Household Income and Expenditure Survey 2006
Nauru, 2006
Nauru Bureau of Statistics
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January 20, 2020
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January 20, 2020
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Identification
Survey ID Number
SPC_NRU_2006_HIES_v01_M_v01_A_PUF
Title
Household Income and Expenditure Survey 2006
Country
| Name | Country code |
|---|---|
| Nauru | NRU |
Study type
Income/Expenditure/Household Survey [hh/ies]
Series Information
Whilst income and expenditure information was collected from a random sample of households in the mid 1990's, this information was not fully exploited and was mainly utilised to update base salaries for the public service. It would therefore be safe to say that the 2006 Household Income and Expenditure Survey (HIES) was the first official HIES conducted in Nauru.
Abstract
The survey was conducted during December 2006, following an initial mini census listing exercise which was conducted about two months earlier in late September 2006.
The objectives of the HIES were as follows:
a) Provide information on income and expenditure distribution within the population;
b) Provide income estimates of the household sector for the national accounts;
c) Provide data for the re-base on the consumer price index;
d) Provide data for the analysis of poverty and hardship.
The objectives of the HIES were as follows:
a) Provide information on income and expenditure distribution within the population;
b) Provide income estimates of the household sector for the national accounts;
c) Provide data for the re-base on the consumer price index;
d) Provide data for the analysis of poverty and hardship.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household;
- Individual.
- Individual.
Version
Version Description
Version 01: Cleaned, labelled and anonymized version of the Master file.
Version Date
2020-01-17
Scope
Notes
-HOUSEHOLD: Dwellin characteristics, Lighting, Water access, Sanitation, Dwelling tenure, Agricutlural assets, Expenditure, Income.
-INDIVIDUAL: Individual characteristics, Health, Education, Labour force, Income, Allowances.
-INDIVIDUAL: Individual characteristics, Health, Education, Labour force, Income, Allowances.
Topics
| Topic | Vocabulary |
|---|---|
| Poverty | World Bank |
| Income & Expenditure | World Bank |
Keywords
| Keyword |
|---|
| Nauru |
| Expenditure |
| Income |
| Household |
| CPI |
| Sanitation |
| Health |
| Education |
| Economic activity |
Coverage
Geographic Coverage
National coverage: whole island was covered for the survey.
Universe
The survey covered all private households on the island of Nauru. When the survey was in the field, interviewers were further required to reduce the scope by removing those households which had not been residing in Nauru for the last 12 months and did not intend to stay in Nauru for the next 12 months.
Persons living in special dwellings (Hospital, Prison, etc) were not included in the survey.
Persons living in special dwellings (Hospital, Prison, etc) were not included in the survey.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Nauru Bureau of Statistics | Ministry of Finance |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Pacific Community | CROP Regional Organisation | Technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Australian Agency for International Development | AusAID | Financial assistance |
| Asian Development Bank | ADB | Financial assistance |
Sampling
Sampling Procedure
The sample size adopted for the survey was 500 households which allowed for expected sample loss, whilst still maintaining a suitable responding sample for the analysis.
Before the sample was selected, the population was stratified by constituency in order to assist with the logistical issues associated with the fieldwork. There were eight constituencies in total, along with "Location" which stretches across the districts of Denigamodu and Aiwo, forming nine strata in total. Although constituency level analysis was not a priority for the survey, sample sizes within each stratum were kept to a minimum of 40 households, to enable some basic forms of analysis at this level if required.
The sample selection procedure within each stratum was then to sort each household on the frame by household size (number of people), and then run a systematic skip through the list in order to achieve the desirable sample size.
Before the sample was selected, the population was stratified by constituency in order to assist with the logistical issues associated with the fieldwork. There were eight constituencies in total, along with "Location" which stretches across the districts of Denigamodu and Aiwo, forming nine strata in total. Although constituency level analysis was not a priority for the survey, sample sizes within each stratum were kept to a minimum of 40 households, to enable some basic forms of analysis at this level if required.
The sample selection procedure within each stratum was then to sort each household on the frame by household size (number of people), and then run a systematic skip through the list in order to achieve the desirable sample size.
Deviations from the Sample Design
No deviations from the sample design took place.
Response Rate
The survey response rates were a lot lower than expected, especially in some districts. The district of Aiwo, Uaboe and Denigomodu had the lowest response rates with 16.7%, 20.0% and 34.8% respectively. The area of Location was also extremely low with a responses rate of 32.2%. On a more positive note, the districts of Yaren, Ewa, Anabar, Ijuw and Anibare all had response rates at 80.0% or better.
The major contributing factor to the low response rates were households refusing to take part in the survey. The figures for responding above only include fully responding households, and given there were many partial responses, this also brought the values down. The other significant contributing factor to the low response rates was the interviewers not being able to make contact with the household during the survey period.
Unfortunately, not only do low response rates often increase the sampling error of the survey estimates, because the final sample is smaller, it will also introduce response bias into the final estimates. Response bias takes place when the households responding to the survey possess different characteristics to the households not responding, thus generating different results to what would have been achieved if all selected households responded. It is extremely difficult to measure the impact of the non-response bias, as little information is generally known about the non-responding households in the survey. For the Nauru 2006 HIES however, it was noted during the fieldwork that a higher proportion of the Chinese population residing in Nauru were more likely to not respond. Given it is expected their income and expenditure patterns would differ from the rest of the population, this would contribute to the magnitude of the bias.
Below is the list of all response rates by district:
-Yaren: 80.5%
-Boe: 70%
-Aiwo: 16.7%
-Buada: 62.5%
-Denigomodu: 34.8%
-Nibok: 68.4%
-Uaboe: 20%
-Baitsi: 47.8%
-Ewa: 80%
-Anetan: 76.5%
-Anabar: 81.8%
-Ijuw: 85.7%
-Anibare: 80%
-Meneng: 64.3%
-Location: 32.2%
-TOTAL: 54.4%
The major contributing factor to the low response rates were households refusing to take part in the survey. The figures for responding above only include fully responding households, and given there were many partial responses, this also brought the values down. The other significant contributing factor to the low response rates was the interviewers not being able to make contact with the household during the survey period.
Unfortunately, not only do low response rates often increase the sampling error of the survey estimates, because the final sample is smaller, it will also introduce response bias into the final estimates. Response bias takes place when the households responding to the survey possess different characteristics to the households not responding, thus generating different results to what would have been achieved if all selected households responded. It is extremely difficult to measure the impact of the non-response bias, as little information is generally known about the non-responding households in the survey. For the Nauru 2006 HIES however, it was noted during the fieldwork that a higher proportion of the Chinese population residing in Nauru were more likely to not respond. Given it is expected their income and expenditure patterns would differ from the rest of the population, this would contribute to the magnitude of the bias.
Below is the list of all response rates by district:
-Yaren: 80.5%
-Boe: 70%
-Aiwo: 16.7%
-Buada: 62.5%
-Denigomodu: 34.8%
-Nibok: 68.4%
-Uaboe: 20%
-Baitsi: 47.8%
-Ewa: 80%
-Anetan: 76.5%
-Anabar: 81.8%
-Ijuw: 85.7%
-Anibare: 80%
-Meneng: 64.3%
-Location: 32.2%
-TOTAL: 54.4%
Weighting
Household weights for the analysis were derived by dividing the known population of households from the sample frame for each stratum, by the responding sample for those strata.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2006-12-01 | 2006-12-30 | Fieldwork |
Data Collection Mode
Face-to-face [f2f]
Supervision
The supervision of field work took place at 2 levels:
-Overall Supervision: 3 staff from the Nauru Bureau of Statistics oversaw the work of the 9 field supervisors. They mainly remained in the office but made some field visits at times to monitor field progress;
-Field Supervisers: 9 field supervisors were hired to provide the immediate supervision to 37 field interviewers. There tasks were to check all survey materials before it was returned to the Bureau of Statistics and answer any queries that the interviewers may have during data collection.
-Overall Supervision: 3 staff from the Nauru Bureau of Statistics oversaw the work of the 9 field supervisors. They mainly remained in the office but made some field visits at times to monitor field progress;
-Field Supervisers: 9 field supervisors were hired to provide the immediate supervision to 37 field interviewers. There tasks were to check all survey materials before it was returned to the Bureau of Statistics and answer any queries that the interviewers may have during data collection.
Data Collection Notes
The staff involved in the survey comprised of the three permanent staff of the National Statistics Office (NSO), nine field supervisors and 37 field enumerators. Each interviewer was allocated between 10 and 12 households each to enumerate over the two week period. For the expenditure questionnaire, income questionnaire and miscellaneous questionnaire, a face-to-face interview was conducted with the household to capture the information. For the two diaries, the diary was left with the household who were then responsible for recording all their expenditures over the two-week diary keeping period.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Nauru Bureau of Statistics | NBOS | Ministry of Finance |
Questionnaires
Questionnaires
The survey schedules adopted for the Household Income and Expenditure Survey (HIES) included the following:
· Expenditure questionnaire;
· Income questionnaire;
· Miscellaneous questionnaire;
· Diary (x2).
Whilst a Household Control Form collecting basic demographics is also normally included with the survey, this wasn't required for this HIES as this activity took place for all households in the mini census.
Information collected in the four schedules covered the following:
-Expenditure questionnaire: Covers basic details about the dwelling structure and its access to things like water and sanitation. It was also used as the vehicle to collect expenditure on major and infrequent expenditures incurred by the household.
-Income questionnaire: Covers each of the main types of household income generated by the household such as wages and salaries, business income and income from subsistence activities.
-Miscellaneous questionnaire: Covers topics relating to health access, labour force status and education.
-Diary: Covers all day to day expenditures incurred by the household, consumption of items produced by the household such as fish and crops, and gifts both received and given by the household.
All questionnaires are provided as External Resources.
· Expenditure questionnaire;
· Income questionnaire;
· Miscellaneous questionnaire;
· Diary (x2).
Whilst a Household Control Form collecting basic demographics is also normally included with the survey, this wasn't required for this HIES as this activity took place for all households in the mini census.
Information collected in the four schedules covered the following:
-Expenditure questionnaire: Covers basic details about the dwelling structure and its access to things like water and sanitation. It was also used as the vehicle to collect expenditure on major and infrequent expenditures incurred by the household.
-Income questionnaire: Covers each of the main types of household income generated by the household such as wages and salaries, business income and income from subsistence activities.
-Miscellaneous questionnaire: Covers topics relating to health access, labour force status and education.
-Diary: Covers all day to day expenditures incurred by the household, consumption of items produced by the household such as fish and crops, and gifts both received and given by the household.
All questionnaires are provided as External Resources.
Data Processing
Data Editing
There were 3 phases to the editing process for the 2006 Household Income and Expenditure Survey (HIES) of Nauru which included:
1. Data Verification operations;
2. Data Editing operations;
3. Data Auditing operations.
The software used for data editting is CSPro 3.0.
After each batch is completed the supervisor should check that all person details have been entered from the household listing form (HCF) and should review the income and expenditure questionnaires for each batch ensuring that all items have been entered correctly. Any omitted or incorrect items should be entered into the system.
The supervisor is required to perform outlier checks (large or small values) on the batched diary data by calculating unit price (amount/quantity) and comparing prices for each item. This is to be conducted by loading the data into Excel files and sorting data by unit price for each item. Any changes to prices or quantities will be made on the batch file.
For more information on what each phase entailed go the document HIES Processing Instructions attached to this documentation.
1. Data Verification operations;
2. Data Editing operations;
3. Data Auditing operations.
The software used for data editting is CSPro 3.0.
After each batch is completed the supervisor should check that all person details have been entered from the household listing form (HCF) and should review the income and expenditure questionnaires for each batch ensuring that all items have been entered correctly. Any omitted or incorrect items should be entered into the system.
The supervisor is required to perform outlier checks (large or small values) on the batched diary data by calculating unit price (amount/quantity) and comparing prices for each item. This is to be conducted by loading the data into Excel files and sorting data by unit price for each item. Any changes to prices or quantities will be made on the batch file.
For more information on what each phase entailed go the document HIES Processing Instructions attached to this documentation.
Other Processing
Key aspects of the Data Processing system were undertaken as follows:
1. Application Files
The data entry applications have been developed with CSPRO 3.0. There are 3 separate data entry applications corresponding to the 3 Household Income and Expenditure Survey (HIES) questionnaires (HCF + Income questionnaire are processed together).
a. HIES 2006 Income
b. HIES 2006 Expenditure
c. HIES 2006 Diary
Each application comprises three modules: a data dictionary, a forms file, and an application file. The data dictionary specifies all records and fields in the data file as well as the value ranges. The forms file specifies the layout and order of the data entry forms. The application file specifies the editing logic, including skips, lookup lists, nil and large values, and consistency checks.
The applications are stored on the data entry PCs in the folder: c:\HIES\DEAPP. The applications are selected via the HIES .pff icon on each PC. Each PC will have a different icon depending on the type of questionnaire being processed.
2. Data Files
Before data entry the default data file is replaced with the filename of the current batch, where each EA is a batch. Use the following filename templates 'QQeano.dat' where QQ = questionnaire type, eano = EA.number.
e.g IN0101.dat - Income, EX0101.dat - Expenditure, DY0101.dat - Diaries
3. Coding Operations
The HIES field staff are responsible for the coding of the household diaries which need to be coded according to the item code list. The coding of the diaries need to be completed for each batch before sending to the data entry operators.
For missing amounts for expenditure items listed in the diaries an estimate needs to be made of the costs of item. Refer to other diaries in the batch to estimate the local cost of the item. Enter the estimated cost in red pen.
For large expenditure items listed in the diaries a check should be made to ensure that the items are captured in the expenditure questionnaire. If not they should be added.
As the other questionnaires are mostly pre-coded, the data entry supervisor will code the income and expenditure questionnaires (especially for occupation & industry).
The data entry supervisor separates the batches into the three questionnaire types for data entry, and then returns them to their original envelopes for storage after the structural checks have been performed.
4. Data Entry operations
The data entry supervisor is responsible for ensuring the data is entered into the system according to the instructions in the HIES data entry manual. The data entry operators will be trained by the supervisor to follow the data entry procedures, including the re-checking of entered data. The operators are supervised during data entry operations and issues raised by them dealt with by the supervisor promptly.
As there more than 500 households to be processed over a period of two months, each operator is to complete a minimum of 10 households per day. Each batch should be completed before the operator takes a tea/lunch break or finishes for the day. The supervisor should monitor the work of the operators and take action to ensure the timetable is maintained.
To assess the level of accuracy during data entry, every tenth batch should be verified by re-entering the batch again preferably by a different operator. The batch should be named with a .ver suffix to distinguish it from the original file. The original and verified batches should be compared and a mismatch statistic produced and recorded.
1. Application Files
The data entry applications have been developed with CSPRO 3.0. There are 3 separate data entry applications corresponding to the 3 Household Income and Expenditure Survey (HIES) questionnaires (HCF + Income questionnaire are processed together).
a. HIES 2006 Income
b. HIES 2006 Expenditure
c. HIES 2006 Diary
Each application comprises three modules: a data dictionary, a forms file, and an application file. The data dictionary specifies all records and fields in the data file as well as the value ranges. The forms file specifies the layout and order of the data entry forms. The application file specifies the editing logic, including skips, lookup lists, nil and large values, and consistency checks.
The applications are stored on the data entry PCs in the folder: c:\HIES\DEAPP. The applications are selected via the HIES .pff icon on each PC. Each PC will have a different icon depending on the type of questionnaire being processed.
2. Data Files
Before data entry the default data file is replaced with the filename of the current batch, where each EA is a batch. Use the following filename templates 'QQeano.dat' where QQ = questionnaire type, eano = EA.number.
e.g IN0101.dat - Income, EX0101.dat - Expenditure, DY0101.dat - Diaries
3. Coding Operations
The HIES field staff are responsible for the coding of the household diaries which need to be coded according to the item code list. The coding of the diaries need to be completed for each batch before sending to the data entry operators.
For missing amounts for expenditure items listed in the diaries an estimate needs to be made of the costs of item. Refer to other diaries in the batch to estimate the local cost of the item. Enter the estimated cost in red pen.
For large expenditure items listed in the diaries a check should be made to ensure that the items are captured in the expenditure questionnaire. If not they should be added.
As the other questionnaires are mostly pre-coded, the data entry supervisor will code the income and expenditure questionnaires (especially for occupation & industry).
The data entry supervisor separates the batches into the three questionnaire types for data entry, and then returns them to their original envelopes for storage after the structural checks have been performed.
4. Data Entry operations
The data entry supervisor is responsible for ensuring the data is entered into the system according to the instructions in the HIES data entry manual. The data entry operators will be trained by the supervisor to follow the data entry procedures, including the re-checking of entered data. The operators are supervised during data entry operations and issues raised by them dealt with by the supervisor promptly.
As there more than 500 households to be processed over a period of two months, each operator is to complete a minimum of 10 households per day. Each batch should be completed before the operator takes a tea/lunch break or finishes for the day. The supervisor should monitor the work of the operators and take action to ensure the timetable is maintained.
To assess the level of accuracy during data entry, every tenth batch should be verified by re-entering the batch again preferably by a different operator. The batch should be named with a .ver suffix to distinguish it from the original file. The original and verified batches should be compared and a mismatch statistic produced and recorded.
Data Appraisal
Estimates of Sampling Error
To determine the impact of sampling error on the survey results, relative standard errors (RSEs) for key estimates were produced. When interpreting these results, one must remember that these figures don't include any of the non-sampling errors discussed in other sections of this documentation
To also provide a rough guide on how to interpret the RSEs provided in the main report, the following information can be used:
Category Description
RSE < 5% Estimate can be regarded as very reliable
5% < RSE < 10% Estimate can be regarded as good and usable
10% < RSE < 25% Estimate can be considered usable, with caution
RSE > 25% Estimate should only be used with extreme caution
The actual RSEs for the key estimates can be found in Section 4.1 of the main report
As can be seen from these tables, the estimates for Total Income and Total Expenditure from the Household Income and Expenditure Survey (HIES) can be considered to be very good, from a sampling error perspective. The same can also be said for the Wage and Salary estimate in income and the Food estimate in expenditure, which make up a high proportion of each respective group.
Many of the other estimates should be used with caution, depending on the magnitude of their RSE. Some of these high RSEs are to be expected, due to the expected degree of variability for how households would report for these items. For example, with Business Income (RSE 56.8%), most households would report no business income as no household members undertook this activity, whereas other households would report large business incomes as it's their main source of income.
To also provide a rough guide on how to interpret the RSEs provided in the main report, the following information can be used:
Category Description
RSE < 5% Estimate can be regarded as very reliable
5% < RSE < 10% Estimate can be regarded as good and usable
10% < RSE < 25% Estimate can be considered usable, with caution
RSE > 25% Estimate should only be used with extreme caution
The actual RSEs for the key estimates can be found in Section 4.1 of the main report
As can be seen from these tables, the estimates for Total Income and Total Expenditure from the Household Income and Expenditure Survey (HIES) can be considered to be very good, from a sampling error perspective. The same can also be said for the Wage and Salary estimate in income and the Food estimate in expenditure, which make up a high proportion of each respective group.
Many of the other estimates should be used with caution, depending on the magnitude of their RSE. Some of these high RSEs are to be expected, due to the expected degree of variability for how households would report for these items. For example, with Business Income (RSE 56.8%), most households would report no business income as no household members undertook this activity, whereas other households would report large business incomes as it's their main source of income.
Data Appraisal
Other than the non-response issues discussed in this documentation, other quality issues were identified which included:
1) Reporting errors
Some of the different aspects contributing to the reporting errors generated from the survey, with some examples/explanations for each, include the following:
a) Misinterpretation of survey questions: A common mistake which takes place when conducting a survey is that the person responding to the questionnaire may interpret a question differently to the interviewer, who in turn may have interpreted the question differently to the people who designed the questionnaire. Some examples of this for a Household Income and Expenditure Survey (HIES) can include people providing answers in dollars and cents, instead of just dollars, or the reference/recall period for an “income” or “expenditure” is misunderstood. These errors can often see reported amounts out by a factor of 10 or even 100, which can have major impacts on final results.
b) Recall problems for the questionnaire information: The majority of questions in both of the income and expenditure questionnaires require the respondent to recall what took place over a 12 month period. As would be expected, people will often forget what took place up to 12 months ago so some information will be forgotten.
c) Intentional under-reporting for some items: For whatever reasons, a household may still participate in a survey but not be willing to provide accurate responses for some questions. Examples for a HIES include people not fully disclosing their total income, and intentionally under-reporting expenditures on items such as alcohol and tobacco.
d) Accidental under-reporting in the household diaries: Although the two diaries are left with the household for a period of two weeks, it is easy for the household to forget to enter all expenditures throughout this period - this problem most likely increases as the two week period progresses. It is also expected that for section 2 in the diary which collects consumption of home produce by the household, the extent of under-reporting will potentially be even higher.
2) Data entry errors
Despite best efforts to keep reporting errors to a minimum, errors can also occur during the data entry phase of the survey. Once again amounts reported as dollars and cents can get entered as whole dollars, and accidental keying mistakes can be a common occurrence. Data entry range checks are often used to keep these mistakes to a minimum, and naturally data editing takes place both during and after data entry, but errors still occur which go undetected.
1) Reporting errors
Some of the different aspects contributing to the reporting errors generated from the survey, with some examples/explanations for each, include the following:
a) Misinterpretation of survey questions: A common mistake which takes place when conducting a survey is that the person responding to the questionnaire may interpret a question differently to the interviewer, who in turn may have interpreted the question differently to the people who designed the questionnaire. Some examples of this for a Household Income and Expenditure Survey (HIES) can include people providing answers in dollars and cents, instead of just dollars, or the reference/recall period for an “income” or “expenditure” is misunderstood. These errors can often see reported amounts out by a factor of 10 or even 100, which can have major impacts on final results.
b) Recall problems for the questionnaire information: The majority of questions in both of the income and expenditure questionnaires require the respondent to recall what took place over a 12 month period. As would be expected, people will often forget what took place up to 12 months ago so some information will be forgotten.
c) Intentional under-reporting for some items: For whatever reasons, a household may still participate in a survey but not be willing to provide accurate responses for some questions. Examples for a HIES include people not fully disclosing their total income, and intentionally under-reporting expenditures on items such as alcohol and tobacco.
d) Accidental under-reporting in the household diaries: Although the two diaries are left with the household for a period of two weeks, it is easy for the household to forget to enter all expenditures throughout this period - this problem most likely increases as the two week period progresses. It is also expected that for section 2 in the diary which collects consumption of home produce by the household, the extent of under-reporting will potentially be even higher.
2) Data entry errors
Despite best efforts to keep reporting errors to a minimum, errors can also occur during the data entry phase of the survey. Once again amounts reported as dollars and cents can get entered as whole dollars, and accidental keying mistakes can be a common occurrence. Data entry range checks are often used to keep these mistakes to a minimum, and naturally data editing takes place both during and after data entry, but errors still occur which go undetected.
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Director of Statisitics | Nauru Bureau of Statistics (NBOS) | [email protected] | http://www.spc.int/prism/country/nr/stats/ |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Director of Statistics | Nauru Bureau of Statistics (NBOS) | [email protected] | https://nauru.prism.spc.int/our-contacts |
Access conditions
Licensed datasets, accessible under conditions.
Citation requirements
"Nauru Bureau of Statistics, Household Income and Expenditure Survey 2006 (HIES 2006), Version 01 of the licensed dataset (January 2020), provided by the Microdata Library. https://microdata.pacificdata.org/index.php/home"
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_SPC_NRU_2006_HIES_v01_M_v01_A_PUF
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Chris Ryan | CMR | SPC - Statistics for Development | Documentation of the survey |
| Accelerated Data Program | ADP | International Household Survey Network | Editing for the IHSN Survey Catalog |
| Statisticis for Devlopment Division | SDD | Pacific Community | Review of the existing documentation |
Date of Metadata Production
2012-07-16
DDI Document version
Version 01 (July 2012): First documentation of HIES 2006 using the IHSN Toolkit. This is the edited version of the documentation produced during the July 2012 workshop in Guam.
Version 02 (January 2020): Review of the first documentation, but revised as this is now the anonymized version of the master file. Done at Noumea, New Caledonia by the Pacific Community (SPC).
Version 02 (January 2020): Review of the first documentation, but revised as this is now the anonymized version of the master file. Done at Noumea, New Caledonia by the Pacific Community (SPC).