Vanuatu GDP Composition by Sector, 2016 Discover more data with ReportLinker!

This statistic shows the share of economic sectors in the gross domestic product (GDP) in Vanuatu from 2012 to 2022. In 2022, the share of agriculture in Vanuatu's gross domestic product was 24.88 percent, industry contributed approximately 7.55 percent and the services sector contributed about 60.45 percent.

Vanuatu VU: GDP: 2010 Price: USD: Gross Value Added Per Worker: Industry data was reported at 0.009 USD mn in 2014. This records a decrease from the previous number of 0.010 USD mn for 2013. Vanuatu VU: GDP: 2010 Price: USD: Gross Value Added Per Worker: Industry data is updated yearly, averaging 0.012 USD mn from Dec 1991 (Median) to 2014, with 24 observations. The data reached an all-time high of 0.017 USD mn in 1991 and a record low of 0.009 USD mn in 2007. Vanuatu VU: GDP: 2010 Price: USD: Gross Value Added Per Worker: Industry data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank: Gross Domestic Product: Real. Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2010 U.S. dollars. Industry corresponds to the International Standard Industrial Classification (ISIC) tabulation categories C-F (revision 3) or tabulation categories B-F (revision 4), and includes mining and quarrying (including oil production), manufacturing, construction, and public utilities (electricity, gas, and water).; ; Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database.; Weighted Average;

Vanuatu VU: GDP: 2015 Price: USD: Gross Value Added Per Worker: Industry data was reported at 0.010 USD mn in 2018. This records an increase from the previous number of 0.010 USD mn for 2017. Vanuatu VU: GDP: 2015 Price: USD: Gross Value Added Per Worker: Industry data is updated yearly, averaging 0.011 USD mn from Dec 1991 (Median) to 2018, with 28 observations. The data reached an all-time high of 0.020 USD mn in 1994 and a record low of 0.007 USD mn in 2014. Vanuatu VU: GDP: 2015 Price: USD: Gross Value Added Per Worker: Industry data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Gross Domestic Product: Real. Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2015 U.S. dollars. Industry corresponds to the International Standard Industrial Classification (ISIC) tabulation categories C-F (revision 3) or tabulation categories B-F (revision 4), and includes mining and quarrying (including oil production), manufacturing, construction, and public utilities (electricity, gas, and water).;Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database.;Weighted average;

This scatter chart displays land area (km²) against tax revenue (% of GDP) in Vanuatu. The data is about countries per year.

Vanuatu VU: Domestic Credit: to Private Sector: % of GDP data was reported at 66.787 % in 2017. This records a decrease from the previous number of 68.515 % for 2016. Vanuatu VU: Domestic Credit: to Private Sector: % of GDP data is updated yearly, averaging 36.223 % from Dec 1979 (Median) to 2017, with 39 observations. The data reached an all-time high of 71.810 % in 2015 and a record low of 29.149 % in 1985. Vanuatu VU: Domestic Credit: to Private Sector: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Bank Loans. Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. The financial corporations include monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.; ; International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.; Weighted average;

Vanuatu VU: Claims on Other Sectors of The Domestic Economy: % of GDP data was reported at 68.295 % in 2017. This records a decrease from the previous number of 70.132 % for 2016. Vanuatu VU: Claims on Other Sectors of The Domestic Economy: % of GDP data is updated yearly, averaging 63.168 % from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 73.564 % in 2014 and a record low of 34.379 % in 2001. Vanuatu VU: Claims on Other Sectors of The Domestic Economy: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Bank Loans. Claims on other sectors of the domestic economy (IFS line 52S or 32S) include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds.; ; International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.; Weighted average;

Abstract

The 2010 Household Income and Expenditure Survey (HIES) is the second survey of income and expenditure in Vanuatu to provide reliable sub-national estimates, with the 2006 HIES being the first time this was attempted. The first HIES was conducted in 1985 in the two urban centres of Luganville and Port Vila. Another was conducted in 1998 but unfortunately, for a number of reasons to do with errors of estimation and observation, the 1998 HIES did not provide reliable and accurate estimates. With the lessons learnt from past experience, the main objectives for the 2010 survey were to: - Supply monitoring data needed for the then Millennium Challenge Account, Vanuatu (MCA) infrastructure projects; - Supplement the data available for use in compiling official estimates of household accounts in the system of national accounts and subsequent estimates of Gross Domestic Product (GDP); - Obtain expenditure weights and other data for the updating of the basket of items and weights used in the Consumer Price Index (CPI); - Provide data for assessing the impact on household living conditions of existing and proposed economic and social policies and programmes, particularly those resulting in changes in the structure of household expenditure and consumption; and - Gather information on key poverty indicators and statistics for poverty analysis.

Geographic coverage

National coverage.

There are eight main populations of interest for which estimates are required for the 2010 Household Income and Expenditure Survey (HIES): the provincial rural areas of Torba, Sanma, Penama, Malampa, Shefa, Tafea and the urban areas of Luganville and Port Vila. For this reason, the detailed analysis focuses on households from each of the eight sub-populations.

Analysis unit

Households (private) and individuals.

Universe

The survey coverage included only persons living in private households during the survey period (September to November 2006). Persons living in institutions, such as school dormitories, hospital wards, hostels, prisons, as well as those households which had temporarily vacated their dwellings were excluded from the survey. Also excluded from the survey were ex-patriot temporary residents and permanent residents who were not residing (and intending to reside) in Vanuatu for at least 12 months.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling method adopted for the survey was a two-stage approach. The first stage involved the selection of Enumeration Areas (EA) using probability proportional to size (PPS) sampling. The size measure was the number of expected households in the EA, based on 2010 population census estimates. Although it would be desirable to cover all of Vanuatu for this survey, due to cost and time constraints some EAs were excluded from the frame before the selections were made. The impact on sub-population estimates will differ, as some areas have had larger scope reductions.

The second stage of sampling adopted systematic sampling from a list of all households contained in the EA. These lists were produced in the field by enumerators during the first visit to the EA. Once the sample had been selected, a review of where the selections were made was conducted to see how well they covered the projects of interest to the MCA. Approximately 18 enumeration areas (EA) were selected on Efate and 21 between Port Olry and Luganville in Santo, providing good representation of each of the areas. A final sample size of 4,737 households was selected for the survey representing around 10% of the households in Vanuatu.

In order to achieve the required level of accuracy for estimates for the target areas of the six provinces and two urban centres different sample allocations were tested to determine which allocation would produce estimates of similar levels of accuracy for each target area. This sample allocation resulted in the selection of approximately 600 in each province, with the exceptions of Luganville and Torba where less than 600 households were selected. Each of the eight target areas was then further stratified to improve the representation within each of the different area types. Strata were determined by allocating Area Councils to area types based on the Area Council’s accessibility. As a result, 21 strata were used the final sample selection. Sample allocation to each stratum was derived by the proportionate allocation of the population within each “target area”.

Sampling deviation

Owing to cost and time constraints, some remote areas were not considered eligible for selection for the survey. Therefore the scope of the survey was reduced to 82.5 percent of all households in the population. Substantial reductions in scope occurred in Torba (62% in scope) and Malampa (68%) provinces. No enumeration areas were excluded in urban areas. While this may introduce some systematic bias, especially for the areas affected, the reduction of scope is not expected to affect the overall representativeness of the sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developped both in English and Bislama. It is made of 4 forms that are listed below: - Household Control Form (HCF)- was designed to list all the members of households, their date of birth, sex, maritial status relationship to the head of the household; - Household Questionnaire Form - Part 1: Dwelling Characteristics, Access to Transport, Communication, Health, Sanitation and Market Centres, Part II: Household Expenditure, Part III: Income and Production; - Person Questionnaire Form - captures information regarding Demographic, health, education and economic activity for household members; - Household two weeks diaries to collect daily consumption and expenditure.

Cleaning operations

-DATA EDITING: Some initial editing was carried when the forms were coded and prepared for data entry. There were then several strands of editing carried out after the data entry was completed. A set of tables designed to identify missing, illegal or potentially incompatible values in the classificatory data was specified.

The development of the "Generate new records" program, described above, required extensive examination of the data. First, it was sometimes necessary to examine original questionnaires to obtain a better understanding of how households responded to certain questions, especially when the recorded responses were unexpected. Second, the development of some of the imputation functions implemented in the program required analysis of detailed data. Third, testing of the program required examination of data before and after transformation to ensure that the program was carrying out its intended functions. These and other more minor reasons for examining the data collectively also played an important editing function, even though it was unstructured from an editing point of view. Most of the editing actions flowing from this work are recorded in Queries.xls.

Outlier analysis is an important part of the editing process for household surveys. For the Household Income and Expenditure Survey (HIES), formal outlier analysis has largely been confined to examining households with very high income or expenditure. However, outliers were also detected during the processes described in the previous paragraphs.

-IMPUTATIONS: Some obvious errors were fixed and missing data supplied manually at the time of the initial coding and checking of the questionnaires prior to the data entry stage. Similarly changes were made as a result of editing queries described in the previous section. A more automated form of imputation was implemented for certain instances of missing data.

For those transactions recorded in diaries where a quantity was supplied without a value, a value was imputed on the basis of transactions in the same commodity in the same province/urban area. Consideration was given to imputing separately for each transaction type (purchases, own account production, gifts given, gifts received) but there is not sufficient data to use a cross classification of province/urban and transaction type. Examination of differences in unit values between provinces/urban areas and between transaction types showed greater differences between provinces/urban areas than between transaction types. Where there was no required data for a commodity in a particular province/urban area, the unit value from a similar province/urban area was used. Calculations are included in value and quantity by prov city 2.xls. Transaction values imputed in this way are flagged on the file by means of the "data source" variable.

For employees who did not report their gross wages and salaries, a value was imputed on the basis of the average wage/salary of other employees with the same industry and occupation codes and who reported their value. Where there were no other employees in the same category reporting wages/salary, the value for a similar industry and occupation code was used. Calculations are included in supporting file W&S 5.xls. Any imputed values were included in the transaction record for wage and salaries for the household concerned (there is only one aggregate record per household, which combines the wages and salaries of all members of the household). If any component is imputed, the whole transaction is flagged as imputed. However, the imputed value is not included in the PERSON record.

For households that own their own dwelling (including those with a loan or mortgage) but who did not estimate the potential rental value of their dwelling in the household

This scatter chart displays forest area (km²) against central government debt (% of GDP) in Vanuatu. The data is filtered where the date is 2021. The data is about countries per year.

Vanuatu GDP: 2015 Price: USD: Gross Value Added Per Worker: Services data was reported at 0.010 USD mn in 2018. This records a decrease from the previous number of 0.011 USD mn for 2017. Vanuatu GDP: 2015 Price: USD: Gross Value Added Per Worker: Services data is updated yearly, averaging 0.013 USD mn from Dec 2002 (Median) to 2018, with 17 observations. The data reached an all-time high of 0.014 USD mn in 2011 and a record low of 0.010 USD mn in 2018. Vanuatu GDP: 2015 Price: USD: Gross Value Added Per Worker: Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Gross Domestic Product: Real. Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2015 U.S. dollars. Services corresponds to the International Standard Industrial Classification (ISIC) tabulation categories G-P (revision 3) or tabulation categories G-U (revision 4), and includes wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social and personal services.;Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database.;Weighted average;

Vanuatu VU: Monetary Sector Credit to Private Sector: % of GDP data was reported at 53.055 % in 2023. This records a decrease from the previous number of 57.018 % for 2022. Vanuatu VU: Monetary Sector Credit to Private Sector: % of GDP data is updated yearly, averaging 35.960 % from Dec 1979 (Median) to 2023, with 45 observations. The data reached an all-time high of 75.694 % in 2014 and a record low of 27.346 % in 1985. Vanuatu VU: Monetary Sector Credit to Private Sector: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Bank Loans. Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.;International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.;;

Vanuatu VU: Domestic Credit: Provided by Financial Sector: % of GDP data was reported at 57.953 % in 2017. This records a decrease from the previous number of 60.874 % for 2016. Vanuatu VU: Domestic Credit: Provided by Financial Sector: % of GDP data is updated yearly, averaging 35.752 % from Dec 1979 (Median) to 2017, with 39 observations. The data reached an all-time high of 72.107 % in 2014 and a record low of 18.725 % in 1985. Vanuatu VU: Domestic Credit: Provided by Financial Sector: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Bank Loans. Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.; ; International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.; Weighted average;

Vanuatu VU: Claims on Other Sectors of The Domestic Economy: Annual Growth as % of Broad Money data was reported at 3.648 % in 2017. This records an increase from the previous number of 1.316 % for 2016. Vanuatu VU: Claims on Other Sectors of The Domestic Economy: Annual Growth as % of Broad Money data is updated yearly, averaging 4.614 % from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 18.538 % in 2008 and a record low of 1.316 % in 2016. Vanuatu VU: Claims on Other Sectors of The Domestic Economy: Annual Growth as % of Broad Money data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Bank Loans. Claims on other sectors of the domestic economy (IFS line 32S..ZK) include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds.; ; International Monetary Fund, International Financial Statistics and data files.; ;

Vanuatu VU: Net Lending (+) / Net Borrowing (-): % of GDP data was reported at 2.525 % in 2016. This records a decrease from the previous number of 7.718 % for 2015. Vanuatu VU: Net Lending (+) / Net Borrowing (-): % of GDP data is updated yearly, averaging -0.243 % from Dec 1990 (Median) to 2016, with 14 observations. The data reached an all-time high of 7.718 % in 2015 and a record low of -2.504 % in 2010. Vanuatu VU: Net Lending (+) / Net Borrowing (-): % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vanuatu – Table VU.World Bank.WDI: Government Revenue, Expenditure and Finance. Net lending (+) / net borrowing (–) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad.; ; International Monetary Fund, Government Finance Statistics Yearbook and data files.; Weighted average;

国内生产总值：2015年价格：美元：每位劳动者的总附加值：工业在12-01-2018达0.010百万美元，相较于12-01-2017的0.010百万美元有所增长。国内生产总值：2015年价格：美元：每位劳动者的总附加值：工业数据按年更新，12-01-1991至12-01-2018期间平均值为0.011百万美元，共28份观测结果。该数据的历史最高值出现于12-01-1994，达0.020百万美元，而历史最低值则出现于12-01-2014，为0.007百万美元。CEIC提供的国内生产总值：2015年价格：美元：每位劳动者的总附加值：工业数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的瓦努阿图 – Table VU.World Bank.WDI: Gross Domestic Product: Real。

VU：国内生产总值：2010年价格：美元：每位劳动者的总附加值：工业在12-01-2014达0.009百万美元，相较于12-01-2013的0.010百万美元有所下降。VU：国内生产总值：2010年价格：美元：每位劳动者的总附加值：工业数据按年更新，12-01-1991至12-01-2014期间平均值为0.012百万美元，共24份观测结果。该数据的历史最高值出现于12-01-1991，达0.017百万美元，而历史最低值则出现于12-01-2007，为0.009百万美元。CEIC提供的VU：国内生产总值：2010年价格：美元：每位劳动者的总附加值：工业数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的瓦努阿图 – 表 VU.世界银行：国内生产总值：实际。