preferred.
Figure 139: Proportion of private households by place of residence and main source of lighting (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Electricity - main grid Own generator Kerosene lamp Coleman lamp Solar Candles Gas Wood, Coconut Torch Others 138 Figure 140: Proportion of private households by place of residence and main source for cooking (%), Vanuatu: 2009 13 85 100 90 80 70 60 % 50 40 30 20 10 0 40 3 53 3 31 96 99 91 97 98 98 64 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Others Gas Charcoal Wood/coconut shells Kerosene Electricity 5.5.8 Private households by main toilet facility At 37%, the private pit latrine was the main toilet facility used in Vanuatu (Fig.141).
Fifteen percent of all household used flushed private toilets, 14% used private VIP10, 10% used pit latrine-shared and 8 used shared VIP toilets.

Forty three percent of all urban households used a flush private toilet and 22% households used a shared flush toilet.

Rural areas tend to use a private pit latrine, a private VIP and shared pit latrine toilet facility.
10 The ventilated improved pit latrine, or VIP, is a pit toilet with a pipe (vent pipe) fitted to the pit, and a screen (fly screen) at the top outlet of the pipe.

VIP latrines are an improvement to overcome the disadvantages of simple pit latrines, i.e.

fly and mosquito nuisance and unpleasant odors.

The smell is carried upwards by the chimney effect and flies are prevented from leaving the pit and spreading disease.

(Wikipedia, the free encyclopedia [online]).
139 Figure 141: Proportion of private households by place of residence and main type of toilet facility (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA None VIP - shared Water seal - private Pit latrine shared VIP - private Flush shared Pit latrine Water seal - shared Flush private 5.5.9 Private households by means of communication Only 4% of all households in the Vanuatu had a landline phone available (Fig.142).
However 11% of urban households had a landline phone.

The majority of households in Vanuatu (76%) had access to mobile phones (Fig.143).

91% of urban households owned a mobile phone compared to 71% of rural households.

The percentage of households owning a mobile phone in Torba was, with 10%, very low.

The number of households with an internet connection was very low in Vanuatu; only 3% of all households had access to internet (Fig.144).
Nine percent of household in urban areas had access to the internet.
140 Figure 142: Proportion of private households by place of residence and availability of a telephone (land-line) (%), Vanuatu: 2009 100 90 80 70 % 60 50 40 30 20 10 0 11 4 VANUATU URBAN 2 RURAL 3 9 TORBA SANMA PENAMA MALAMPA SHEFA 2 TAFEA Yes No Figure 143: Proportion of private households by place of residence and availability of a mobile phone (%), Vanuatu: 2009 100 % 90 80 70 60 50 40 30 20 10 0 91 76 71 81 71 75 87 63 10 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No 141 Figure 144: Proportion of private households by place of residence and accessibility to the Internet connection (%), Vanuatu: 2009 100 90 80 70 60 % 50 40 30 20 10 0 9 3 8 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No 5.5.10 Private households by main means of waste disposal During the 2009 census, information was collected with respect to how households manage their waste.
There were 8 different means of waste disposal in Vanuatu: Authorized waste collection, taken to a central place, burning, recycling, lagoon/ocean/stream, burying, composting and others means of waste disposal.

In Vanuatu, 52% of all households burned waste, 18% used authorized waste collection and 12% took waste to a central place (Fig.145).

Urban areas had 67% of households who used the authorized waste collection.

In contrast, there was very little waste collection in the rural areas.
Burning waste was widely used by rural households (61%) as the main means of waste disposal compared to the 24% of urban households.

Four percent of all households in Torba used the lagoon or ocean or stream to dispose of waste.
142 Figure 145: Proportion of private households by place of residence and main mode of waste disposal (%), Vanuatu: 2009 % VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Others Lagoon/ocean/stream Composting Recycling Bury Burn Take to central place Authorised waste collection 5.5.11 Private households involved in agricultural cash crop and fisheries activities The 2009 census included several questions on whether the households were engaged in agricultural and fisheries activities.
In general, agricultural cash cropping involvement was more common in the rural areas compared to the urban areas.

For Vanuatu, 48% of all households were involved in kava agricultural activity, 50% in coconut, 18% cocoa, 15% vanilla, 13% sandalwood, 3% coffee, 2% pepper and 18% involved in other agricultural cash crop activities.

The province with the highest involvement in kava cultivation was Penama province where 84% of all households were engage in kava agricultural activity (Fig.146).
This is in contrast to urban households who had only 3% of its household engaged in kava activity.

Malampa province led the coconut agricultural activity, by having 88% of its household engaged in coconut cultivation (Fig.147).

The urban areas again recorded the lowest proportion of households engaged in this activity (4%).

Table 44 shows the engagement of households in other agricultural cash crops such as cocoa, coffee, sandalwood, pepper, vanilla and other crops.
143 Figure 146: Proportion of private households by place of residence and agricultural activities (%), Kava, Vanuatu: 2009 TAFEA 72 SHEFA 14 MALAMPA PENAMA SANMA TORBA RURAL URBAN 3 84 58 53 69 63 VANUATU 48 0 10 20 30 40 60 70 80 90 100 50 % Yes No Figure 147: Proportion of private households by place of residence and agricultural activities (%), Coconut, Vanuatu: 2009 TAFEA SHEFA MALAMPA PENAMA SANMA TORBA RURAL 23 88 56 51 52 71 64 URBAN 4 VANUATU 50 0 10 20 30 40 50 60 70 80 90 100 % Yes No 144 Table 44: Proportion of private households by place of residence and agricultural activity (%), Vanuatu: 2009 Place of residence VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Cocoa Coffee Sandalwood Pepper Vanilla Other crops Yes 18 0 24 4 20 18 64 3 1 No 82 100 76 96 80 82 36 97 99 Yes 3 0 4 0 0 0 1 1 19 No 97 100 96 100 100 100 99 99 81 Yes 13 1 17 5 11 4 11 6 54 No 87 99 83 95 89 96 89 94 46 Yes 2 0 2 2 3 2 3 0 1 No 98 100 98 98 97 98 97 100 99 Yes 15 2 19 9 21 24 27 5 9 No 85 98 81 91 79 76 73 95 91 Yes 18 3 23 11 13 46 10 15 17 No 82 97 77 89 87 54 90 85 83 Fisheries included two types of fishing activities namely marine fishing and fresh water fishing activity and the census included several questions on whether the households were engaged in fisheries activities for subsistence, sale or both.
The data shows that 32% of all households in Vanuatu were engaged in marine fishing; 26% were engaged but only for subsistence and a further 6% of households were engaged in marine fishing for both subsistence and sale purposes (Fig.

148).

Furthermore, 17% of all households in Vanuatu were engaged in fresh water fishing.

Of this number, 15% were engaged for subsistence purposes (Fig.149).

Torba province has most of its households (57%) engaged in marine fishing but mainly as subsistence activities (Fig.
148).
145 Figure 148: Proportion of private households by place of residence and marine fishing activities (%), Vanuatu: 2009 9 89 6 26 68 7 31 61 17 57 25 % 100 90 80 70 60 50 40 30 20 10 0 5 25 7 24 6 34 5 32 4 17 77 70 68 59 62 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA No fishing Subsistence only Sale only Both Figure 149: Proportion of private households by place of residence and fresh water fishing activities (%), Vanuatu: % 40 30 20 10 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA No fishing Subsistence only Sale only Both 146 5.5.12 Private households owning or raising livestock The following section provides an overview on the number of livestock counted (Table 45), and the proportion of households that raise or own livestock (Figs.150 to 154).
In general, livestock such as chickens (64%), pigs (45%) and cattle (29%) were commonly raised by households in Vanuatu, but only a few households raised goats (8%) and horses (3%).
Table 45: Total number of livestock, Vanuatu: 2009 Place of residence VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Number of livestock Pigs 108,056 Goats 34,086 Cattle 105,051 Horses 5,559 Chickens Figure 150: Proportion of private households by place of residence raising pigs (%), Vanuatu: 2009 5 45 40 57 56 52 66 22 76 100 90 80 70 60 % 50 40 30 20 10 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No 147 Figure 151: Proportion of private households by place of residence raising goats (%), Vanuatu: 2009 8 2 9 6 3 6 6 23 100 90 80 70 60 % 50 40 30 20 10 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No Figure 152: Proportion of private households by place of residence raising cattle (%), Vanuatu: 2009 1 10 29 30 39 39 35 43 42 100 90 80 70 60 % 50 40 30 20 10 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No 148 Figure 153: Proportion of private households by place of residence raising horses (%), Vanuatu: 2009 100 3 4 9 6 90 80 70 60 % 50 40 30 20 10 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No Figure 154: Proportion of private households by place of residence raising chicken (%), Vanuatu: 2009 14 32 64 80 79 67 87 81 90 100 90 80 70 60 % 50 40 30 20 10 0 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No 149 5.5.13 Private households and availability of various household items This section briefly summarizes the availability of a variety of household items and appliances.
The different sections include a summary table presenting the total number of items by place of residence.

Subsequently graphs are added that show the proportion of households by place of residence with at least one item that is in working order.

It excludes any items that were broken, borrowed or rented.

The graphs therefore are simply divided into two categories: ‘yes’ if the household owns the item or ‘no’ if it does not own the item.
5.5.13.1 Private households and availability of kitchen appliances In 2009.

27% of all households in Vanuatu had gas stoves; 15% in rural areas and 65% in urban households (Table 46 and Fig.

155).

Figure 156 shows that, 13% of all households in Vanuatu had a fridge or freezer; 6% and 37% of rural and urban households had this kitchen appliance.
Table 46: Number of items of cooking appliances by place of residence, Vanuatu: 2009 Place of Residence VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Item Gas stove Fridge/Freezer 14,024 8,024 6,000 104 2,519 802 1,086 8,945 568 7,670 5,090 2,580 45 1,179 230 322 5,657 237 150 Figure 155: Proportion of private households by place of residence and availability of a gas stove (%), Vanuatu: 2009 52 TAFEA 8 SHEFA MALAMPA PENAMA 12 11 SANMA 24 TORBA 5 RURAL 15 URBAN 65 VANUATU 27 0 10 20 30 40 50 % Yes No 60 70 80 90 100 Figure 156: Proportion of private households by place of residence and availability of a Fridge/Freezer (%), Vanuatu: 2009 TAFEA 3 SHEFA 30 MALAMPA PENAMA 3 3 SANMA 10 TORBA 2 RURAL 6 URBAN 37 VANUATU 13 0 10 20 30 40 50 % Yes No 60 70 80 90 100 151 5.5.13.2 Private households and availability of entertainment appliances A minority of households in Vanuatu had the following entertainment appliances; radio (35%), TV (37%), computer (8%) and a DVD player (36%) (Table 47 and Figs.
157 to 160).

However, it is a different scenario when comparing urban and rural households as more urban households had access to radio (53%), TV (72%), computer (22%) and DVD players (68%).
Table 47: Number of items of entertainment appliances by place of residence, Vanuatu: 2009 Place of residence VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Item TV Screen 19,583 Radio Computer DVD Deck Figure 157: Proportion of private households by place of residence and availability of a radio (%), Vanuatu: 2009 TAFEA 18 SHEFA MALAMPA PENAMA SANMA 24 27 TORBA 20 RURAL URBAN 39 29 49 53 VANUATU 35 0 10 20 30 40 50 % No Yes 60 70 80 90 100 Figure 158: Proportion of private households by place of residence and availability of a TV (%), Vanuatu: 2009 19 TAFEA SHEFA MALAMPA 24 PENAMA 19 SANMA 36 TORBA 16 RURAL URBAN 26 VANUATU 37 61 72 0 10 20 30 40 50 % Yes No 60 70 80 90 100 153 Figure 159: Proportion of private households by place of residence and availability of a computer (%), Vanuatu: 2009 TAFEA 3 SHEFA 17 MALAMPA PENAMA 2 2 SANMA 5 TORBA 2 RURAL 3 URBAN 22 VANUATU 8 0 10 20 30 40 50 % No Yes 60 70 80 90 100 Figure 160: Proportion of private households by place of residence and availability of a DVD player (%), Vanuatu: 2009 19 TAFEA SHEFA 58 MALAMPA 26 PENAMA 20 SANMA 36 TORBA 17 RURAL URBAN 26 VANUATU 36 68 0 10 20 30 40 50 % No Yes 60 70 80 90 100 154 5.5.13.3 Private households and availability of various means of transport Only a minority of households in Vanuatu had the following means of transport; motor vehicle (7%), motor bike (2%), boat 3%) and canoe (10%).
Figures 161 to 164 and Table 48) display these various transportation means by place of residence.
Table 48: Number of transport items by place of residence, Vanuatu: 2009 Place of residence VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Item Motor vehicle 4,698 Motorbike 1,158 2,999 1,699 9 818 149 179 3,328 215 423 735 22 198 52 187 612 87 Boat 1,601 394 1,207 43 421 131 252 600 154 Canoe 5,982 293 5,689 646 1,051 607 2,143 876 659 Figure 161: Proportion of private households by place of residence and availability of a motor vehicle (%), Vanuatu: 2009 TAFEA 3 SHEFA 15 MALAMPA PENAMA 2 2 SANMA 7 TORBA RURAL 3 URBAN 18 VANUATU 7 0 10 20 30 40 50 % Yes No 60 70 80 90 100 155 Figure 162: Proportion of private households by place of residence and availability of a motor bike (%), Vanuatu: 2009 TAFEA 1 SHEFA 2 MALAMPA 2 PENAMA 1 SANMA 1 TORBA 1 RURAL 1 URBAN 2 VANUATU 2 0 10 20 30 40 % 50 Yes No 60 70 80 90 100 Figure 163: Proportion of private households by place of residence and availability of a boat (%), Vanuatu: 2009 TAFEA 2 SHEFA 3 MALAMPA 2 PENAMA 2 SANMA 3 TORBA 2 RURAL URBAN VANUATU 3 3 3 0 10 20 30 40 50 % Yes No 60 70 80 90 100 156 Figure 164: Proportion of private households by place of residence and availability of a canoe (%), Vanuatu: 2009 TAFEA 10 SHEFA 4 MALAMPA 21 8 9 PENAMA SANMA TORBA RURAL 13 URBAN 2 VANUATU 10 30 0 10 20 30 40 50 % Yes No 60 70 80 90 100 5.5.13.4 Private households and availability of other appliances Ownership of household appliances such as generator and lawn mowers was asked in the 2009 population census of which 18% and 10% of households in Vanuatu stated that they have access to both.
Table 49 and Figures 165 and 166 display their availability by place of enumeration.
Table 49: Number of other household appliances by place of residence, Vanuatu: 2009 Item Generator Mower Place of residence VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA 9,287 5,681 1,287 2,053 8,000 3,628 339 45 2,049 1,142 1,548 535 1,854 527 SHEFA 2,671 2,956 TAFEA 826 476 157 Figure 165: Proportion of private households by place of residence and availability of a generator (%), Vanuatu: 2009 TAFEA 12 SHEFA 15 MALAMPA PENAMA SANMA 21 21 20 TORBA 17 RURAL 20 URBAN 10 VANUATU 18 0 10 20 30 40 50 % Yes No 60 70 80 90 100 Figure 166: Proportion of private households by place of residence and availability of a mower (%), Vanuatu: 2009 TAFEA 6 SHEFA 15 MALAMPA PENAMA 5 6 SANMA 11 TORBA 2 RURAL 8 URBAN 15 VANUATU 10 0 10 20 30 40 50 % Yes No 60 70 80 90 100 158 5.6 Household bednets The majority of all households in Vanuatu had a bednet (76%) as shown in Figure 167.
In total 130,789 bednets were counted in the 2009 census (Table 50).

More households in the rural areas (88%) had bednets compared to households in the urban areas (38%).
Table 50: Number of private households by place of residence and availability of bednets, Vanuatu: 2009 Place of Residence VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Households with/without bednets Total 47,373 11,606 35,767 1,766 9,213 6,620 7,991 15,930 5,853 Yes 36,028 4,425 31,603 1,723 7,329 5,870 7,336 8,340 5,430 No 11,345 7,181 4,164 43 1,884 750 655 7,590 423 Number of bednets 130,789 11,526 119,263 7,982 25,932 22,622 27,346 25,798 21,109 Fig.
167: Proportion of private households by place of residence and availability of bednets (%), Vanuatu: 2009 100 90 80 70 60 % 50 40 30 20 10 0 98 88 80 89 92 93 52 76 38 VANUATU URBAN RURAL TORBA SANMA PENAMA MALAMPA SHEFA TAFEA Yes No 159 6.

POPULATION PROJECTIONS Timely and accurate information about population trends is in high demand for anyone making decisions in business, research, government and the community.
Knowledge about the current size and structure of a country’s population is needed for the formulation and implementation of policies and programmes in almost all areas of public life.

As policies are aimed at resolving current issues through the achievement of goals in the future, knowledge about future population trends is required.

Activities in areas as diverse as health, environment, poverty reduction, social progress, and economic growth rely on comprehensive and consistent demographic information.
The appropriate method to produce population trends is to prepare estimates and projections of population size and structure by age and sex.

The starting point for any projection is a reliable and current age–sex distribution of a population.

Furthermore, information on recent levels and patterns of fertility, mortality, and migration is needed.

The cohort-component method was used to compute the population projections presented in this report.
This procedure simulates population changes as a result of changes in the components of growth: fertility, mortality and migration.

Based on past information and current levels, assumptions are made about future trends in these components of change.

The assumed rates are applied to the age and sex structure of the population in a simulation that takes into account:  the age at which people die is related to their sex and age;  women have children; and  some people change their place of residence.
The cohort-component method of projecting a population follows each cohort of people of the same age and sex throughout their lifetime, according to their exposure to fertility, mortality and migration11.

The key to making meaningful projections lies in the choice of assumptions about future population developments.

These assumptions concern possible future birth, death and migration rates.
6.1 National projections Projection assumptions As a general guideline, when preparing multiple assumptions about future levels of fertility, mortality and migration, it is advisable to arrive at outcomes that are symmetrical.

This means 11 1994.

Arriaga E.E.

Population analysis with microcomputers, volume I, Presentation of techniques, p. 309–310.

US Census Bureau, Department of Commerce, USA.


160 that the level of low and high, or fast and slow, growth assumptions should be equally positioned with respect to the medium level assumption (i.e.

above and below).

The following demographic inputs were developed for the projections.

Projection period The population projections cover the 45-year period of 2009–2054.

Base population Projections are based on the 2009 Vanuatu census age and sex distribution, adjusted to mid-year 2009.
The population is further adjusted for suspected under enumeration of the age groups 15-24 for males, and to a lesser degree for females.

Table 51 and Figures 168 and 167 show a comparison of the actual 2009 population count, and results of a population projection that used the 1999 population as a starting point (base population), and intercensal fertility and mortality estimates.
A comparison of the counted and projected population shows a very good fit in general, with the exceptions of the 15-24 year age groups.

While these young people are often the age groups most likely affected by migration (people in search for further education and/or employment opportunities overseas), it seems unlikely that there were that many people who had left Vanuatu during the intercensal period.
It was therefore decided to assume that these people must have been missed during the 2009 census count.

In addition, the female population aged 30-44 was slightly adjusted for suspected under enumeration.

In total the enumerated census population of 234,023 was adjusted by 7,000 to 241,023 people (Table 52).
Because the projections should refer to the mid-year of each year of the projection period, the base year population has further been adjusted to a new total of 239,000 for mid-year 2009 (the PAS procedure MOVEPOP has been used to estimate the mid-year population from the November census population) (Table 53).
161 Table 51: Comparison of the projected population with the enumerated population, Vanuatu 2009 Age group 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+ Total 2009 Census 2009 Projection absolute difference percentage difference Male observed 17,310 15,455 14,762 12,027 10,415 9,124 7,790 7,076 5,814 5,066 3,789 3,261 2,192 2,054 1,085 1,871 119,091 Female observed 16,057 14,230 13,159 11,855 11,126 9,291 7,903 7,095 5,709 5,175 3,626 3,102 2,127 1,772 983 1,722 114,932 Male projected Female projected Males Females Males Females -348 1,952 2,114 -129 100 90 328 178 177 64 219 -204 22 -545 4,401 218 112 199 1,353 416 -718 365 400 548 95 204 56 170 -116 99 -527 2,875 1.9 0.3 -2.4 16.2 20.3 -1.4 1.3 1.3 5.6 3.5 4.7 1.9 10.0 -10.0 2.0 -29.1 3.7 1.4 0.8 1.5 11.4 3.7 -7.7 4.6 5.6 9.6 1.8 5.6 1.8 8.0 -6.5 10.1 -30.6 2.5 162 Figure 168: Comparison of the projected male population with the enumerated male population, Vanuatu 2009 Male observed Male projected l e p o e p f o r e b m u N 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+ Age group Figure 169: Comparison of the projected female population with the enumerated female population, Vanuatu 2009 Female observed Female projected l e p o e p f o r e b m u N 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+ Age group 163 Table 52: Adjusted census population, Vanuatu: 16 November 2009 Age group 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total Males 17,310 15,455 14,762 13,977 12,515 9,124 7,790 7,076 5,814 5,066 3,789 3,261 2,192 2,054
 1,085 941 930 123,141 Females 16,057 14,230 13,159 13,205 11,526 9,291 8,203 7,495 6,209 5,175 3,626 3,102 2,127 1,772 983 799 923 117,882 Total 33,367 29,685 27,921 27,182 24,041 18,415 15,993 14,571 12,023 10,241 7,415 6,363 4,319 3,826 2,068 1,740 1,853 241,023 Table 53: Base population for projections, Vanuatu: 1 July 2009 Age group 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total Males 17,150 15,311 14,626 13,847 12,399 9,039 7,719 7,010 5,760 5,019 3,754 3,231 2,172 2,035 1,076 931 921 122,000 Females 15,937 14,124 13,060 13,106 11,440 9,222 8,141 7,439 6,163 5,137 3,598 3,079 2,112 1,758 976 793 916 117,000 Total 33,087 29,435 27,686 26,953 23,839 18,261 15,860 14,449 11,923 10,156 7,352 6,309 4,284 3,793 2,052 1,724 1,837 239,000 164 Fertility The estimated TFR of the period 2009 and associated ASFR, as described in Section 3.1 (Table 11), are used as a starting point, with four different assumptions made about future fertility developments (Fig.170).
The future TFR level of the medium fertility assumption is assumed to reach 2.0, which is the average level of TFR of populations in present-day Australia, France, New Zealand and the United States (App.

22).

This level will be reached (by means of extrapolation) with a pace of fertility decline that is based on Vanuatu’s past fertility trend.

According to this pace, Vanuatu will reach a TFR of 2.0 in the year 2058, and will reach a level of 2.1 at the end of the projection period in 2054.
The reason for choosing the fertility level of countries such as Australia, France, New Zealand and the United States as the future level for Vanuatu is twofold: 1) These countries have completed the “demographic transition” (see explanatory note in App.24).

Appendix 22 shows that the TFR of these four countries has remained at an almost constant level of 2.0 over the last 30 years (1975–2010).

2) They are regarded as the metropolitan focal points of Pacific Island countries.
Therefore the medium fertility assumption is set as follows.

Assumption 1 — Medium Fertility: Fertility decreases to 2.1 in the year 2054 (as described above).

The high and low fertility assumptions were built symmetrically around the medium fertility assumption.

Assumption 2 — High Fertility: The high fertility assumption assumes a TFR of 0.5 higher than the medium fertility level.

Therefore, the level of TFR in 2054 is 2.6.


Assumption 3 — Low Fertility: The low fertility assumption assumes a TFR of 0.5 lower than the medium fertility level.

Therefore, the level of TFR in 2054 is 1.6.

Assumption 4 — Constant Fertility: This is a purely academic assumption, with the purpose to demonstrate what would happen to Vanuatu in terms of population size if the current TFR of 4.1 remains constant at this level for the entire projection period.
165 Figure 170: Estimated past levels of fertility, and future fertility assumptions for projections, Vanuatu: 1954–2054 7.6 8 7 6 5 R F T Year Past level Medium High Low Constant Mortality It is thought that under normal circumstances (meaning in the absence of catastrophes such as wars, epidemics and major natural disasters), the Vanuatu’ health situation and mortality levels will continuously improve throughout the projection period.
The estimated life expectancies at birth [E(0)] — 69.6 years and 72.7 years for males and females, respectively — are used as the starting point for projections in 2009.

These estimates are based on the estimates as outlined in section 3.2.

Assumption: The population projections presented here assumes a rising trend in life expectancy for males and females according to the UN working models of mortality improvement, as described in “World Population Prospects, p. 14412 (App.23).
According to this model, current estimated life expectancies gradually increase and reach 77.3 and 81.4 years in 2054 for males and females, respectively (Fig.171).

Only one assumption regarding mortality is made.

The reason for this is that variations in mortality levels (multiple assumptions) usually have only a minor impact on final projection 12 1995.

United Nations.

World Population Prospects.

NewYork: United Nations.


886 p. 166 results; they also would require the production of too many different scenarios that ultimately would only complicate the presentation of results.

The derived mortality pattern (age-specific death rates) was compared with the different Coale- Demeny and United Nations model life tables using MORTPAK4.1, procedure COMPAR.

The assumption was made that possible under-registration of deaths is not age specific and therefore does not affect the overall pattern of mortality.
It was found that the Coale-Demeny West model pattern resembles most closely the empirical mortality pattern of Vanuatu.
Figure 171: Estimated past levels of mortality, and future mortality assumptions for projections, Vanuatu: 1989–2054 81.4 77.3 72.7 69.6 69.0 65.6 64.2 61.5 ) s r a e y n i ( h t r i b t a y c n a t c e p x e e f i L 85 80 75 70 65 60 55 50 45 40 1989 1999 2009 2019 2029 2039 2049 2054 Males-observed Females-observed Males-projected Females-projected Year Migration Making meaningful assumptions about future migration developments provides the single greatest difficulty for undertaking population projections, because many of the social and economic parameters shaping migration patterns depend largely on countries’ overall social, economic and political developments, as well as environmental factors (e.g.
possible sea level rise, frequency and strength of cyclones).

All of these factors fluctuate widely and are hard to predict.

Migration projections also depend on economic and political developments overseas.

Apart from these global considerations, making assumptions about migration is difficult because reliable information on international arrivals and departures, and especially vital statistics such as annual number of births and deaths is not available.
167 However, at present the Vanuatu population is not known for migrating permanently overseas at any significant measure, while Vanuatu itself is not an immigration country either.
Furthermore, an analysis of the fertility and mortality level reveals that the last intercensal population growth rate (taking under enumeration of the 2009 census into account as described above) closely resembles the current natural growth rate, which implies that there is very little international migration (see section 3.3.2).
In view of the absence of significant past international migration, it is decided to assume zero net migration for the entire projection period as it is impossible to predict what the level of migration would be should it occur in future.

Of course the projections needs to be amended should this situation change.

Projection results The four different fertility assumptions results in four different projections (Table 54 and Fig.172).
These different projections highlight the impact of different levels of fertility on the population size and structure of Vanuatu: The higher the fertility level assumed, the higher the population outcome.
Table 54: Population size according to 4 projection variants, Vanuatu: 2010– 2050 Fertility assumption 2010 2015 2020 2025 2030 2035 2040 2045 2050 Year Constant 245,376 280,245 319,467 362,340 409,251 461,367 519,923 585,596 658,948 High 245,376 279,022 314,579 351,060 387,908 425,211 463,510 502,652 541,896 Medium 245,376 277,503 309,299 340,221 370,392 400,033 429,157 457,130 483,048 Low 245,376 275,995 304,025 329,363 352,879 375,025 395,452 413,275 427,621 The four population projection scenarios are described in detail below: 1) High population scenario.
This projection outcome is determined by applying the high fertility assumption (constant fertility).

This scenario results in a population size of 388 thousand in the year 2030, and 542 thousand in the year 2050.

2) Medium population scenario.

This projection outcome is determined by applying the medium fertility assumption (moderate fertility decline).

This scenario results in a population size of 370 thousand in the year 2030, and 483 thousand in 2050.

3) Low population scenario.


This projection outcome is determined by applying the low fertility assumption (fast fertility decline).

This scenario results in a population size of 353 thousand in the year 2030, and only 428 thousand in the year 2050.

168 4) Constant population scenario.

This projection outcome is determined by assuming that the current high level of fertility remains constant during the entire projection period.
This scenario results in a population size of 409 thousand in the year 2030, and 659 thousand in the year 2050.
Figure 172: Past and future population trends according to 4 projection variants, Vanuatu: 1967–2050 s d n a s u o h T 700 600 500 400 300 200 l e p o e p f o r e b m u N 100 78 0 239 187 142 111 659 542 483 428 1967 1979 1989 1999 2009 2015 2025 2030 2035 2040 2045 2050 2020 Year Past Medium High Low Constant In general it becomes clear that the population will increase substantially regardless of which projection scenario is used.
It can be seen that the impact of the different projections on the population size until the year 2020 are relatively minor.

Significant population differences based on the different projection assumptions can only be expected thereafter.

Figures 173 to 176 provide the comparative results of the various projections, and highlight the differential impact on population size, growth and structure.
The population aged 6–13 — the mandatory school age population — can be expected to increase from its current size of 39 thousand until the year 2020 regardless of the projection scenario used (Fig.173) and will then be almost 60 thousand people; only according to the low population scenario is the school age population expected to decrease after 2020, but it will stay higher than in 2009.

All other projection scenarios will result in a significant higher school age population than in 2009.
169 According to the constant population scenario, assuming constant fertility at its present high level, the school age population would more than double until 2050.

However, according to the medium scenario, the school age population aged 6–13 would increase to about 60,000 people in 2025 and stabilize at that level thereafter.

The general impact on the future population structure by broad age groups can be seen in Table 55 and Figures 174 to 176.
Figure 173: Population aged 6–13 (mandatory school age) according to high, medium, low and constant population projection scenarios, Vanuatu: 2009-2050 s r a e y 3 1 - 6 d e g a e p o e p f o r e b m u N l s d n a s u o h T 120 100 80 60 40 20 0 2009 2015 2020 2025 2030 Year 2035 2040 2045 2050 2009 census High Medium Low Constant Regardless of the projection scenario used, the size of the working age population (aged 15–59) will be much larger than in 2009 (129 thousand), and will be 159 thousand in 2015, and will further increase to more than 200 thousand people in 2030.
According to the medium variant scenario, the working age population will reach 300 thousand in the year 2050.

Another general outcome is that the population aged 60 and older will be significantly larger than 14 thousand in 2009, regardless of the projection scenario used.

The ‘elderly’ population will be 17 thousand in 2015, 32 thousand in 2030, and 68 thousand in 2050.
Therefore the population will grow older regardless of which projection is used, as is expressed in the median age, which will increase from 20.7 years in 2009 to 25 in 2030 and 32 in 2050 according to the medium projection variant.

170 The proportion of the young population aged 0–14 (as part of the total population) will decrease regardless of the type of projection scenario used (Table 55).
However, its size will increase at least until 2030, and only under the low projection scenario would the population aged 0-14 decrease thereafter.

The size of the population younger than 15 years is likely to increase from about 91 thousand in 2009 to about 112 thousand in 2030 (according to the medium population scenarios), and would be 115 thousand in 2050.
On the other hand the young population will be much higher than that if fertility levels remain high and there could be almost 150 thousand people aged 0-14 years in 2050.

The three different projection scenarios will produce very different population growth rates: the high population scenario will result in an annual population growth rate of 2.4%, while the medium population scenario will only produce 1.5% annual growth in 2030, and only 1% in 2050.
Finally, the different projections result in very different age-dependency ratios: the lower the level of future fertility, the lower the age-dependency ratio.
Table 55: Population structure and indicators according to three different projection scenarios, Vanuatu: 2010, 2030 and 2050 Indicator Population by broad age groups (%) 0 - 14 years 15 - 59 years 60 years and older Dependency ratio Median age Average annual growth rate (%) 2010 Constant High Medium Low Figure 174: 2015 population projections by broad age groups according to four scenarios s d n a s u o h T e z i s n o i t a u p o P l 600 500 400 300 200 100 0 14 129 91 17 159 104 2009 census Constant 17 159 103 High 17 159 101 Medium Projection variant younger than 15 years 15 - 59 years 60 years and older 17 159 100 Low Figure 175: 2030 population projections by broad age groups according to four scenarios s d n a s u o h T e z i s n o i t a u p o P l 600 500 400 300 200 100 0 32 229 148 14 129 91 32 228 128 32 227 112 2009 census Constant High Medium Projection variant younger than 15 years 15 - 59 years 60 years and older 32 225 96 Low 172 Figure 176: 2050 population projections by broad age groups according to four scenarios s d n a s u o h T e z i s n o i t a u p o P l 700 600 500 400 300 200 100 0 14 129 91 68 361 230 68 325 149 68 300 115 2009 census Constant High Medium Projection variant younger than 15 years 15 - 59 years 60 years and older 68 275 85 Low The different impacts on the population size and structure are furthermore illustrated as population pyramids (Figs.
177 to 180).

The shaded area represents the enumerated 2009 population size by sex and age group, and the outlined area represents the estimated (projected) population size in 2050, according to the high (Fig.178), medium (Fig.179), and low (Fig.180) population scenarios.

In addition, Figure 177 illustrates the impact of constant high level fertility.

It shows what the population would look like if the current level of 4 children per woman remains at this level for the entire projection period 2009–2050.
As is shown in Figure 172, the population would then be 659 thousand people.

The different shaped pyramids of the four different projection scenarios (constant, high, medium, and low) clearly illustrate that the difference in population size and structure in 2050 is the size of the population aged 0–34.
It highlights the effect of the assumed fertility level on future population size and structure: the lower the assumption of the future fertility level, the smaller the size of the population younger than 35 years of age in the future.
173 Figure 177: Population pyramid, constant fertility projection, Vanuatu: 2010 and 2050 2010 (shaded area) & 2050 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 50,000 40,000 30,000 20,000 10,000 0 10,000 20,000 30,000 40,000 50,000 Number of people Figure 178: Population pyramid, high fertility projection, Vanuatu: 2010 and 2050 2010 (shaded area) & 2050 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 50,000 40,000 30,000 20,000 10,000 0 10,000 20,000 30,000 40,000 50,000 Number of people 174 Figure 179: Population pyramid, medium fertility projection, Vanuatu: 2010 and 2050 2010 (shaded area) & 2050 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 50,000 40,000 30,000 20,000 10,000 0 10,000 20,000 30,000 40,000 50,000 Number of people Figure 180: Population pyramid, low fertility projection, Vanuatu: 2010 and 2050 2010 (shaded area) & 2050 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 50,000 40,000 30,000 20,000 10,000 0 10,000 20,000 30,000 40,000 50,000 Number of people 175 Most likely outcome Predicting the likelihood of a certain future population size and structure is difficult for any country, and the further into the future the prediction, the more uncertain the outcome.
Therefore, several projection variants need to be produced to allow users to choose from an outcome that seems most probable according to their own views and opinions.

Most data users, however, prefer to use a recommended projection scenario that depicts a "most likely outcome".

Such a variant is usually called the “medium” projection scenario using the medium assumptions used.
Population changes close to those presented in the medium population scenario appears to be the most likely outcome because:  The current fertility level is expected to decline as it has in Vanuatu’s recent past, and is furthermore expected to do so based on historical worldwide observations of countries with a similar level of fertility (see also the “theory of demographic transition”, App.24).
Therefore, the high fertility assumption, with its very slow fertility decline, seems to be a more unlikely outcome, and a constant high level of the current TFR of 4.1 is surely an unrealistic scenario.

 Regarding the low fertility assumption, fertility levels (TFR) have already declined to well below 2 in many parts of the world, and it is therefore a realistic assumption to make.
Nevertheless, such rapid fertility decline does not seem likely to occur in Vanuatu as it seems “uncharacteristic” for Pacific Islands populations at the moment.

6.2 Sub-national projections Population projections for each of Vanuatu’s six provinces were prepared according to the same principles as the national projection.

The cohort-component method was applied, and the fertility and mortality indicators as estimated from the 2009 census were used as inputs for each province.
However, there is one significant difference in the preparation of sub-national projections compared to the national projection, and that is the assumptions of (internal) migration.

While no migration assumption was made for the national projections (net international migration is zero), a migration assumption for the provinces had to be included as the demographic pattern of each province is strongly influenced by internal migration.
176 Base population The census age distributions of each province as recorded in the census are used and prorated to be consistent with the adjusted base population for the national projections (App.25 and Tables 52 to 53).

Fertility The trend of fertility of each province throughout the projection period follows the same pattern as that established for the national projection (medium variant).

However, the level of fertility is determined by the estimated level for 2009 (Fig.181).
The higher the level in 2009, the higher the level throughout the projection period, because the target level of a TFR of 2 will be reached later in time.

According to this fertility schedule, Shefa would reach a TFR of 2 already in 2029 (because it starts with a much lower level in 2009), and will stay at this level throughout the remainder of the projection period.
Figure 181: Fertility trend by province, Vanuatu: 2009-2054 6 5 4 R F T 3 2 1 0 2009 2014 2019 2024 2029 2034 2039 2044 2049 2054 Vanuatu Torba Sanma Penama Shefa Tafea Malampa Year Note: The line showing the fertility level and trend of Sanma and Malampa overlap as their TFR in 2009 was with 4.2 the same 177 Mortality The level of future mortality (life expectancy at birth) of each province is determined by using the UN working models of mortality improvement as described earlier (App.23), applied to the estimated life expectancy at birth of each province as described in section 3.2.2.
Migration In order to estimate the migration component of each province, the balancing equation was used.

Balancing equation: Population growth = Births minus Deaths plus Migration Net migration can be estimated as Migration = Population growth minus Births plus Deaths Since the population growth rate and the level of fertility and mortality are known from the census, the migration component can be calculated.
The following levels of fertility, mortality and migration were used as a starting point for each province (Table 56).

Please note that for Sanma zero net migration is assumed, as its current growth rate is about the same as the estimated natural growth rate, and its demographic dynamic is very similar to the national average of Vanuatu.

With respect to migration, two variants of migration have been prepared.

1.
1.

Constant migration: The estimated level in 2009 has been kept constant throughout the projection period 2.

Zero net migration: Zero net migration is assumed for the entire projection period (only natural growth influences population growth).

The zero migration variant has been prepared in order to illustrate the impact of migration on the population size of the different provinces.
With regard to the age and sex structure of migrants, it is assumed that there will be equal numbers of males and females, and the age structure resembles that of a family type migration pattern (App.26).

178 Table 56 summarizes the different population components used for the provincial projections.
Table 56: Demographic components for provincial projections Deomographic component Torba Sanma Penama Malampa Shefa Tafea Vanuatu Annual growth rate (%)* TFR E(0) - 2009 Males Females E(0) - 2054 Males Females Net migration (annual) 1.9 4.5 67.2 71.2 76.3 80.8 -60 2.4 4.2 69.8 72.4 77.5 81.4 0 1.5 4.7 66.2 71.7 75.6 81.2 -260 1.2 4.2 69.2 72.6 77.0 81.3 -400 3.7 3.4 70.4 73.4 77.6 81.7 1,370 1.1 5.2 69.1 72.1 76.9 81.4 -650 2.3 4.1 69.6 72.7 77.3 81.4 0 *not adjusted for under enumeration Projection results Figures 182 to 189 and Table 57 illustrate the impact of migration on the size of the different provinces.
Table 57: Population size by province according to different migration variants, Vanuatu: 2009-2050 Migration variant/ Year Vanuatu* Torba Sanma Penama Malampa Shefa Tafea 2009 239,000 9,545 46,839 31,395 37,395 80,754 33,073 Constant migration 2010 2015 2020 2025 2030 2035 2040 2045 2050 Zero migration *The provincial projections are prepared to match the national medium fertility projection 179 In general, all provinces will increase in size until 2050 regardless of which type of migration assumption was applied, although the rate of increase varies considerably depending on the type of migration assumption used.
The size of Shefa will increase substantially to 218 thousand people until the year 2050 if the current high level of rural-urban migration continues.

In comparison the other provinces remain relatively small in size, although each province will continue to increase.

About 45% of the Vanuatu population would live in Shefa in 2050 compared to one third in 2009.
The estimated demographic components of births, deaths, and migration, and population size and growth by province for the years 2011, 2015, and 2020 are shown in Table 58.

If suddenly there would be no further internal migration, and each province would be closed to migration, Shefa’s population would only be 134 thousand people in 2050, and the other provinces would increase sharply in size.

Only 28% of Vanuatu’s population would live in Shefa.
Most likely outcome Clearly Vanuatu’s provinces are not and will not be closed to migration, and internal migration (rural-urban) will continue.

In view of the currently relative small urban proportion of Vanuatu, it can be expected to grow substantially in future, as has been the case in most other countries in the world.

Therefore the projection results using the constant migration assumption is the more realistic outcome.
180 Table 58: Projected demographic components of births, deaths and migration, and population size and growth by province, Vanuatu: 2011, 2015, and 2020 Indicator Births Deaths Migrants Growth Population Growth rate Indicator Births Deaths Migrants Growth Population Growth rate Indicator Births Deaths Migrants Growth Population Growth rate Torba Sanma Penama Malampa Shefa 2011 1,570 1,008 1,113 234 -260 515 246 -400 467 2,526 370 1,370 3,527 328 61 -60 207 9,959 2.1 229 0 1,341 49,503 2.7 32,430 38,354 87,721 33,816 1.6 1.2 4.0 1.0 Torba Sanma Penama Malampa Shefa 2015 327 60 -60 208 10,792 1.9 1,622 243 0 1,378 54,975 2.5 330 60 -60 210 11,843 1.8 1,636 264 0 1,372 61,865 2.2 1,006 1,053 225 -260 521 240 -400 414 2,705 415 1,370 3,660 34,521 40,120 102,143 34,952 1.5 1.0 3.6 0.7 1,028 1,006 221 -260 547 238 -400 368 2,711 479 1,370 3,602 37,236 42,114 120,235 36,004 1.5 0.9 3.0 0.5 Torba Sanma Penama Malampa Shefa 2020 Tafea 1,172 172 -650 351 Tafea 1,055 170 -650 235 Tafea 1,002 170 -650 182 Vanuatu 7,719 1,311 0 6,407 251,784 2.5 Vanuatu 7,768 1,353 0 6,416 277,503 2.3 Vanuatu 7,713 1,432 0 6,281 309,299 2.0 181 Figure 182: Population size by province according to the constant migration variant, Vanuatu: 2010-2050 e l p o e p f o r e b m u N s d n a s u o h T 250 200 150 100 50 0 2010 2015 2020 2025 2035 2040 2045 2050 2030 Year Torba Sanma Penama Malampa Shefa Tafea Figure 183: Population size by province according to the zero migration variant, Vanuatu: 2010-2050 e l p o e p f o r e b m u N s d n a s u o h T 250 200 150 100 50 0 2010 2015 2020 2025 2035 2040 2045 2050 2030 Year Torba Sanma Penama Malampa Shefa Tafea 182 Figure 184: Population size according to two different migration variants, Torba: 2010-2050 21 20 18 17 19 17 16 14 14 16 15 13 13 11 12 10 11 10 2010 2015 2020 2025 2030 Year 2035 2040 2045 2050 Constant migration Zero migration Figure 185: Population size, Sanma: 2010-2050 99 93 88 82 75 69 62 55 48 25 20 15 10 5 0 s
o r e b m u N s d n a s u o h T 25 20 15 10 5 0 s d n a s u o h T 120 100 80 60 40 20 0 2010 2015 2020 2025 2030 Year 2035 2040 2045 2050 Note: for Sanma only one projection variant has been prepared with zero net migration Declining migration 183 Figure 186: Population size according to two different migration variants, Penama: 2010-2050 e l p o e p f o r e b m u N e l p o e p f o r e b m u N 70 65 55 52 60 56 49 46 50 45 41 36 37 43 40 32 35 32 s d n a s u o h T 80 70 60 50 40 30 20 10 0 2010 2015 2020 2025 2030 Year 2035 2040 2045 2050 Constant migration Zero migration Figure 187: Population size according to two different migration variants, Malampa: 2010-2050 s d n a s u o h T Year 2035 2040 2045 2050 Constant migration Zero migration 184 Figure 188: Population size according to two different migration variants, Shefa: 2010-2050 e l p o e p f o r e b m u N s d n a s u o h T 250 200 150 170 154 138 120 100 84 83 102 102 93 109 115 50 0 218 202 186 120 125 130 134 2010 2015 2020 2025 2030 Year 2035 2040 2045 2050 Constant migration Zero migration Figure 189: Population size according to two different migration variants, Tafea: 2010-2050 s d n a s u o h T e l p o e p f o r e b m u N Year 2035 2040 2045 2050 Constant migration Zero migration 185 7.
IMPLICATIONS OF DEMOGRAPHIC TRENDS 7.1 Population dynamics 7.1.1 Growth rate Vanuatu’s annual population growth rate of 2.3% is still one of the highest in the Pacific region.

Nevertheless the population density (19 people per sq.

km) is one of the lowest in the region.

At a fixed growth rate, the population of Vanuatu would double in 31 years.

In addition, Tafea (at 1.1%) and Malampa (at 1.2%) will have their populations doubling in 60 years.
Shefa (at 3.7%) would have the lowest doubling time of just 19 years caused by its growing urban area of Port Vila.

7.1.2 Fertility Annual population growth is the result of a relatively high natural growth rate which is caused by high fertility (birth) rates.

The average number of children per woman (TFR) has only dropped from about 4.6 to 4.1 children per woman during the 10-year period 1999–2009, and is still one of the highest in the Pacific region.
Should the government wish to influence fertility levels, policies and programmes directed toward the expansion of family-planning services and reproductive health programmes should be considered.

In the rural areas, access to services is of importance.

Life education, as part of the curriculum, is one means of ensuring that young people are provided with the information and support they may need.

Access to education should extend beyond school age and be part of a life long process.
The availability and accessibility of family planning services for women (and their partners) of all ages will empower them to make conscious decisions about the number and spacing of their births.

Furthermore, pregnancies of young women are often unwanted and the result of unprotected sex.

This is a major health concern, especially considering the risk of HIV/AIDS and sexually transmitted diseases.

The promotion of arranged marriages at an early age, through custom and culture, can affect fertility.
Teen pregnancy is a social issue; children of teenage mothers often have lower educational levels, higher rates of poverty, and other poorer “life outcomes”.

In general, teenage pregnancy usually occurs outside of marriage and, for this reason it often carries a social stigma.

Social protection for solo parents and young mothers can include the provision of child support and maintenance.
Many stakeholders are involved in teenage reproductive health strategies, working at various levels to reduce teenage pregnancy by increasing the knowledge and practice of family planning, promoting peer education, providing sex education advisory services including contraceptives, 186 involving young people in service design, educating the parents of teenagers on effective communication, providing better support for teenage mothers (such as help returning to education, advice and support), working with young fathers, giving better childcare, and increasing the availability of supported housing.
7.1.3 Mortality Improved mortality rates mean that healthier people live longer lives.

Based on census data for the number of children ever born and still alive, the infant mortality rate (IMR) was estimated at 21; 22 for males and 19 for females.

This estimate is lower than the 1999 levels when the IMR was 27 and 26 for males and females – and is thus an improvement in infant mortality rates.
Estimates of mortality level presented in this report suggest that females live longer than males, and live on average about three years longer than males.

Life expectancy at birth is estimated at 69.6 and 72.7 for males and females, respectively.

This represents an increase compared to 1999 when it was 65.6 and 69.0 years for males and females.

The figures in Vanuatu compare with levels of 78.8 and 82.7 years for males and females in New Zealand.
Life expectancy at birth in France is 78.1 and 84.8 years for males and females, and in Australia it is 79.3 and 83.9 years.

Therefore an average person in New Zealand, France or Australia lives about 10 years longer than a Ni-Vanuatu.

On the other hand, life expectancies at birth for Fiji are estimated at 63.8 and 67.7 years for males and females in 2001, which is considerably shorter than in Vanuatu.
7.1.4 Internal migration Shefa province had the highest growth rate of all provinces – a clear sign of internal migration flows towards Shefa and the capital Port Vila.

Some rural areas and islands show a very low population increase despite high natural growth or sometimes even negative population growth rates (i.e.

a population decline) during the intercensal period.

These developments point to a possible dissatisfaction with living conditions in these areas.
Reasons may tertiary or vocational/technical qualifications), and limited employment opportunities.

The urban centers attract people by offering higher living standards through the availability and accessibility to services such as medical and educational institutions, entertainment facilities, and a wider range of employment opportunities.
lack of education opportunities include (for the If the government wishes to change this trend, at least some of the disadvantages of living in the remote rural areas and outer islands need to be eased by improving the above-mentioned services and opportunities through: 187  Promotion of policies for employment and livelihood in rural areas;  Decentralization of services to all provinces;  Support of income generating opportunities in other provinces to retain populations, in particular the youth;  Provision of better education in the rural areas;  Promotion of better market distribution systems;  Provision of better and cheaper transport;  Conducting of in depth research into youth migration and their reasons for migrating;  Provision of basic services for the growing population in the urban/peri-urban areas.
7.1.5 International migration Data on arrivals and departures remain incomplete for detailed migration analysis.

The net migration level can only be crudely estimated by comparing intercensal population growth with estimated rates of natural increase for the same time period.
While this method provides a reasonably robust indication of net migration, planners and policy-makers require more detailed and timelier information on the demographic makeup of opposing migration streams in order to make and implement realistic policy decisions.

Hence, further improvements are needed to collect and process information on age, sex and nationality of all arriving and departing passengers in Vanuatu.
If improvements prove to be impossible, an alternative would be to apply the proper demographic methodologies, by comparing the two nearest censuses, to calculate the desired population data.

The disadvantage of this option is that this can only be done after the analysis of the latest census is completed.

This exercise could prove more time consuming and costly than an efficient registration system that would provide regular and timely migration information.
As the national average annual population growth rates are similar to the estimated natural growth, it can be concluded that net migration rates are negligible, and no significant international migration had occurred during the intercensal period 1999-2009.

7.1.6 Population projections Knowledge about the current size and structure of a country’s population is needed for the formulation and implementation of policies and programmes in almost all areas of public life.
Because policies are aimed at achieving goals in the future, knowledge about future population trends is required.

The population projection scenarios presented in this report point to a continuously growing population for Vanuatu during the next 40 years.
The medium-variant scenario of the projections points to a population of about 278 thousand in 2015, 370 thousand in 2030, and 483 thousand people in 2050.. Changes in Vanuatu's population age structure, as a result of possible declining fertility rates, will have an impact on the proportion of the young population aged 0–14.

Changes will be reflected in 188 a smaller proportion of those under the age of 15, and a larger working age population aged 15– 59.
As a result, the dependency ratio of Vanuatu’s population will decrease, and the population’s median age will increase.

The proportion of the population aged 60 and older will increase from 6% in 20069 to 8–9% of the total population in 2030.

The working age population is expected to increase considerably, both in proportion and in absolute numbers.

According to the medium population scenario, the working age population will be about 227 thousand people in 2030, compared to 129 thousand in 2009.
The needs of this larger population size and its different population subgroups should be considered in development plans in areas as diverse as health, education, environment, and economic growth.

7.2 Crosscutting issues Vanuatu will most likely experience a continued population growth during the next few years.

Appropriate health, education, and social welfare programmes must be in place to fulfill the needs and aspirations of Vanuatu’s communities.
7.2.1 Vital statistics A well functioning registration system, able to supply accurate and timely statistics on population developments, is of fundamental importance to planners and policy-makers.
To make reliable estimates regarding fertility and mortality indicator levels and trends, a complete registration system needs to be in place; one that records the number of deaths by age and sex, and cause of death, and the number of births by sex and by age of mother, and place of mother’s usual place of residence.

Improved coordination between all agencies involved is required.
By tracking all immigrants and exiting people, policy-makers will have an accurate and current picture of Vanuatu’s total population size and structure.

Such information will be indispensable for policy planning purposes and policy formulation.

7.2.2 The environment Careful use of terrestrial and marine resources forms the basis of a sustainable and healthy life for Vanuatu's people.
As such, maintaining a healthy and sustainable living environment should be a top priority for the government and people of Vanuatu.

Apart from enabling a good quality of life for local people, conservation of the environment can foster a vibrant tourism industry.

The size and density of the population has a direct impact on water and energy consumption, sewage and waste production, general infrastructure such as roads, the use of land, and the development of agriculture and marine resources.
189 High population densities put considerable stress on the environment.

Consequently, there is a higher demand on environmental health services, such as public garbage collection, and most importantly, a well-functioning sewage system.

In addition, water sources need to be protected.


7.2.3 Households Population growth not only contributes to an increased demand in water and energy supply, waste disposal, sewage connections and general infrastructure, but also to an increase in the number of households due to changes in average household size.
Even if the population size remained stable, the number of households would still increase when households and/or family structures break up into smaller units, often described as the transition from extended family type households to nuclear family type living arrangements.

Households and families that are economically incapable of sustaining an acceptable and healthy lifestyle might need extra assistance from the government, since unhealthy living environments affect everyone in the long term.
In particular, access to clean water, public electricity, an adequate public sewage system and waste disposal facilities should all be the minimum housing standard for Vanuatu’s population.

Specific areas of assistance include:  Dwellings: 25% of dwellings in rural areas are more than 10 yrs old and are prone to natural disaster.

As such, government needs to improve housing in rural areas using local materials which are affordable and cyclone proof.
 Water supply: 33% of total households in Tafea use the river, lake, or spring as sources of water.

The development of more community programmes focusing on water supply, and providing water tanks, or water pumps is required.

 Lighting: 48% of households in Vanuatu use kerosene lamps as the main source of light and mainly in the rural areas.

With continued rising prices, kerosene is no longer an affordable source for the home, community, school, or business.
Alternatives are needed and ‘green power’ sources such as solar (currently used by 4% of households), wind, or renewable energy should be researched.

In this respect, government could encourage students to enter into engineering or environmental studies.

 Toilet facilities: 88% of the total households in Penama use the pit-latrine which is not a hygienic toilet facility.

Health awareness programmes and assistance in the introduction and improvement of toilet facilities are needed.
7.2.4 Health services and well-being The health status of each individual and his/her family members is probably one of the most important concerns people have.

Therefore, the availability, use and affordability of quality health 190 care and medical services are major issues of concern.

Government and health officials need to address the challenges of health services and the health care system.
In Vanuatu’s remote areas and outer islands, small population size and isolation inhibit the operation of state-of-the-art health services that require the employment of specialist personnel and the purchase and maintenance of specialised equipment.

However resident medical staff needs to be sufficiently qualified to provide basic health care.
An efficient referral service to the nearest health facility, together with regular visits by medical specialists, is needed to ensure that peoples’ health demands are met.

The population projections have shown that the population aged 60 and older will increase in future.

This requires strengthening of special services for the growing number of elderly people, including a pension scheme with retirement benefits, and specialised health care for the elderly.
In working towards a healthier population, the following efforts should be made:  Improve infant, child and maternal health by improving primary health care programmes;  Improve emergency obstetric care to decrease neo natal mortality  Expand immunization programmes;  Prevent HIV and AIDS, and other STIs by: - Increasing awareness and knowledge of safer sexual behaviours and practices by using appropriate language; - Targeting priority groups (youth, women and men, particularly aged 10–24); - Enhancing education programmes to encourage open discussions (between partners and their children) on issues of sexual behaviours; - Promoting and disseminating information outlining the advantages and proper use of condoms by men and women, with an emphasis on targeting male organisations; - Reviewing, developing, implementing and evaluating the effectiveness of appropriate policies; - Delaying young peoples' initial sexual activity; - Developing a well-planned media campaign throughout the year based on health promotion with regards to HIV and AIDS; - Ensuring protection of the rights of people living with HIV and AIDS; - Ensuring that people living with HIV and AIDS have free and unrestricted access to medical treatment, facilities and support services; - Ensuring that a reliable HIV and AIDS testing system is in place; - Establishing a voluntary, confidential system of HIV and AIDS testing with informed consent that includes pre and post test counseling;  Address the increasing occurrence of Non Communicable Diseases (NCDs);  Combat the prevalence of diabetes and heart disease;  Promote healthy eating habits and food nutrition programmes;  Advocate a general healthy life style including regular physical exercise; and  Discourage smoking and excessive alcohol consumption;  Provide a hygienic and safe living environment;  Improve the quality of drinking water; 191  Distribute bednets as a way of combating malaria.
7.2.5 Disabilities Vanuatu is a signatory to a United Nations convention to uphold the rights of people with disabilities; and is therefore obliged to: “Promote, protect and ensure the full and equal enjoyment of all human rights and fundamental freedoms by all persons with disabilities and to promote respect for their inherent dignity.” 7.2.6 Education Educational level is a key indicator of development and quality of life in a country.
Education plays an important role in development through its links with demographic, as well as economic and social factors.

In general, there is a close and complex relationship between education, fertility, morbidity, mortality and mobility: when couples are better educated, they tend to have fewer children, their children’s health status improves, and their survival rates tend to increase.
Higher levels of educational attainment also contribute to a better qualified workforce, higher wages, and better economic performance than for people who have little or no formal education and training.

The Ministry of Education has made considerable progress towards achieving universal primary education.
As a result of broad consultation processes and the SWA (Sector wide approach) used in developing the Vanuatu Education Sector Strategy 2007-2016, the Ministry of Education realigned its priorities towards universal primary education and literacy.
Constraints for universal primary education identified during this process were the costs of maintaining the dual education system with separate streams for ‘English’ and ‘French’ as the language of instruction and the need to achieve an integrated system of bilingual schools.

A concerted effort with development partners to provide ‘fee free’ primary level education up to Year 6 in Government and Government-assisted schools began in some areas in 2009 and achieved full coverage in 2010.
Compulsory primary school contributions have been phased out and replaced by grants paid directly to the schools.

This was in direct response to declining primary enrolment rates which were around 95% in 2005 but decreased to 80% in 2008; and subsequent research highlighted rising parental contributions (school fees) as one of the main reasons why enrolment rates were falling.
Other options for government include; considering increasing school budgets (for materials and teachers) to reduce year eight drop-outs.

In addition, the building of vocational centres featuring youth development programmes could provide life skills (including family planning).

The programmes may reduce teenage delinquency, and teenage pregnancy, while providing youth the skills they need to be part of the work force and community.
192 School attendance, educational attainment, and literacy rates are much lower in the rural than in the urban areas.

The provinces of Tafea and Torba require specific attention.

7.2.7 Economic activity and labour market Economic activity and employment are shaped by the size of the working age population, the educational skill level of the labour force, and the economic resources available to a country.
Although a high proportion (71%) of Vanuatu’s population aged 15 and older was economically active (in the labour force), only a relatively small proportion (30%) was engaged in paid employment.

These relatively few people (42 thousand) supported the rest of the population with respect to paid income, meaning that one paid person supports, on average, about 4.5 other people.

Vanuatu enjoys the benefits of migrant labour with Government agreements with New Zealand and Australia.
The most popular country was New Zealand and its Recognized Seasonal Employer (RSE) programme began in 2007 with a pilot and larger numbers followed thereafter.

The Australian pilot was in 2009.

The workers are mostly unskilled and are mainly involved in agricultural work.

During the year before the census, almost 8 thousand N-Vanuatu workers and their families benefitted from these agreements, and will hopefully continue to benefit in future.
According to projection results presented in this report, the working age population will increase significantly during the next years.

Government and business officials are encouraged to collaborate in developing innovative strategies that will promote economic diversification and growth.

193 7.2.8 Communication and internet use The access and use of telecommunications has increased a lot since liberalization in the mid 2008.
Existing research in telecommunications suggests that access can increase economic growth, attract foreign investment, improve market efficiencies, increase accessibility to health and education and empower women and others.

The telecommunication sector is presumed to provide new opportunities and frontiers across businesses, social, economic and political arena.
An improvement in the infrastructure and facilities of telecommunications will have a direct effect on the well being of individuals in the country.

i) Examples where assistance is needed include:  Mobile phone access: 90% of Torba household do not have mobile phone; this may be due to a coverage issue.

Government should negotiate with telecommunication companies to discuss improvements in phone coverage to Torba and Tafea.
 Radio availability: 82% and 80% of households in Tafea and Torba do not have radio; this may be mainly due to limited radio coverage.

One way to improve coverage to remote areas is through the establishment of provincial radio stations devoted mainly to culture, sport, education, and health awareness programmes.

 Better coverage for radio and phone services in the rural areas is vital in improving communication in the areas of health (family planning), and education.
ii) Examples where assistance can be provided include:  The use of the internet to provide online medical advice;  Registration of births using mobile phones;  Dissemination of computers to school children as part of an education programme, as per the SPC one laptop per child programme.
7.2.9 Good governance Good governance and effective policy-making should provide the framework for sustainable development within which the interrelationship of population, environment, and all possible socioeconomic aspects of a country can prosper cohesively.
In this regard it is important that policy-makers, planners, politicians and community leaders are aware of the needs and aspirations of their country's people in order to effectively provide for the specific needs of the population, and the different population sub-groups.
Then government needs to know about its country’s population structure, population processes and socioeconomic characteristics in order to plan for an adequate standard of living, and for a proper provision and distribution of goods and services.
194 GLOSSARY Indicator Definition Age–dependency ratio Number of people in the “dependent” age category (population younger than 15 years plus population 60 years and older) per 100 in the “economically productive ages” 15–59 years Average age at (first) marriage (SMAM) Approximation of average age at marriage, based on proportion of population never married (single) Balance equation Population growth = births – deaths + net migration Births — estimated number for 2009 Estimated age-specific fertility rates (ASFR) multiplied by enumerated number of women by age in 2009 Child-woman ratio (CWR) Number of children under age 5 per 1,000 women aged 15- 49 Child mortality rate (1q5) The probability of dying between age 1 and age 5 Crude birth rate (CBR) Estimated number of births per 1,000 population (7,335/234,023 X 1,000) Crude death rate (CDR) Estimated number of deaths per 1,000 population (1,260/234,023 X 1,000) Crude net migration rate Rate of growth minus rate of natural increase Deaths — estimated number for 2009 Estimated age-specific death rates [m(x)] by sex (from life multiplied by enumerated population by age and sex in 2009 Employment–population ratio General fertility rate Gini coefficient Proportion of employed people in cash work (by a given age and sex), as part of the corresponding total number of people of the same age and sex Annual number of births per 1,000 women of childbearing age (15-49) The Gini coefficient is a measure of the inequality of a distribution, a value of 0 expressing total equality and a value of 1 maximal inequality.
Infant mortality rate (IMR) Number of infant deaths (children younger than 1 year) per 1,000 births 195 Institutions Boarding schools, prisons, hospitals, hotels/hostels/guesthouses, and mobile households (passenger boats) Intercensal period Time period between two censuses Labour force People employed (cash work plus village work) and unemployed (excludes those not seeking employment) Labour force participation rate Proportion of people in the labour force (by a given age and sex), as part of the corresponding total number of people of the same age and sex Language ability see Literacy rate Life expectancy at birth Number of years a newborn baby can expect to live on average Life expectancy at age 20 Number of additional years a 20 year old can expect to live on average Literacy rate Proportion of the population age 15 years and older or 15- 24 years, who are able to read and write a simple sentence in any language Mean age at childbearing Average age of women when giving birth Median age The age at which exactly half the population is older and half is younger Parity (average) Average number of children per woman Rate of growth (%) Average annual growth rate during 1999–2009 ln(TotPop2009/TotPop1999)/10 X 100 Rate of natural increase Crude birth rate (CBR) minus crude death rate (CDR) Sex ratio Number of males per 100 females Teenage fertility rate Number of births by women aged 15–19 per 1,000 Total fertility rate (TFR) Average number of children per woman Under 5 mortality (q5) The probability of dying between birth and age 5 Urban population Total population of the towns Luganville in province Sanma and Port Vila in province Shefa 196 APPENDICES 197 Appendix 1: Accuracy of age reporting - indices of age heaping The 2009 Vanuatu census population shows the following distinct age patterns (Fig.A1): 1.
Age heaping at ages ending with ‘0, ‘7’ and ‘9’, and avoidance of ages ending in ‘8’; 2.

Relatively high number of people aged 75 years and older, and of these more males than females.

Figure A1: Population pyramid, Vanuatu: 2009 Males Females e g A 90+ Regarding the above: Number of people 1.
The occurrence of age heaping is expressed by the calculated Whipple, Myers, Bachi, and the United Nations age-sex accuracy indexes (Table A1).

Table A1: Age accuracy indices, Vanuatu: 1999 and 2009 Census year 1999 2009 Myers' Bachi Males Females Males Females Males Females Whipples 5.6 5.2 With respect to the interpretation of these indices: 10.1 7.8 8.2 7.2 6.5 5.5 117 112 117 110 UN Secretariat 24.3 26.4 198 A. Myers – the higher the index, the greater the concentration on the age examined.
Positive values show a preference for the digit, and negative values avoidance of the digit (Figs.2a/b).

The index calculated for males is 7.8 and for females 7.2.

As a comparison, the index for the 1999 Vanuatu census population was 10.1 and 8.2 for males and females respectively.

The theoretical range of Myer’s index is 0, representing no heaping, to 90, which would result if all ages were reported at a single digit.
Figure 2b shows high indexes for ages ending with a ‘0’ and ‘9’, and to a lesser degree ‘7’ and ‘5’; these indexes are over represented.

Indexes with a negative value such as ‘8’, and ‘1’, ‘2’, and ‘3’ were avoided and are underrepresented.

B. Bachi – the higher the index, the greater the concentration on the age examined.

Positive values show a preference for the digit, and negative values avoidance of the digit.
The index calculated for Males is 5.5; Females 5.2, which compares to 6.5 and 5.6 for males and females for the 1999 census (Figs.3a/b).

The Bachi index as indicator of the general extend of heaping differs little from Myers’.

The theoretical range of Bach’s index is also 0, representing no heaping, to 90, which would result if all ages were reported at a single digit, say zero.
Figure 3b shows high indexes for ages ending with a ‘7’, ‘0’ and ‘9’, and to a lesser degree ‘5’; these indexes are over represented.

Indexes with a negative value such as ‘8’, and ‘1’, ‘2’, and ‘3’ were avoided and are underrepresented.

C. Whipple: Males and Females was 112 and 110 respectively.

This measure means that the Vanuatu population overstated ages ending in 0 or 5 by 12% and 10% for males and females.
As a comparison, the index for 1999 Vanuatu census population was 117 for both males and females.

The decrease of the different indices is an indication that age reporting in the 2009 census has improved compared to the 1999 census.

In general it is not possible to measure digit preference precisely, because an accurate distinction between the error due to digit preference, other errors, and real fluctuations cannot be made.
Therefore none of the above indexes provides a critical value of age heaping/misreporting because of each country-specific effect of past trends of births, deaths and migration on a population’s age distribution.

The genuine fluctuations become the more pronounced the smaller the population (sample) size.

Nonetheless, the fluctuations observed suggest some faulty reporting.

199 Finally, the United Nations age-sex accuracy index for the Vanuatu 1999 and 2009 census populations is calculated.
It shows a (corrected) index of 24.3 and 26.4 for the 1999 and 2009 censuses respectively.

Census age-sex data are described by the United Nations as “accurate”, “inaccurate”, or “highly inaccurate” depending on whether the UN index is under 20, 20-40, or over 40.

However, this procedure as a measurement of age- sex accuracy is labeled as questionable13 due to its problematic underlying assumptions made.
Figure 2a: Myers Preference by digit, Vanuatu: 1999 Figure 2b: Myers Preference by digit, Vanuatu: 2009 Male Female x e d n I 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 Male Female x e d n I 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 0 0 Age Age Figure 3a: Bachi Preference by digit, Vanuatu: 1999 Figure 3b: Bachi Preference by digit, Vanuatu: 2009 Male Female 3.0 2.5 2.0 1.5 1.0 x e d n I 0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 Male Female 3.0 2.5 2.0 1.5 1.0 0.5 0.0 x e d n I -0.5 -1.0 -1.5 -2.0 -2.5 0 0 Age Age Source: US Census Bureau, Population Analysis Spreadsheets (PAS), procedure SINGAGE 13 Methods and Materials of Demography, Second Edition, Jacob S. Siegel/David A. Swanson, p.150 200 2.
With respect to the second observation made, the occurrence of higher number of males than females at older ages has to be regarded as suspect, because life expectancy of females is estimated to be higher than males (more females survive to older ages than males).

Furthermore, census data show that the proportion of widowed females at older ages is significantly higher than males.

The number of widows increases (much) faster than widowers at older ages.
This is explained by the higher death rates among men.

Thus there are more widows than widowers among the old.

Furthermore the question on whether a respondent’s father and/or mother were still alive revealed that the proportion of surviving mothers was significantly higher than that of fathers.

These findings contradict the access count of older males versus females, and points to: a. Under-enumeration of (older) females, and/or b.
(Sex-selective) age misreporting - old people (particularly males) reported to be even older than they really were.

201 Appendix 2: Arraiga's approach for estimation of ASFR for one point (1999) in time and the age pattern of fertility (Brass) Month Year November 1999 Fertility pattern is tabulated by age of woman at enumeration Age Group of Women Children Ever Born Age Specific Fertility Pattern Fertility Consistent with C.E.B.

(A.S.F.R.)
(A.S.F.R.)

Fertility Pattern by Age at Survey Date Fertility Pattern by Age at Birth of Child Cumulation of A.S.F.R.
Fertility Pattern by Age at Birth Adjustment Factors Age Specific Fertility Rates Based on Adjustment Factor for the Age Group 25 - 30 20 - 30 20 - 25 Nov-99 15 - 20 20 - 25 25 - 30 30 - 35 35 - 40 40 - 45 45 - 50 0.129 0.997 2.100 3.256 4.058 4.573 4.782 0.042 0.194 0.204 0.158 0.109 0.054 0.036 Mean Age of Childbearing: Total Fertility Rate: Recorded 0.042 0.194 0.204 0.158 0.109 0.054 0.036 Calculated Appendix 3: Arraiga's approach for estimation of ASFR for one point (2009) in time and the age pattern of fertility (Brass) Month Year November 2009 Fertility pattern is tabulated by age of woman at enumeration Age Group of Women Children Ever Born Age Specific Fertility Pattern Fertility Consistent with C.E.B.
(A.S.F.R.)

Fertility Pattern by Age at Survey Date Fertility Pattern by Age at Birth of Child Cumulation of A.S.F.R.
Fertility Pattern by Age at Birth Adjustment Factors Age Specific Fertility Rates Based on Adjustment Factor for the Age Group 25 - 30 20 - 30 20 - 25 Nov-09 15 - 20 20 - 25 25 - 30 30 - 35 35 - 40 40 - 45 45 - 50 0.096 0.887 1.898 2.927 3.635 4.109 4.418 0.044 0.180 0.190 0.146 0.098 0.045 0.015 Mean Age of Childbearing: Total Fertility Rate: Recorded 0.044 0.180 0.190 0.146 0.098 0.045 0.015 Calculated Appendix 4: Arraiga's approach for estimation of ASFR for two points (19999 and 2009) in time and the age patterns of fertility (Arriaga- Brass) First Enumeration Month Year November 1999 Fertility pattern is tabulated by age of woman at enumeration Age Group of Women Age Specific Fertility Pattern November 1999 to November 2000 Children Ever Born Fertility Consistent with C.E.B.
(A.S.F.R.)

Fertility Pattern by Age at Survey Date Fertility Pattern by Age at Birth of Child Cumulation of A.S.F.R.
Fertility Pattern by Age at Birth Adjustment Factors Age Specific Fertility Rates Based on Adjustment Factor for the Age Group 25 - 30 20 - 30 20 - 25 Recorded 0.042 0.194 0.204 0.158 0.109 0.054 0.036 Calculated - 20 20 - 25 25 - 30 30 - 35 35 - 40 40 - 45 45 - 50 0.129 0.997 2.100 3.256 4.058 4.573 4.782 0.042 0.194 0.204 0.158 0.109 0.054 0.036 Mean Age of Childbearing: Total Fertility Rate: Second Enumeration Month Year November 2009 Fertility pattern is tabulated by age of woman at enumeration Age Group of Women Age Specific Fertility Pattern November 2008 to November 2009 Children Ever Born Fertility Consistent with C.E.B.
(A.S.F.R.)

Fertility Pattern by Age at Survey Date Fertility Pattern by Age at Birth of Child Cumulation of A.S.F.R.
Fertility Pattern by Age at Birth Adjustment Factors Age Specific Fertility Rates Based on Adjustment Factor for the Age Group 25 - 30 20 - 30 20 - 25 15 - 20 20 - 25 25 - 30 30 - 35 35 - 40 40 - 45 45 - 50 0.096 0.887 1.898 2.927 3.635 4.109 4.418 0.044 0.180 0.190 0.146 0.098 0.045 0.015 Mean Age of Childbearing: Total Fertility Rate: Recorded 0.044 0.180 0.190 0.146 0.098 0.045 0.015 Calculated Appendix 5: Fertility estimates using the Trussell P/F Ratio Technique, PAS procedure PFRATIO, Vanuatu: 1999 Reported Average Cumulative fertility Phi(i) 0.209 1.177 2.199 2.991 3.539 3.811 3.993 CEB P(i) 0.129 0.997 2.100 3.256 4.058 4.573 4.782 ASFR f(i) 0.042 0.194 0.204 0.158 0.109 0.054 0.036 0.000 3.993 Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Age code TFR F(i) 0.079 0.762 1.803 2.694 3.337 3.658 3.950 P/F ratio 1.627 1.307 1.164 1.208 1.216 1.250 1.211 * Age code: ASFR based on age of mother at: 0 census/survey 1 birth of child Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 ASFR * 0.054 0.203 0.202 0.153 0.105 0.050 0.032 P2/F2 1.307 0.070 0.266 0.264 0.200 0.137 0.065 0.042 Adjusted ASFR's P3/F3 1.164 0.062 0.237 0.235 0.178 0.122 0.058 0.037 P4/F4 Avg(P3/F3,P4/F4) 1.186 1.208 0.064 0.065 0.241 0.246 0.239 0.244 0.182 0.185 0.124 0.127 0.059 0.060 0.038 0.039 3.99 5.22 TFR * Pattern corrected for one-half year between birth and reporting.
ASFR Age-specific fertility rate.

CEB Average number of children ever born.
4.65 4.82 4.74 204 Appendix 6: Fertility estimates using the Trussell P/F Ratio Technique, PAS procedure PFRATIO, Vanuatu: 2009 Reported Average Cumulative fertility Phi(i) 0.218 1.118 2.067 2.797 3.285 3.510 3.586 CEB P(i) 0.096 0.887 1.898 2.927 3.635 4.109 4.418 ASFR f(i) 0.044 0.180 0.190 0.146 0.098 0.045 0.015 0.000 3.586 Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Age code TFR F(i) 0.085 0.730 1.698 2.525 3.108 3.421 3.568 P/F ratio 1.136 1.214 1.118 1.159 1.169 1.201 1.238 * Age code: ASFR based on age of mother at: 0 census/survey 1 birth of child Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 ASFR * 0.055 0.189 0.187 0.141 0.093 0.041 0.012 P2/F2 1.214 0.067 0.229 0.227 0.171 0.113 0.050 0.014 Adjusted ASFR's P3/F3 1.118 0.062 0.211 0.209 0.158 0.104 0.046 0.013 P4/F4 Avg(P3/F3,P4/F4) 1.139 1.159 0.063 0.064 0.215 0.219 0.213 0.217 0.161 0.163 0.106 0.108 0.047 0.048 0.014 0.014 3.59 4.35 TFR * Pattern corrected for one-half year between birth and reporting.
ASFR Age-specific fertility rate.

CEB Average number of children ever born.
4.01 4.16 4.08 205 Appendix 7: Fertility estimates using the Relational Gompertz method, Vanuatu: 1999 and 2009 Summary Estimates of the Total Fertility Rate - 1999 Age Based on ASFR and CEB Based on CEB only 2+2 points 3+3 points 2+2 points 3+3 points 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Average Average (20-39) 2+2 points based on the age groups 15-19 to 35- 3+3 points based on the age groups 15-19 to 45-49 CEB Children ever born.
ASFR Age-specific fertility rate.

Summary Estimates of the Total Fertility Rate - 2009 Age Based on CEB only Based on ASFR and CEB 2+2 points 3+3 points 2+2 points 3+3 points 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Average (15-49) Average (20-39) 2+2 points based on the age groups 15-19 to 35-39 3.833 5.189 4.830 4.715 4.449 4.320 4.436 4.539 4.796 3.706 4.369 4.130 4.213 4.174 4.229 4.425 4.178 4.221 3+3 points based on the age groups 15-19 to 45-49 CEB Children ever born.
ASFR Age-specific fertility rate.
4.734 4.302 3.905 4.014 4.057 4.191 4.422 4.232 4.069 3.684 4.755 4.474 4.463 4.311 4.273 4.430 4.342 4.501 206 Appendix 8: Abridged life table - Males, Torba: 2009 q(x,n) 0.0150 0.0020 0.0053 0.0049 0.0113 0.0116 0.0120 0.0135 0.0170 0.0235 0.0347 0.0523 0.0795 0.1199 0.1788 0.2665 0.3885 ... l(x) 100,000 98,500 98,303 97,781 97,303 96,205 95,087 93,945 92,674 91,098 88,959 85,868 81,379 74,906 65,928 54,138 39,711 24,283 d(x,n) 1,500 197 522 478 1,098 1,118 1,141 1,272 1,575 2,140 3,091 4,489 6,473 8,979 11,790 14,427 15,428 24,283 L(x,n) 98,628 393,529 490,209 487,708 483,962 478,238 472,611 466,633 459,600 450,443 437,545 418,814 391,656 353,221 301,363 235,496 159,785 136,653 S(x,n) 0.9843 0.9960 0.9949 0.9923 0.9882 0.9882 0.9874 0.9849 0.9801 0.9714 0.9572 0.9352 0.9019 0.8532 0.7814 0.6785 0.4610 ...
Appendix 9: Abridged life table - Females, Torba: 2009 q(x,n) 0.0190 0.0030 0.0024 0.0021 0.0042 0.0056 0.0055 0.0062 0.0081 0.0125 0.0211 0.0349 0.0584 0.0926 0.1460 0.2276 0.3441 ... l(x) 100,000 98,100 97,806 97,570 97,367 96,957 96,417 95,885 95,289 94,514 93,337 91,372 88,182 83,028 75,336 64,338 49,697 32,596 d(x,n) 1,900 294 236 204 410 540 531 596 775 1,177 1,966 3,190 5,154 7,692 10,998 14,641 17,101 32,596 L(x,n) 98,285 391,693 488,440 487,342 485,891 483,462 480,765 477,983 474,619 469,857 462,181 449,526 428,973 397,137 350,690 286,493 206,241 199,856 S(x,n) 0.9800 0.9969 0.9978 0.9970 0.9950 0.9944 0.9942 0.9930 0.9900 0.9837 0.9726 0.9543 0.9258 0.8830 0.8169 0.7199 0.4921 ... T(x) 6,716,094 6,617,466 6,223,937 5,733,728 5,246,020 4,762,058 4,283,821 3,811,210 3,344,577 2,884,977 2,434,534 1,996,988 1,578,175 1,186,519 833,298 531,934 296,438 136,653 T(x) 7,119,434 7,021,149 6,629,456 6,141,016 5,653,674 5,167,783 4,684,321 4,203,556 3,725,572 3,250,953 2,781,096 2,318,915 1,869,389 1,440,416 1,043,279 692,589 406,096 199,856 Age 0 1 70 75 80 Age 0 1 70 75 80 m(x,n) 0.0152 0.0005 0.0011 0.0010 0.0023 0.0023 0.0024 0.0027 0.0034 0.0047 0.0071 0.0107 0.0165 0.0254 0.0391 0.0613 0.0966 0.1777 m(x,n) 0.0193 0.0008 0.0005 0.0004 0.0008 0.0011 0.0011 0.0012 0.0016 0.0025 0.0043 0.0071 0.0120 0.0194 0.0314 0.0511 0.0829 0.1631 e(x) 67.2 67.2 63.3 58.6 53.9 49.5 45.1 40.6 36.1 31.7 27.4 23.3 19.4 15.8 12.6 9.8 7.5 5.6 e(x) 71.2 71.6 67.8 62.9 58.1 53.3 48.6 43.8 39.1 34.4 29.8 25.4 21.2 17.3 13.8 10.8 8.2 6.1 207 Age 0 1 70 75 80 Age 0 1 70 75 80 m(x,n) 0.0245 0.0013 0.0006 0.0005 0.0010 0.0013 0.0013 0.0015 0.0019 0.0029 0.0048 0.0078 0.0130 0.0207 0.0331 0.0534 0.0860 0.1664 m(x,n) 0.0204 0.0008 0.0004 0.0003 0.0006 0.0008 0.0008 0.0009 0.0012 0.0020 0.0035 0.0060 0.0106 0.0174 0.0288 0.0476 0.0782 0.1581 Appendix 10: Abridged life table - Males, Sanma:
 2009 q(x,n) 0.0240 0.0050 0.0030 0.0025 0.0048 0.0066 0.0066 0.0074 0.0097 0.0145 0.0237 0.0385 0.0629 0.0986 0.1533 0.2364 0.3544 ... l(x) 100,000 97,600 97,112 96,821 96,577 96,109 95,475 94,845 94,139 93,229 91,880 89,700 86,248 80,820 72,853 61,684 47,101 30,410 d(x,n) 2,400 488 291 244 468 634 630 706 909 1,349 2,180 3,451 5,429 7,967 11,169 14,583 16,691 30,410 L(x,n) 97,868 389,219 484,831 483,493 481,806 478,997 475,813 472,513 468,543 463,020 454,377 440,529 418,621 385,392 337,778 273,242 194,108 182,774 S(x,n) 0.9742 0.9954 0.9972 0.9965 0.9942 0.9934 0.9931 0.9916 0.9882 0.9813 0.9695 0.9503 0.9206 0.8765 0.8089 0.7104 0.4850 ...
Appendix 11: Abridged life table - Females, Sanma: 2009 q(x,n) 0.0200 0.0030 0.0018 0.0015 0.0030 0.0042 0.0041 0.0046 0.0061 0.0097 0.0173 0.0298 0.0518 0.0837 0.1347 0.2138 0.3279 ... l(x) 100,000 98,000 97,706 97,532 97,384 97,096 96,686 96,289 95,845 95,256 94,329 92,695 89,936 85,281 78,145 67,616 53,161 35,728 d(x,n) 2,000 294 174 148 288 410 397 445 588 928 1,633 2,759 4,655 7,136 10,528 14,455 17,433 35,728 L(x,n) 98,200 391,292 488,095 487,290 486,262 484,484 482,446 480,372 477,842 474,160 467,932 457,183 438,966 409,787 365,977 303,518 223,014 226,048 S(x,n) 0.9790 0.9971 0.9984 0.9979 0.9963 0.9958 0.9957 0.9947 0.9923 0.9869 0.9770 0.9602 0.9335 0.8931 0.8293 0.7348 0.5034 ... T(x) 6,982,924 6,885,056 6,495,837 6,011,005 5,527,513 5,045,706 4,566,709 4,090,896 3,618,384 3,149,841 2,686,822 2,232,445 1,791,916 1,373,295 987,903 650,125 376,883 182,774 T(x) 7,242,868 7,144,668 6,753,375 6,265,281 5,777,991 5,291,729 4,807,245 4,324,799 3,844,428 3,366,585 2,892,426 2,424,494 1,967,311 1,528,345 1,118,557 752,580 449,062 226,048 e(x) 69.8 70.5 66.9 62.1 57.2 52.5 47.8 43.1 38.4 33.8 29.2 24.9 20.8 17.0 13.6 10.5 8.0 6.0 e(x) 72.4 72.9 69.1 64.2 59.3 54.5 49.7 44.9 40.1 35.3 30.7 26.2 21.9 17.9 14.3 11.1 8.4 6.3 208 Age 0 1 70 75 80 Age 0 1 70 75 80 m(x,n) 0.0245 0.0013 0.0012 0.0010 0.0021 0.0024 0.0025 0.0028 0.0035 0.0049 0.0072 0.0109 0.0168 0.0257 0.0395 0.0617 0.0972 0.1783 m(x,n) 0.0298 0.0018 0.0004 0.0003 0.0005 0.0008 0.0008 0.0009 0.0012 0.0019 0.0034 0.0059 0.0104 0.0171 0.0283 0.0470 0.0774 0.1572 Appendix 12: Abridged life table - Males, Penama: 2009 q(x,n) 0.0240 0.0050 0.0059 0.0050 0.0103 0.0120 0.0124 0.0141 0.0176 0.0242 0.0355 0.0532 0.0806 0.1212 0.1803 0.2682 0.3904 ... l(x) 100,000 97,600 97,112 96,541 96,054 95,063 93,921 92,751 91,448 89,839 87,665 84,549 80,049 73,597 64,680 53,016 38,798 23,652 d(x,n) 2,400 488 571 487 991 1,143 1,169 1,303 1,610 2,174 3,116 4,500 6,452 8,918 11,663 14,218 15,146 23,652 L(x,n) 97,868 389,219 484,133 481,488 477,969 472,500 466,713 460,586 453,389 444,058 431,008 412,181 385,041 346,806 295,407 230,374 155,899 132,613 S(x,n) 0.9742 0.9939 0.9945 0.9927 0.9886 0.9878 0.9869 0.9844 0.9794 0.9706 0.9563 0.9342 0.9007 0.8518 0.7799 0.6767 0.4596 ...
Appendix 13: Abridged life table - Females, Penama: 2009 q(x,n) 0.0290 0.0070 0.0018 0.0014 0.0025 0.0040 0.0039 0.0044 0.0058 0.0093 0.0167 0.0289 0.0507 0.0822 0.1328 0.2114 0.3251 ... l(x) 100,000 97,100 96,420 96,248 96,112 95,874 95,489 95,117 94,700 94,147 93,269 91,710 89,056 84,544 77,596 67,290 53,063 35,810 d(x,n) 2,900 680 172 137 238 385 372 417 553 878 1,560 2,654 4,512 6,948 10,307 14,227 17,252 35,810 L(x,n) 97,465 386,745 481,671 480,901 480,016 478,444 476,521 474,577 472,204 468,731 462,808 452,503 434,908 406,558 363,781 302,464 223,013 227,801 S(x,n) 0.9684 0.9948 0.9984 0.9982 0.9967 0.9960 0.9959 0.9950 0.9926 0.9874 0.9777 0.9611 0.9348 0.8948 0.8314 0.7373 0.5053 ... T(x) 6,617,252 6,519,385 6,130,165 5,646,032 5,164,545 4,686,576 4,214,077 3,747,364 3,286,778 2,833,388 2,389,330 1,958,322 1,546,141 1,161,100 814,294 518,886 288,512 132,613 T(x) 7,171,110 7,073,645 6,686,900 6,205,229 5,724,328 5,244,312 4,765,868 4,289,347 3,814,770 3,342,567 2,873,836 2,411,028 1,958,525 1,523,617 1,117,059 753,278 450,814 227,801 e(x) 66.2 66.8 63.1 58.5 53.8 49.3 44.9 40.4 35.9 31.5 27.3 23.2 19.3 15.8 12.6 9.8 7.4 5.6 e(x) 71.7 72.8 69.4 64.5 59.6 54.7 49.9 45.1 40.3 35.5 30.8 26.3 22.0 18.0 14.4 11.2 8.5 6.4 209 Appendix 14: Abridged life table - Males, Malampa: 2009 q(x,n) 0.0240 0.0050 0.0034 0.0029 0.0056 0.0074 0.0075 0.0085 0.0109 0.0161 0.0258 0.0412 0.0663 0.1030 0.1587 0.2429 0.3618 ... l(x) 100,000 97,600 97,112 96,780 96,500 95,957 95,243 94,530 93,730 92,707 91,215 88,860 85,198 79,547 71,353 60,029 45,449 29,005 d(x,n) 2,400 488 332 280 543 714 713 800 1,022 1,492 2,355 3,662 5,651 8,194 11,325 14,580 16,444 29,005 L(x,n) 97,868 389,219 484,729 483,198 481,247 478,040 474,448 470,708 466,226 460,067 450,631 435,815 412,815 378,451 329,847 264,890 186,347 171,837 S(x,n) 0.9742 0.9952 0.9968 0.9960 0.9933 0.9925 0.9921 0.9905 0.9868 0.9795 0.9671 0.9472 0.9168 0.8716 0.8031 0.7035 0.4797 ... T(x) 6,916,384 6,818,517 6,429,297 5,944,568 5,461,371 4,980,124 4,502,084 4,027,636 3,556,928
 3,090,702 2,630,634 2,180,004 1,744,188 1,331,373 952,922 623,075 358,184 171,837 Appendix 15: Abridged life table - Females, Malampa: 2009 q(x,n) 0.0160 0.0020 0.0018 0.0016 0.0033 0.0044 0.0043 0.0049 0.0064 0.0102 0.0179 0.0306 0.0529 0.0852 0.1366 0.2161 0.3307 ... l(x) 100,000 98,400 98,203 98,023 97,866 97,539 97,107 96,687 96,217 95,596 94,625 92,929 90,085 85,322 78,055 67,391 52,826 35,357 d(x,n) 1,600 197 180 158 327 432 420 470 620 972 1,695 2,844 4,763 7,267 10,664 14,565 17,469 35,357 L(x,n) 98,542 393,128 490,566 489,723 488,580 486,636 484,492 482,298 479,627 475,756 469,265 458,151 439,453 409,676 365,187 302,096 221,203 222,502 S(x,n) 0.9833 0.9978 0.9983 0.9977 0.9960 0.9956 0.9955 0.9945 0.9919 0.9864 0.9763 0.9592 0.9322 0.8914 0.8272 0.7322 0.5015 ... T(x) 7,256,880 7,158,339 6,765,210 6,274,644 5,784,922 5,296,342 4,809,706 4,325,214 3,842,916 3,363,289 2,887,533 2,418,268 1,960,117 1,520,664 1,110,988 745,801 443,705 222,502 m(x,n) 0.0245 0.0013 0.0007 0.0006 0.0011 0.0015 0.0015 0.0017 0.0022 0.0032 0.0052 0.0084 0.0137 0.0217 0.0343 0.0550 0.0882 0.1688 m(x,n) 0.0162 0.0005 0.0004 0.0003 0.0007 0.0009 0.0009 0.0010 0.0013 0.0020 0.0036 0.0062 0.0108 0.0177 0.0292 0.0482 0.0790 0.1589 Age 0 1 70 75 80 Age 0 1 70 75 80 e(x) 69.2 69.9 66.2 61.4 56.6 51.9 47.3 42.6 37.9 33.3 28.8 24.5 20.5 16.7 13.4 10.4 7.9 5.9 e(x) 72.6 72.7 68.9 64.0 59.1 54.3 49.5 44.7 39.9 35.2 30.5 26.0 21.8 17.8 14.2 11.1 8.4 6.3 210 Age 0 1 70 75 80 Age 0 1 70 75 80 m(x,n) 0.0224 0.0010 0.0006 0.0005 0.0009 0.0012 0.0012 0.0014 0.0018 0.0028 0.0046 0.0076 0.0126 0.0202 0.0324 0.0525 0.0848 0.1652 m(x,n) 0.0121 0.0003 0.0003 0.0003 0.0006 0.0008 0.0008 0.0009 0.0012 0.0019 0.0034 0.0059 0.0104 0.0171 0.0283 0.0470 0.0774 0.1572 Appendix 16: Abridged life table - Males, Shefa: 2009 q(x,n) 0.0220 0.0040 0.0028 0.0024
 0.0046 0.0062 0.0062 0.0070 0.0091 0.0137 0.0227 0.0371 0.0613 0.0964 0.1506 0.2331 0.3506 ... l(x) 100,000 97,800 97,409 97,140 96,911 96,464 95,866 95,274 94,610 93,752 92,468 90,368 87,013 81,683 73,812 62,697 48,081 31,225 d(x,n) 2,200 391 269 228 447 598 592 664 858 1,284 2,100 3,355 5,330 7,871 11,115 14,617 16,855 31,225 L(x,n) 98,033 390,256 486,371 485,127 483,527 480,859 477,861 474,760 471,022 465,789 457,509 444,104 422,689 389,955 342,737 278,274 198,662 189,065 S(x,n) 0.9766 0.9961 0.9974 0.9967 0.9945 0.9938 0.9935 0.9921 0.9889 0.9822 0.9707 0.9518 0.9226 0.8789 0.8119 0.7139 0.4876 ...
Appendix 17: Abridged life table - Females, Shefa: 2009 q(x,n) 0.0120 0.0010 0.0016 0.0014 0.0032 0.0040 0.0039 0.0044 0.0058 0.0093 0.0167 0.0289 0.0507 0.0822 0.1328 0.2114 0.3251 ... l(x) 100,000 98,800 98,701 98,546 98,403 98,083 97,689 97,309 96,882 96,316 95,419 93,823 91,108 86,492 79,384 68,840 54,285 36,636 d(x,n) 1,200 99 155 143 319 394 381 426 566 898 1,596 2,715 4,616 7,108 10,544 14,555 17,650 36,636 L(x,n) 98,892 394,964 493,116 492,370 491,282 489,446 487,501 485,512 483,084 479,532 473,472 462,929 444,929 415,927 372,163 309,434 228,151 233,050 S(x,n) 0.9877 0.9985 0.9985 0.9978 0.9963 0.9960 0.9959 0.9950 0.9926 0.9874 0.9777 0.9611 0.9348 0.8948 0.8314 0.7373 0.5053 ... T(x) 7,036,602 6,938,570 6,548,313 6,061,942 5,576,815 5,093,288 4,612,429 4,134,568 3,659,807 3,188,785 2,722,996 2,265,486 1,821,383 1,398,693 1,008,738 666,001 387,727 189,065 T(x) 7,335,757 7,236,864 6,841,900 6,348,784 5,856,413 5,365,131 4,875,685 4,388,184 3,902,672 3,419,587 2,940,056 2,466,584 2,003,654 1,558,725 1,142,799 770,635 461,202 233,050 e(x) 70.4 70.9 67.2 62.4 57.5 52.8 48.1 43.4 38.7 34.0 29.4 25.1 20.9 17.1 13.7 10.6 8.1 6.1 e(x) 73.4 73.2 69.3 64.4 59.5 54.7 49.9 45.1 40.3 35.5 30.8 26.3 22.0 18.0 14.4 11.2 8.5 6.4 211 Age 0 1 70 75 80 Age 0 1 70 75 80 m(x,n) 0.0245 0.0013 0.0007 0.0006 0.0012 0.0015 0.0015 0.0017 0.0022 0.0033 0.0053 0.0085 0.0138 0.0218 0.0345 0.0553 0.0886 0.1692 m(x,n) 0.0371 0.0025 0.0003 0.0002 0.0003 0.0006 0.0006 0.0006 0.0009 0.0014 0.0027 0.0050 0.0091 0.0153 0.0259 0.0437 0.0728 0.1524 Appendix 18: Abridged life table - Males, Tafea: 2009 q(x,n) 0.0240 0.0050 0.0035 0.0030 0.0058 0.0076 0.0076 0.0086 0.0111 0.0164 0.0262 0.0417 0.0669 0.1037 0.1596 0.2439 0.3630 ... l(x) 100,000 97,600 97,112 96,772 96,486 95,930 95,203 94,475 93,659 92,617 91,100 88,716 85,019 79,332 71,102 59,754 45,178 28,777 d(x,n) 2,400 488 340 286 556 727 728 816 1,042 1,516 2,385 3,697 5,687 8,230 11,348 14,576 16,401 28,777 L(x,n) 97,868 389,219 484,711 483,146 481,149 477,872 474,211 470,394 465,823 459,555 449,984 435,008 411,829 377,282 328,523 263,510 185,078 170,082 S(x,n) 0.9742 0.9951 0.9968 0.9959 0.9932 0.9923 0.9920 0.9903 0.9865 0.9792 0.9667 0.9467 0.9161 0.8708 0.8021 0.7024 0.4789 ...
Appendix 19: Abridged life table - Females, Tafea: 2009 q(x,n) 0.0360 0.0100 0.0013 0.0010 0.0016 0.0030 0.0029 0.0032 0.0044 0.0072 0.0136 0.0245 0.0447 0.0739 0.1222 0.1981 0.3093 ... l(x) 100,000 96,400 95,436 95,309 95,212 95,057 94,771 94,499 94,195 93,784 93,107 91,836 89,584 85,583 79,255 69,570 55,785 38,533 d(x,n) 3,600 964 127 97 155 286 272 304 411 677 1,271 2,252 4,001 6,328 9,685 13,785 17,253 38,533 L(x,n) 96,926 383,233 476,863 476,304 475,710 474,604 473,179 471,761 470,015 467,387 462,677 454,091 438,782 413,272 373,659 315,098 236,889 252,804 S(x,n) 0.9603 0.9931 0.9988 0.9988 0.9977 0.9970 0.9970 0.9963 0.9944 0.9899 0.9814 0.9663 0.9419 0.9041 0.8433 0.7518 0.5162 ... T(x) 6,905,245 6,807,377 6,418,158 5,933,447 5,450,300 4,969,152 4,491,280 4,017,069 3,546,675 3,080,852 2,621,297 2,171,313 1,736,305 1,324,476 947,194 618,670 355,160 170,082 T(x) 7,213,254 7,116,328 6,733,095 6,256,232 5,779,928 5,304,219 4,829,614 4,356,435 3,884,675 3,414,659 2,947,273 2,484,596 2,030,505 1,591,723 1,178,451 804,792 489,694 252,804 e(x) 69.1 69.7 66.1 61.3 56.5 51.8 47.2 42.5 37.9 33.3 28.8 24.5 20.4 16.7 13.3 10.4 7.9 5.9 e(x) 72.1 73.8 70.6 65.6 60.7 55.8 51.0 46.1 41.2 36.4 31.7 27.1 22.7 18.6 14.9 11.6 8.8 6.6 212 Appendix 20: Total population size and growth by place of residence and sex, Vanuatu: 1999 -2009 Place of residence Total population size 1999 Female Male Total Male 2009 Female Total (in numbers) Female Male Population change from 1999 to 2009 (in %) Female Male Total Total VANUATU 95682 90996 186678 119091 114932 234023 23409 23936 47345 URBAN Port Vil a Luga nvil l e 20726 15189 5537 19368 14167 5201 40094 29356 10738 29618 22852 6766 27577 21187 6390 57195 44039 13156 8892 7663 1229 8209 7020 1189 17101 14683 2418 RURAL TORBA Ga ua Hi u Kwa kea Loh Merel a va Merig Metoma Mota Mota l a va Ra h Tegua Toga Urepa ra pa ra Va nua l a va SANMA 74956 71628 146584 89473 87355 176828 14517 15727 30244 3937 1026 92 - 61 347 8 5 337 606 73 17 148 185 1032 3820 1005 108 - 78 403 11 5 349 540 70 22 150
 178 901 7757 2031 200 - -51 -1 3 0 110 22 7 -17 41 356 812 224 39 14 29 -52 -6 0 -3 195 24 12 -5 33 308 1602 460 69 26 71 -103 -7 3 -3 305 46 19 -22 74 664 18676 2 236 52 16 1868 9 100 15923 188 281 1 Aes e Aore Ara ki Bokis s a Ma l o Ma l oki l iki l i Ma vea Sa nto Ta ngoa Tutuba Urel a pa = places with an annual population growth rate of higher than 3 percent between 1999 and 2009 = places that decreased in population size between 1999 and 2009 18.2 25.0 87.5 21.0 -33.3 4.0 27.9 5.9 10.7 45855 - 556 140 56 4279 13 207 39601 394 609 - 23623 - 279 65 30 2260 6 104 20369 199 311 - 17408 - 206 46 14 1664 6 72 14977 185 237 1 22232 - 277 75 26 2019 7 103 19232 195 298 - 36084 2 442 98 30 3532 15 172 30900 373 518 2 9771 -2 114 42 26 747 -2 35 8701 21 91 -2 4824 0 71 29 12 355 1 31 4255 10 61 -1 4947 -2 43 13 14 392 -3 4 4446 11 30 -1 24.5 42.9 50.5 22.2 19.4 20.1 23.0 32.6 68.9 -14.7 -12.5 60.0 0.0 18.2 30.1 41.2 -11.5 22.2 34.5 26.3 25.4 42.4 49.6 22.9 42.7 50.0 22.5 22.0 20.6 21.3 22.3 36.1 37.2 -12.9 -54.5 0.0 -0.9 36.1 34.3 54.5 -3.3 18.5 34.2 20.7 22.6 34.5 51.1 -13.7 -36.8 30.0 -0.4 26.6 32.2 48.7 -7.4 20.4 34.4 26.5 27.7 27.1 34.5 63.0 85.7 21.3 16.7 43.1 28.4 5.4 25.7 25.8 42.9 86.7 21.1 -13.3 20.3 28.2 5.6 17.6 Annual growth rate Male Female Total 2.2 3.6 4.1 2.0 1.8 1.8 2.1 2.8 5.2 -1.6 -1.3 4.7 0.0 1.7 2.6 3.4 -1.2 2.0 3.0 2.3 1.7 2.2 6.3 1.9 -4.1 0.4 2.5 0.6 1.0 2.3 3.5 4.0 2.1 2.0 1.9 2.0 3.1 3.2 -1.4 -7.9 0.0 -0.1 3.1 2.9 4.4 -0.3 1.7 2.9 2.4 3.0 4.9 6.2 1.9 1.5 3.6 2.5 0.5 2.3 2.3 3.6 4.1 2.0 1.9 1.9 2.0 3.0 4.1 -1.5 -4.6 2.6 0.0 2.4 2.8 4.0 -0.8 1.9 3.0 2.4 2.3 3.6 6.2 1.9 -1.4 1.9 2.5 0.5 1.6 213 Appendix 20: Total population size and growth by place of residence and sex, Vanuatu: 1999 -2009 (cont’d.)
Total population size 1999 Female 12922 4536 1515 6871 Male 13724 4882 1656 7186 Total Male 26646 9418 3171 14057 15543 5325 1805 8413 2009 Female 15276 5082 1764 8430 Total 30819 10407 3569 16843 1819 443 149 1227 2354 546 249 1559 4173 989 398 2786 (in numbers) Female Male Population change from 1999 to 2009 (in %) Female Total Male Total Annual growth rate Male Female Total Place of residence PENAMA Amba e Ma ewo Pentecos t MALAMPA 16653 311 3763 152 377 74 16052 256 3606 138 384 95 32705 567 7369 290 761 169 - 18446 324 3638 - 357 79 8 18281 322 3637 - 381 102 6 36727 646 7275 - 738 181 14 - 1 9762 425 - 1 18984 944 - - - 9222 519 - Akha mb Ambrym Ars eo Atchi n Avock Khoti Lopevi Ma l ekul a Ma s kel ynes Lembong Nors up Pa a ma Ra no Tomma n Uri Uri pi v Va o Wa l a = places with an annual population growth rate of higher than 3 percent between 1999 and 2009 = places that decreased in population size between 1999 and 2009 - 22902 1021 60 88 1627 304 290 29 384 898 270 - 11302 540 29 47 823 149 141 14 204 446 138 - -138 -3 7 6 0 2080 21 29 -2 -25 5 33 -18 -16 104 49 4022 79 -94 -290 -23 12 14 -1 3918 77 60 -4 -6 31 46 -37 -60 231 69 1793 13 -125 -152 -20 5 8 -1 1838 56 31 -2 19 26 13 -19 -44 127 20 13.3 9.1 9.0 17.1 10.8 4.2 -3.3 -5.3 6.8 18.8 13.2 -4.7 2.4 20.2 9.6 -19.6 39.1 17.9 18.2 12.0 16.4 22.7 13.9 25.8 0.9 -0.8 7.4 22.6 4.0 -4.1 -2.9 3.5 30.6 -7.3 30.4 55.1 15.7 10.5 12.6 19.8 12.3 13.9 -1.3 -3.0 7.1 20.6 8.2 -4.3 -0.4 11.4 18.9 -13.5 34.6 34.3 1.2 0.9 0.9 1.6 1.0 0.4 -0.3 -0.5 0.7 1.7 1.2 -0.5 0.2 1.8 0.9 -2.2 3.3 1.6 1.7 1.1 1.5 2.0 1.3 2.3 0.1 -0.1 0.7 2.0 0.4 -0.4 -0.3 0.3 2.7 -0.8 2.7 4.4 1.5 1.0 1.2 1.8 1.2 1.3 -0.1 -0.3 0.7 1.9 0.8 -0.4 0.0 1.1 1.7 -1.5 3.0 3.0 214 Appendix 20: Total population size and growth by place of residence and sex, Vanuatu: 1999 -2009 (cont’d.)
Population change from 1999 to 2009 (in %) Female Male Total Total Annual growth rate Male Female Total Total population size 1999 Female Total Male 2009 Female 26320 82 20233 403 358 2211 481 - - 208 169 67 51 119 492 97 137 1212 54439 160 42128 850 705 4554 983 - - Male 28119 78 21895 447 347 2343 502 - - 202 181 65 50 115 467 123 119 1185 Total 78723 128 65734 743 602 5207 811 98 4 440 387 106 74 237 1255 330 267 2300 (in numbers) Female Male 12431 -13 12156 -45 -54 284 -107 52 2 6 22 -10 -19 9 159 47 13 -71 11853 -19 11450 -62 -49 369 -65 46 2 24 15 -16 -8 -6 137 63 -2 -26 24284 -32 23606 -107 -103 653 -172 98 4 30 37 -26 -27 3 296 110 11 -97 44.2 -16.7 55.5 -10.1 -15.6 12.1 -21.3 3.0 12.2 -15.4 -38.0 7.8 34.0 38.2 10.9 -6.0 Place of residence SHEFA Buni nga Efa te Ema e Ema u Epi Ifi ra Iri ri ki Ka kul a La men Lel epa Ma ki ra Ma ta s o Mos o Nguna Pel e Tonga ri ki Tongoa TAFEA 14573 420 218 817 202 12916 Anei tyum Ani wa Erroma ngo Futuna Ta nna = places with an annual population growth rate of higher than 3 percent between 1999 and 2009 = places that decreased in population size between 1999 and -54 152 62 1408 3493 94 -83 390 133 2959 1864 33 -29 238 71 1551 11.2 14.5 -24.8 18.6 30.7 10.9 45.0 -23.2 56.6 -15.4 -13.7 16.7 -13.5 11.5 8.9 -23.9 -15.7 -5.0 27.8 64.9 -1.5 -2.1 12.9 8.2 -14.1 32.0 35.5 12.0 44.6 -20.0 56.0 -12.6 -14.6 14.3 -17.5 7.3 10.6 -19.7 -26.7 1.3 30.9 50.0 4.3 -4.0 12.0 11.4 -19.6 25.0 33.1 11.5 3.7 -1.8 4.4 -1.1 -1.7 1.1 -2.4 0.3 1.1 -1.7 -4.8 0.8 2.9 3.2 1.0 -0.6 1.1 1.4 -2.8 1.7 2.7 1.0 3.7 -2.6 4.5 -1.7 -1.5 1.5 -1.5 1.1 0.9 -2.7 -1.7 -0.5 2.5 5.0 -0.1 -0.2 1.2 0.8 -1.5 2.8 3.0 1.1 3.7 -2.2 4.4 -1.3 -1.6 1.3 -1.9 0.7 1.0 -2.2 -3.1 0.1 2.7 4.1 0.4 -0.4
 1.1 1.1 -2.2 2.2 2.9 1.1 215 Appendix 21: Population aged 15 and older by labour market activity, sex, and residence, Vanuatu: 2009 Labour Force Non Labour Force Self employed Employee for wage/salary Employer Unpaid worker Subsistence work Un- employed Full time student Home duties Retired/ Old age Total Disabled Other* Total 2,525
Region/ Sex Vanuatu Males Females Urban Males Females Rural Males Females Torba Males Females Sanma Males Females Penama Males Females Malampa Males Females Shefa Males Females Tafea Males Females * includes: 2,751 people who did not want to work, 897 who did not work because they believed no work was available, 51 who did not work because of weather/transport, 988 ‘Other reasons’, 3,129 not stated, and 939 people who were not available to work 216 Appendix 22: Level of TFR of Australia, France, New Zealand, and the USA since 1975 Total Fertility rate (TFR) R F T ) ( 2.5 2.0 1.5 1.0 0.5 0.0 Year USA NZ France Australia Average 217 Appendix 23: Models for mortality improvement.
Quinquennial gains in life expectancy at birth according to initial level of life expectancy (P.125) Initial life expectancy level (years) 40.0 - 42.5 42.5 - 45.0 45.0 - 47.5 47.5 - 50.0 50.0 - 52.5 52.5 - 55.0 55.0 - 57.5 57.5 - 60.0 60.0 - 62.5 62.5 - 65.0 65.0 -67.5 67.5 - 70.0 70.0 - 72.5 72.5 - 75.0 75.0 - 77.5 77.5 - 80.0 80.0 - 82.5 82.5 - 85.0 85.0 - 87.5 87.5 - 90.0 90.0 - 92.5 Fast Slow Very fast Very slow pace of mortality improvement Medium Male Female Male Female Male Female Male Female Male Female Source: Table Vi.6.
Models for mortality improvement: Quinquennial gains in Life Expectancy at Birth according to initial level of Life Expectancy (1995.

United Nations.

World Population Prospects.

NewYork: United Nations.

886 p 218 Appendix 24: The demographic transition According to the theory of demographic transition, over time all countries will undergo change from high rates of births and deaths to low rates of births and deaths.
This transition process is usually closely associated with economic, social and scientific developments.
This is assumed to happen in four distinct stages: Stage 1: High birth rate, high death rate growth Stage 2: High birth rate, falling death rate Stage 3: Declining birth rate, relatively low death rate Stage 4: Low birth rate, low death rate  little or no population  high growth  slowed growth  very low growth Historically, high levels of births and deaths kept most populations from growing rapidly through time.
In fact, many populations not only failed to grow but also completely died out when birth rates did not compensate for high death rates (stage 1).

There are few populations/communities left today at stage 1.

Death rates eventually fell as living conditions, nutrition and public health improved.

The decline in mortality usually preceded the decline in fertility, resulting in population growth during the transition period (stage 2).

In Europe and other industrialised countries, death rates fell slowly.
With the added benefit of medical advances, death rates fell more rapidly in the countries that began the transition in the 20th century.

These are/were primarily developing countries.

Their death rates often fell much faster than in European countries because they benefited from Western inventions and innovations.

In general, fertility rates fell neither as quickly nor as dramatically as death rates, and thus populations grew rapidly.
Stage 3 is characterized by falling birth rates, which occur for many reasons and vary from country to country and population to population.

A decrease in birth rates may result from: a transition from a non-monetary to a monetary economy, urbanization, a change in values from a community emphasis to individualism, increasing emphasis on consumerism, improved education, availability of (modern) family planning methods (i.e.
contraceptives), greater involvement of women in the workplace, rising cost of living, rising cost of raising children, and preferences in how people want to spend their time.

The demographic transition is regarded as completed when both birth and death rates have reached a low and stable level (stage 4).

As a result, population growth is very low.

Originally, the theory of demographic transition included only the four stages described above.
There is now another stage, the post-transition period (although it is uncertain whether all countries will reach this stage).

Post-transition period: Very low birth rate, low death rate  negative growth When fertility falls to very low levels and stays there for a protracted period, a slow rate of population growth can turn into a negative one, resulting in a population decrease.

Many countries in Europe and some in Asia now have TFRs well below two children per 219 woman.
The TFRs of the Republic of Korea, Ukraine, Czech Republic, Slovakia, Slovenia, Republic of Moldova, Bulgaria, and Belarus — all about 1.2 — are among the world’s lowest, and those of several other countries were not far behind.

The TFRs of Macao and Hong Kong were even less than 1 child per woman on average.
Many of the factors that lowered fertility in the first place — greater involvement of women in the workplace, rising cost of living, and preferences in how people want to spend their time — appear to be keeping fertility rates very low.

While the theory of demographic transition describes the population history of western Europe quite well, for many reasons developing countries do not always exhibit the same patterns of change.
In some cases early contact with outside societies resulted in local epidemics, as groups succumbed to diseases against which they had no natural immunity, resulting in increased death rates.

When health conditions improved as a result of the application of new and efficient disease control technologies, death rates declined, while birth rates sometimes increased.
This combination of factors produced population growth rates in today's developing countries that are much higher than ever experienced in pre- industrial Western Europe.

Stylised graph of the European demographic transition Sources: 2004.

Population Handbook, Population Reference Bureau, Inc, Washington D.C., 5th Edition; 1999.
Papua New Guinea National Population Policy 2000–2010, Department of Planning 220 Appendix 25: Base population for projections for each province, 1 July 2009 Age group 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total Age group 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total Males 691 TORBA Females 695 Total 1,385 Males 3,460 SANMA Females 3,170 Total PENAMA Females 2,233 Total Males 2,315 2,065 2,121 1,886 1,279 1,012 899 898 732 644 473 372 309 292 192 180 208 4,833 4,709 9,542 24,194 22,648 46,841 15,877 15,518 31,394 MALAMPA Females 2,467 Total 5,105 Males 5,221 SHEFA Females Males 2,637 2,587 2,459 2,049 1,518 1,190 1,109 994 907 768 597 535 387 394 224 216 244 Total 9,996 8,131 7,745 9,691 10,152 6,846 6,056 5,313 4,471 3,718 2,694 2,160 1,346 1,091 521 407 418 Males 2,826 2,670 2,366 1,597 1,312 1,079 876 860 633 572 436 405 243 290 128 115 88 TAFEA Females 2,596 Total 5,422 2,451 2,068 1,555 1,499 1,239 1,049 922 771 661 439 394 301 267 165 121 79 5,121 4,434 3,152 2,811 2,318
Appendix 26: UN migration model: Family migration VANUATU Males Females p u o r g e g A 75+ 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 25 20 15 10 5 0 % Source: UN Population Division, New York, USA 222 Appendix 27: Age and sex structure for Vanuatu and each province: 1999 and 2009 223 l e p o e p f o r e b m u N s d n a s u o h T 250 200 150 100 50 0 VANUATU Population trend: 1967–2009 234 187 142 111 78 1967 1979 1989 Census year 1999 2009 Population pyramid by five-year age group and sex, 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 20,000 16,000 12,000 8,000 4,000 0 4,000 8,000 12,000 16,000 20,000 Number of people 224 VANUATU 1999 2009 Age Males Females Total Age Males Females Total Population by 5-year age groups and sex 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 0-14 15-24 25-59 25-64 60+ 65+ 0-14 15-24 25-59 25-64 60+ 65+ 15-59 15- 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 17,310 15,455 14,762 12,027 10,415 9,124 7,790 7,076 5,814 5,066 3,789 3,261 2,192 2,054 1,085 941 930 119,091 Population by broad age groups (in numbers) 38,397 17,051 31,336 32,764 4,212 2,783 79,734 34,215 63,323 66,348 9,407 6,382 0-14 15-24 25-59 25-64 60+ 65+ 47,527 22,442
 41,920 44,112 7,202 5,010 Population by broad age groups (in percentages) 42 19 34 36 5 3 43 18 34 36 5 3 0-14 15-24 25-59 25-64 60+ 65+ 40 19 35 37 6 4 Age dependency ratio 91 86 15-59 15-64 Sex ratio (males per 100 females) 105 Median age (years) Total 18.6 19.1 18.8 Total 20.0 21.0 20.5 Population change 1999-2009 Total difference Average annual change (in numbers) Percentage difference (%) Average annual growth rate (%) Males 23,409 2,341 24.5 2.19 Females 23,936 2,394 26.3 2.34 Total 47,345 4,735 25.4 2.26 225 TORBA Population trend: 1967–2009 3 1967 5 1979 6 1989 Census year 8 1999 9 2009 l e p o e p f o r e b m u N s d n a s u o h T 100 90 80 70 60 50 40 30 20 10 0 Population pyramid by five-year age group and sex, 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 1,000 800 600 400 200 0 200 400 600 800 1,000 Number of people 226 1999 2009 Age Males Females Total Age Males Females Total Population by 5-year age groups and sex TORBA 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 0-14 15-24 25-59 25-64 60+ 65+ 0-14 15-24 25-59 25-64 60+ 65+ 15-59 15- 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 697 688 681 483 363 363 267 287
 209 146 137 99 96 74 54 35 48 4,727 Population by broad age groups (in numbers) 1,735 687 1,185 1,242 214 156 3,624 1,334 2,350 2,487 450 313 0-14 15-24 25-59 25-64 60+ 65+ 2,066 846 1,508 1,604 307 211 Population by broad age groups (in percentages) 45 18 31 33 6 4 47 17 30 32 6 4 0-14 15-24 25-59 25-64 60+ 65+ 44 18 32 34 6 4 Age dependency ratio 111 103 15-59 15-64 Sex ratio (males per 100 females) 103 Median age (years) Total 16.1 17.6 16.8 Total 18.1 19.3 18.7 Population change 1999-2009 Total difference Average annual change (in numbers) Percentage difference (%) Average annual growth rate (%) Males 790 79 20.1 1.8 Females 812 81 21.3 1.9 Total 1,602 160 20.7 1.9 227 SANMA Population trend: 1967–2009 46 36 26 19 13 s d n a s u o h T l e p o e p f o r e b m u N 100 90 80 70 60 50 40 30 20 10 0 1967 1979 1989 Census year 1999 2009 Population pyramid by five-year age group and sex, 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 4,000 3,200 2,400 1,600 800 0 800 1,600 2,400 3,200 4,000 Number of people 228 1999 2009 Age Males Females Total Age Males Females Total Population by 5-year age groups and sex SANMA 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 0-14 15-24 25-59 25-64 60+ 65+ 0-14 15-24 25-59 25-64 60+ 65+ 15-59 15- 168
2,120 1,519 1,289 956 702 476 352 204 129 168 36,084 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 3,492 3,159 2,970 2,441 1,989 1,875 1,564 1,368 1,076 996 701 643 426 401 213 165 144 23,623 Population by broad age groups (in numbers) 7,636 3,466 5,780 5,995 526 311 15,777 6,971 12,007 12,483 1,329 853 0-14 15-24 25-59 25-64 60+ 65+ 9,621 4,430 8,223 8,649 1,349 923 Population by broad age groups (in percentages) 44 20 33 34 3 2 44 19 33 35 4 2 0-14 15-24 25-59 25-64 60+ 65+ 41 19 35 37 6 4 Age dependency ratio 90 85 15-59 15-64 Sex ratio (males per 100 females) 107 Median age (years) Total 18.1 17.9 18.0 Total 19.5 19.7 19.6 Population change 1999-2009 Total difference Average annual change (in numbers) Percentage difference (%) Average annual growth rate (%) Males 4,947 495 26.5 2.3 Females 4,824 482 27.7 2.4 Total 9,771 977 27.1 2.4 229 PENAMA Population trend: 1967–2009 19 22 14 27 31 s d n a s u o h T l e p o e p f o r e b m u N 100 90 80 70 60 50 40 30 20 10 0 1967 1979 1989 Census year 1999 2009 Population pyramid by five-year age group and sex, 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 3,000 2,400 1,800 1,200 600 0 600 1,200 1,800 2,400 3,000 Number of people 230 PENAMA 1999 2009 Age Males Females Total Age Males Females Total Population by 5-year age groups and sex 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 0-14 15-24 25-59 25-64 60+ 65+ 0-14 15-24 25-59 25-64 60+ 65+ 15-59 15-64 2,090 2,097 1,859 1,350 1,033 988 844 738 545 473 378 353 255 254 149 110
0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 2,337 2,084 2,141 1,638 1,074 1,021 907 906 739 650 477 376 312 295 194 182 210 15,543 Population by broad age groups (in numbers) 5,478 2,310 4,325 4,565 808 568 11,525 4,694 8,642 9,138 1,785 1,289 0-14 15-24 25-59 25-64 60+ 65+ 6,562 2,712 5,076 5,388 1,193 881 Population by broad age groups (in percentages) 42 18 33 35 6 4 43 18 32 34 7 5 0-14 15-24 25-59 25-64 60+ 65+ 42 17 33 35 8 6 Age dependency ratio 100 93 15-59 15-64 Sex ratio (males per 100 females) 106 Median age (years) Total 18.0 18.9 18.5 Total 18.7 19.9 19.3 Population change 1999-2009 Total difference Average annual change (in numbers) Percentage difference (%) Average annual growth rate (%) Males 1,819 182 13.3 1.2 Females 2,354 235 18.2 1.7 Total 4,173 417 15.7 1.5 231 MALAMPA Population trend: 1967–2009 24 28 17 33 37 s d n a s u o h T l e p o e p f o r e b m u N 100 90 80 70 60 50 40 30 20 10 0 1967 1979 1989 Census year 1999 2009 Population pyramid by five-year age group and sex, 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 4,000 3,200 2,400 1,600 800 0 800 1,600 2,400 3,200 4,000 Number of people 232 MALAMPA 1999 2009 Age Males Females Total Age Males Females Total Population by 5-year age groups and sex 0-4 5-9 10-14 15-19 20-24 25-29
 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 0-14 15-24 25-59 25-64 60+ 65+ 0-14 15-24 25-59 25-64 60+ 65+ 15-59 15- 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 2,662 2,611 2,482 1,780 1,275 1,201 1,119 1,003 916 775 603 540 391 398 226 218 246 18,446 Population by broad age groups (in numbers) 6,685 2,864 5,548 5,854 956 650 13,895 5,807 10,840 11,479 2,163 1,524 0-14 15-24 25-59 25-64 60+ 65+ 7,755 3,055 6,157 6,548 1,479 1,088 Population by broad age groups (in percentages) 42 18 35 36 6 4 42 18 33 35 7 5 0-14 15-24 25-59 25-64 60+ 65+ 42 17 33 35 8 6 Age dependency ratio 96 89 15-59 15-64 Sex ratio (males per 100 females) 104 Median age (years) Total 18.4 19.4 18.9 Total 19.1 21.8 20.4 Population change 1999-2009 Total difference Average annual change (in numbers) Percentage difference (%) Average annual growth rate (%) Males 1,793 179 10.8 1.0 Females 2,229 223 13.9 1.3 Total 4,022 402 12.3 1.2 233 SHEFA Population trend: 1967–2009 79 54 38 27 18 l e p o e p f o r e b m u N s d n a s u o h T 100 90 80 70 60 50 40 30 20 10 0 1967 1979 1989 Census year 1999 2009 Population pyramid by five-year age group and sex, 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29
 20-24 15-19 10-14 5- 9 0- 4 6,000 5,000 4,000 3,000 2,000 1,000 0 1,000 2,000 3,000 4,000 5,000 6,000 Number of people 234 1999 2009 Age Males Females Total Age Males Females Total Population by 5-year age groups and sex SHEFA 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 0-14 15-24 25-59 25-64 60+ 65+ 0-14 15-24 25-59 25-64 60+ 65+ 15-59 15- 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 5,270 4,218 4,100 4,298 4,612 3,575 3,049 2,644 2,235 1,922 1,431 1,194 722 593 269 225 193 40,550 Population by broad age groups (in numbers) 10,201 5,222 9,919 10,243 978 655 21,069 10,611 20,580 21,323 2,179 1,436 0-14 15-24 25-59 25-64 60+ 65+ 13,588 8,910 16,050 16,772 2,002 1,280 Population by broad age groups (in percentages) 39 20 38 39 4 2 39 19 38 39 4 3 0-14 15-24 25-59 25-64 60+ 65+ 34 22 40 41 5 3 Age dependency ratio 75 70 15-59 15-64 Sex ratio (males per 100 females) 107 Median age (years) Total 21.0 20.8 20.9 Total 22.6 22.5 22.6 Population change 1999-2009 Total difference Average annual change (in numbers) Percentage difference (%) Average annual growth rate (%) Males 12,431 1,243 44.2 3.7 Females 11,853 1,185 45.0 3.7 Total 24,284 2,428 44.6 3.7 235 TAFEA Population trend: 1967–2009 18 12 1967 1979 22
 1989 Census year 29 33 1999 2009 l e p o e p f o r e b m u N s d n a s u o h T 100 90 80 70 60 50 40 30 20 10 0 Population pyramid by five-year age group and sex, 1999 and 2009 1999 (shaded area) & 2009 (outlined) Males Females p u o r g e g A 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 4,000 3,000 2,000 1,000 0 1,000 2,000 3,000 4,000 Number of people 236 1999 2009 Age Males Females Total Age Males Females Total Population by 5-year age groups and sex TAFEA 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 0-14 15-24 25-59 25-64 60+ 65+ 0-14 15-24 25-59 25-64 60+ 65+ 15-59 15- 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total 2,852 2,695 2,388 1,387 1,102 1,089 884 868 639 577 440 409 245 293 129 116 89 16,202 Population by broad age groups (in numbers) 6,662 2,503 4,579 4,865 730 443 13,843 4,798 8,904 9,438 1,502 968 0-14 15-24 25-59 25-64 60+ 65+ 7,935 2,489 4,906 5,151 872 627 Population by broad age groups (in percentages) 46 17 32 34 5 3 48 17 31 32 5 3 0-14 15-24 25-59 25-64 60+ 65+ 49 15 30 32 5 4 Age dependency ratio 112 104 15-59 15-64 Sex ratio (males per 100 females) 101 Median age (years) Total 15.4 17.4 16.4 Total 15.6 18.6 17.1 Population change 1999-2009 Total difference Average annual change
 (in numbers) Percentage difference (%) Average annual growth rate (%) Males 1,629 163 11.2 1.1 Females 1,864 186 12.9 1.2 Total 3,493 349 12.0 1.1 237