Household Income and Expenditure Survey 2006
Vanuatu, 2006
Vanuatu Natioanl Statistics Offie
Identification
IDNO
SPC_VUT_2006_HIES_v01_M
Title
Household Income and Expenditure Survey 2006
Translated Title
Vanuatu Sevei blong Haoshol Inkam moa Expendija 2006
Country
| Name | Country code |
|---|---|
| Vanuatu | VUT |
Abstract
The 2006 Household Income and Expenditure Survey (2006 HIES) was initiated by Vanuatu National Statistical Office (VNSO) to review its income and expenditure patterns for the national accounts system, to update the Consumer Price Index (CPI) and subsequently revise its Gross Domestic Products (GDP).
Although the 2006 HIES is primarily designed to satisfy the data requirements of the Vanuatu NSO, it is also expected to provide benchmark data for the Millennium Challenge Accounts' (MCA's) infrastructure projects for its impact assessment on the rural economy.
The main objectives of the survey are:
(a) To obtain expenditure weights and other useful data for the up-dating of the basket and weight of the CPI;
(b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts;
(c) To supply benchmark data needed for assessment for MCA infrastructure projects;
(d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption;
(e) To supply basic data needed for policy making in connection with social and economic planning; and
(f) To gather information on poverty lines and incidence of poverty for determining nutritional level of people.
Although the 2006 HIES is primarily designed to satisfy the data requirements of the Vanuatu NSO, it is also expected to provide benchmark data for the Millennium Challenge Accounts' (MCA's) infrastructure projects for its impact assessment on the rural economy.
The main objectives of the survey are:
(a) To obtain expenditure weights and other useful data for the up-dating of the basket and weight of the CPI;
(b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts;
(c) To supply benchmark data needed for assessment for MCA infrastructure projects;
(d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption;
(e) To supply basic data needed for policy making in connection with social and economic planning; and
(f) To gather information on poverty lines and incidence of poverty for determining nutritional level of people.
Kind of Data
Sample survey data [ssd]
Version
Version number
Version 2.0 - Public use file: Cleaned and anonymized.
Version Date
2010-07-05
Scope
Topic Classification
| Topic | Vocabulary | URI |
|---|---|---|
| Income | ||
| Expenditure | ||
| consumption/consumer behaviour [1.1] | CESSDA | http://www.nesstar.org/rdf/common |
| income, property and investment/saving [1.5] | CESSDA | http://www.nesstar.org/rdf/common |
| economic conditions and indicators [1.2] | CESSDA | http://www.nesstar.org/rdf/common |
| employment [3.1] | CESSDA | http://www.nesstar.org/rdf/common |
| unemployment [3.5] | CESSDA | http://www.nesstar.org/rdf/common |
| health care and medical treatment [8.5] | CESSDA | http://www.nesstar.org/rdf/common |
| specific diseases and medical conditions [8.9] | CESSDA | http://www.nesstar.org/rdf/common |
| general health [8.4] | CESSDA | http://www.nesstar.org/rdf/common |
Coverage
Geographic Coverage
There are eight main populations of interest for which estimates are required for the 2006 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. Based on the 2006 Agricultural Census, 78 percent of the households are located in rural areas and 22 percent in urban areas.
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.
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.
Unit of Analysis
Private Household, individuals and expenditure items
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-patriates, temporary residents and permanent residents who were not residing (and intending to reside) in Vanuatu for at least 12 months.
Producers and sponsors
Authoring entity/Primary investigators
| Agency Name | Affiliation |
|---|---|
| Vanuatu Natioanl Statistics Offie | Vanuatu Government |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Secretariat of the Pacific Community | Regional Organization | Technical Assistant |
| Fiji Bureau of Statistics (FIBOS) | Fiji Government | Technical Assistant (Dat Processing) |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| AUSAID | AUSAID | Funding |
| Milllanium Challenge Corperation | MCC | Funding |
| Vanuatu Government | VANGOV | Funding |
Other Identifications/Acknowledgments
| Name | Affiliation | Role |
|---|---|---|
| Mr. Leon Pietsch | Private Consultant (Australia) | Technical Assistant |
Sampling
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 2006 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. A total of nine additional EAs were selected to better cover some of the project areas which were not suitably represented by the original sample. A sample size of 4,532 households was adopted for the survey representing around 10 percent of the total households in Vanuatu.
Eight target areas were identified as sub-populations for which estimates would be desirable. These eight areas included the six provinces with separate target areas for the urban centres of Port Vila and Luganville. In order to achieve the required level of accuracy, different sample allocations were produced to determine which allocation would produce estimates of similar level accuracy for each target area. The sample allocation resulted in approximately 600 households selected for each province, except for Luganville and Torba where less than 500 households were selected.
Within each target area, further stratification was adopted in order to enhance suitable 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 created for the final sample. Sample allocation to each stratum was performed by allocating proportionally to the population within each “target area”. The sample weights were calculated for each stratum separately and were adjusted for non-response and benchmarked against household counts from the 2006 agricultural census.
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. A total of nine additional EAs were selected to better cover some of the project areas which were not suitably represented by the original sample. A sample size of 4,532 households was adopted for the survey representing around 10 percent of the total households in Vanuatu.
Eight target areas were identified as sub-populations for which estimates would be desirable. These eight areas included the six provinces with separate target areas for the urban centres of Port Vila and Luganville. In order to achieve the required level of accuracy, different sample allocations were produced to determine which allocation would produce estimates of similar level accuracy for each target area. The sample allocation resulted in approximately 600 households selected for each province, except for Luganville and Torba where less than 500 households were selected.
Within each target area, further stratification was adopted in order to enhance suitable 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 created for the final sample. Sample allocation to each stratum was performed by allocating proportionally to the population within each “target area”. The sample weights were calculated for each stratum separately and were adjusted for non-response and benchmarked against household counts from the 2006 agricultural census.
Deviations from the Sample Design
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 estimated number of households removed from scope of the survey, with the percentage remaining, can be found in the table below:
The survey was conducted over a three month period beginning in the first week of September and finishing at the end of November 2006. (Some EAs that had not been enumerated as planned were later enumerated in December).
The survey was conducted over a three month period beginning in the first week of September and finishing at the end of November 2006. (Some EAs that had not been enumerated as planned were later enumerated in December).
Response Rate
A total of 3884 out of 4590 selected households fully responded to the survey, representing an overall response rate of 84.6 percent (refer table 3.1). Only 4 percent did not fully respond or provided inadequate information to be included in the survey. However 11.4 percent of households were reported as vacant dwellings which, most probably, include households that could not be contacted during the survey period.
Lower responses rates were reported for Port Vila (69%) and the rest of Shefa (76%) than in the other surveyed provinces. This was largely due to inadequate enumeration in Shefa province. However, apart from these areas the overall response rate indicated a high level of response especially in the provincial rural areas.
Lower responses rates were reported for Port Vila (69%) and the rest of Shefa (76%) than in the other surveyed provinces. This was largely due to inadequate enumeration in Shefa province. However, apart from these areas the overall response rate indicated a high level of response especially in the provincial rural areas.
Weighting
The sample weights were calculated for each stratum separately, adjusted for non-response, and benchmarked against household counts from the 2006 Agricultural Census
Data Collection
Dates of Data Collection (YYYY/MM/DD)
| Start date | End date | Cycle |
|---|---|---|
| 2006-09-01 | 2006-11-07 | Listing of household for the first week of the month |
| 2006-11-15 | 2006-11-30 | Enumeration in the second week of the month |
| 2006-09-15 | 2006-11-30 | Diary for the last two weeks of the month |
Mode of data collection
Face-to-face [f2f]
Supervision
The National Statistics Office recruited and trained six provincial coordinators, 30 supervisors and 118 interviewers to conduct the survey in the six provincial areas and two urban centres. Over the survey period each interviewer completed three workloads of about 45 households in total.
Enumerators were asked to report to their supervisors if finding difficulties. If the one workload is completed then the supervisor should check. If the forms are not completed well then should give back to the enumerators to check again. Once the forms are completed the supervisor make the final check and sign it off and send to Provincial Cordinators check again. If there is still problems then the cordinator has to return the form back to the supevisor. Once this is done then the cordintaor will sign off the form then send to the head office. The office will then check if there are problems then will ask for clarification from cordinator and supervisor. The cordinators and supervisors were asked to visit their enumerators once a week .
The enumerators and supervisors were given impress, maps, list of all the villages in the EA and the selected households. Also they were given separate manual for enumerators and separate for the supervisor with questionnairs according to the number of households selected in the EAs.
Enumerators were asked to report to their supervisors if finding difficulties. If the one workload is completed then the supervisor should check. If the forms are not completed well then should give back to the enumerators to check again. Once the forms are completed the supervisor make the final check and sign it off and send to Provincial Cordinators check again. If there is still problems then the cordinator has to return the form back to the supevisor. Once this is done then the cordintaor will sign off the form then send to the head office. The office will then check if there are problems then will ask for clarification from cordinator and supervisor. The cordinators and supervisors were asked to visit their enumerators once a week .
The enumerators and supervisors were given impress, maps, list of all the villages in the EA and the selected households. Also they were given separate manual for enumerators and separate for the supervisor with questionnairs according to the number of households selected in the EAs.
Type of Research Instrument
- Household Control Form (HCF)- was designed to list all the members of households, their date of birth, sex, maritial status relationship to the head and typ of activity the person is involved in
- 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 health, education and economic activity for individual perosn
- Diary - Captures information regarding items bought, consumption of items, gifts and winnings from betting, riffles and lotteries by 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 health, education and economic activity for individual perosn
- Diary - Captures information regarding items bought, consumption of items, gifts and winnings from betting, riffles and lotteries by household
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Vanuatu National Statistics Office-Enumerators | VNSO | Vanuatu Government |
Data Processing
Cleaning Operations
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 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 questionnaire, a value was imputed on the basis of the average for other dwellings in the same enumeration area. Potential rental values are not included in aggregate income or expenditure and therefore there are no transaction records for these values. Rather they are stored in the dwelling characteristic and tenure records. The imputed values are NOT flagged as imputed (because these values are not recorded as transactions, and no other record structure had been set up to record the values as imputed). Calculations of imputed values are included in dwelling tenure.xls, which can also be used to identify the households for which values were imputed.
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 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 questionnaire, a value was imputed on the basis of the average for other dwellings in the same enumeration area. Potential rental values are not included in aggregate income or expenditure and therefore there are no transaction records for these values. Rather they are stored in the dwelling characteristic and tenure records. The imputed values are NOT flagged as imputed (because these values are not recorded as transactions, and no other record structure had been set up to record the values as imputed). Calculations of imputed values are included in dwelling tenure.xls, which can also be used to identify the households for which values were imputed.
Other Processing
The data entry system was developed in CSPro, which requires the captured data to be stored in a complex record structur.. In essence, the data from each page of each questionnaire is stored in a separate record, and each of those records has to have all its variables unique to itself.
13 Pc's were used for data entry with 13 data entry operators three males and 10 females. A program was developed to accommodate first and second entry (Double entry).
13 Pc's were used for data entry with 13 data entry operators three males and 10 females. A program was developed to accommodate first and second entry (Double entry).
Data access
Contact
| Name | Affiliation | |
|---|---|---|
| Vanuatu Statistics Office-Government Statistician | Vanuatu Government | [email protected] |
Confidentiality Declaration
Statistical Act cap 1983
Conditions
The Act only specify the confidentiality of information but do not mention anything about public accessibility. Currently the Statistical Act is under review and still under review for the past five years.
Citation requirement
Vanuatu National Statistics Office
Disclaimer and copyrights
Disclaimer
The Act only specify the confidentiality of information but do not mention anything about public accessibility. Currently the Statistical Act is under review
Copyright
National Statistics Office, 2006 HIES
Contacts
Contact
| Name | Affiliation | URI | |
|---|---|---|---|
| Vanuatu Government Statistician | National Statistics Office | [email protected] | http://www.vanuatustats.com |
Metadata production
Document ID
DDI_SPC_VUT_2006_HIES_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Harry Nalau | HN | Vanuatu National Statistics Office | Documentation of the study |
| Rara Soro | RS | Vanuatu National Statistics Office | Documentation of the study |
Date of Production
2010-07-05
Document version
Version 01 - first documentation for the VHIES2006.