Household Income and Expenditure Survey 2012-2013
Nauru, 2012 - 2013
Nauru Bureau of Statistics
Identification
IDNO
DDI_SPC_NRU_2012_HIES_v01_M_v01_A_PUF
Title
Household Income and Expenditure Survey 2012-2013
Country
| Name | Country code |
|---|---|
| Nauru | NRU |
Abstract
Extracted from the Field Work Instruction Manual NRU HIES 2013 (Attached):
1.2 Objectives
The purpose of the HIES survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Nauru. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people.
Some more specific outputs from the survey are listed below:
a) To obtain expenditure weights and other useful data for the revision of the consumer price index;
b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts;
c) To supply basic data needed for policy making in connection with social and economic planning;
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 gather information on poverty lines and incidence of poverty throughout Nauru.
1.2 Objectives
The purpose of the HIES survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Nauru. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people.
Some more specific outputs from the survey are listed below:
a) To obtain expenditure weights and other useful data for the revision of the consumer price index;
b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts;
c) To supply basic data needed for policy making in connection with social and economic planning;
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 gather information on poverty lines and incidence of poverty throughout Nauru.
Kind of Data
Sample survey data [ssd]
Version
Version number
Version 01: Cleaned, labelled and anonymized version of the Master file.
Version Date
2019-11-21
Version Notes
This version is the anonymized one of the Master file. This means a thorough process of anonymization was undertaken in order to reduce risk disclosure.
Scope
Keywords
| Keyword |
|---|
| Household |
| Individual |
| HIES |
| Income |
| Expenditure |
| COICOP |
| PACCOI |
| Economic activity |
| Education |
| Health |
| Commodities |
| ISIC |
| ISCO |
Coverage
Geographic Coverage
National coverage.
Unit of Analysis
- Households
- Individuals
- Expenditure items
- Individuals
- Expenditure items
Universe
The scope of the 2012-2013 Household Income Expenditure Survey (HIES) was all occupied households in Nauru.
HIES covered all persons who were considered to be usual residents of private dwellings, usual residents who were away for a short amount of time and those away for a for a longer amount of time but still financially linked to the household.
HIES covered all persons who were considered to be usual residents of private dwellings, usual residents who were away for a short amount of time and those away for a for a longer amount of time but still financially linked to the household.
Producers and sponsors
Authoring entity/Primary investigators
| Agency Name | Affiliation |
|---|---|
| Nauru Bureau of Statistics | Government of Nauru |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Statistics for Development Division | Pacific Community (SPC) | Technical assistance in data collection and data processing |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Nauru | GoN | Principal Funding Agency |
| Pacific Community | SPC | Technical Assistance |
| Asian Development Bank | ADB | Principal Funding Agency |
| Australian Agency for International Development | AUSAID | Principal Funding Agency |
Sampling
Sampling Procedure
-SAMPLE DESIGN:
The sample was selected to be representative of the whole country of Nauru. Approximately 37 per cent of households were selected across Nauru, although not all responded to the survey - these details are provided in Section 4 of the Methodology Report (provided as External Resources). The sample fractions applied to each of the eight constituencies (plus Location) within Nauru were roughly the same, although they were slightly modified to create even workloads for interviewers. Whilst a 37 per cent sample seems exceptionally large for even a small country like Nauru, due to the large levels of non-response experienced in the 2006 HIES it was considered necessary.
At the household level the survey is targeting All members of the Household; however, the main respondent will be the Household Head which could be either the Husband or spouse. If both are unavailable during the survey then any responsible adult will become the respondent. Additional information will also be sought from members of the household who are 15 years with respect to their economic activity and income earning activities.
The selection of the households was based on the 2011 Census of Population and Housing. The process for making the selections was to order the households within each constituency by size (number of persons in the household) and running a systematic skip through each constituency list to achieve the desirable sample size. Such a procedure should ensure each constituency is suitably represented as well as each household size type.
-SAMPLE SELECTION:
Yaren: 40 (out of 105);
Boe: 49 (out of 51);
Aiwo: 81 (out of 84);
Buada: 49 (out of 51);
Ubenide: 101 (out of 104);
Ewa/Anetan: 51 (out of 53);
Anabar/Ijuw/Anibane: 51 (out of 53);
Meneng: 86 (out of 89);
Location (phosphate mining barracks): 116 (out of 120).
The sample was selected to be representative of the whole country of Nauru. Approximately 37 per cent of households were selected across Nauru, although not all responded to the survey - these details are provided in Section 4 of the Methodology Report (provided as External Resources). The sample fractions applied to each of the eight constituencies (plus Location) within Nauru were roughly the same, although they were slightly modified to create even workloads for interviewers. Whilst a 37 per cent sample seems exceptionally large for even a small country like Nauru, due to the large levels of non-response experienced in the 2006 HIES it was considered necessary.
At the household level the survey is targeting All members of the Household; however, the main respondent will be the Household Head which could be either the Husband or spouse. If both are unavailable during the survey then any responsible adult will become the respondent. Additional information will also be sought from members of the household who are 15 years with respect to their economic activity and income earning activities.
The selection of the households was based on the 2011 Census of Population and Housing. The process for making the selections was to order the households within each constituency by size (number of persons in the household) and running a systematic skip through each constituency list to achieve the desirable sample size. Such a procedure should ensure each constituency is suitably represented as well as each household size type.
-SAMPLE SELECTION:
Yaren: 40 (out of 105);
Boe: 49 (out of 51);
Aiwo: 81 (out of 84);
Buada: 49 (out of 51);
Ubenide: 101 (out of 104);
Ewa/Anetan: 51 (out of 53);
Anabar/Ijuw/Anibane: 51 (out of 53);
Meneng: 86 (out of 89);
Location (phosphate mining barracks): 116 (out of 120).
Response Rate
The HIES 2012/2013 survey response rates were a significant improvement on those achieved for the 2006 HIES, reaching levels more in line with what is considered acceptable. The national response rate of the survey was 74% (460 households responded out of the 624 that were selected).
Yaren: 55%;
Boe: 76%;
Aiwo: 72%;
Buada: 88%;
Ubenide: 68%;
Ewa/Anetan: 92%;
Anabar/Ijuw/Anibane: 77%;
Meneng: 93%;
Location (phosphate mining barracks): 56%.
Yaren: 55%;
Boe: 76%;
Aiwo: 72%;
Buada: 88%;
Ubenide: 68%;
Ewa/Anetan: 92%;
Anabar/Ijuw/Anibane: 77%;
Meneng: 93%;
Location (phosphate mining barracks): 56%.
Weighting
-HOUSEHOLD WEIGHTS:
The household weights for the analysis were derived by dividing the projected population of households for each constituency as at the mid-point of Household Income and Expenditure Survey (HIES) field work, by the final responding sample of households for each constituency.
Weight i (HH)=ProjPop i (HH)/RespondingSample i (HH)
*i: constituency.
-PERSON WEIGHTS:
The person weights for the analysis were derived by dividing the projected population of persons for each constituency, sex and age group as at the mid-point of HIES field work, by the final responding sample of persons for each constituency, sex and age group.
Weight i (Per)=ProjPop i (Per)/RespondingSample i (Per)
*i: each combination of constituency by sex by age group.
The household weights for the analysis were derived by dividing the projected population of households for each constituency as at the mid-point of Household Income and Expenditure Survey (HIES) field work, by the final responding sample of households for each constituency.
Weight i (HH)=ProjPop i (HH)/RespondingSample i (HH)
*i: constituency.
-PERSON WEIGHTS:
The person weights for the analysis were derived by dividing the projected population of persons for each constituency, sex and age group as at the mid-point of HIES field work, by the final responding sample of persons for each constituency, sex and age group.
Weight i (Per)=ProjPop i (Per)/RespondingSample i (Per)
*i: each combination of constituency by sex by age group.
Data Collection
Dates of Data Collection (YYYY/MM/DD)
| Start date | End date | Cycle |
|---|---|---|
| 2012-09-07 | 2013-09-12 | Data Collection |
Time periods (YYYY/MM/DD)
| Start date | End date | Cycle |
|---|---|---|
| 2012-09-07 | 2012-09-27 | Round 1 |
| 2012-09-28 | 2012-10-18 | Break 1 |
| 2012-10-19 | 2012-11-08 | Round 2 |
| 2012-11-09 | 2012-11-29 | Round 3 |
| 2012-11-30 | 2012-12-20 | Round 4 |
| 2012-12-21 | 2013-01-03 | Break 2 |
| 2013-01-04 | 2013-01-24 | Round 5 |
| 2013-01-25 | 2013-02-14 | Round 6 |
| 2013-02-15 | 2013-03-07 | Round 7 |
| 2013-03-08 | 2013-03-28 | Round 8 |
| 2013-03-29 | 2013-04-18 | Round 9 |
| 2013-04-19 | 2013-05-09 | Round 10 |
| 2013-05-10 | 2013-05-30 | Round 11 |
| 2013-05-31 | 2013-06-20 | Round 12 |
| 2013-06-21 | 2013-07-11 | Round 13 |
| 2013-07-12 | 2013-08-01 | Round 14 |
| 2013-08-02 | 2013-08-22 | Round 15 |
| 2013-08-23 | 2013-09-12 | Round 16 |
Mode of data collection
Face-to-face [f2f]
Supervision
The structure of the team overseeing the field work consisted of the Household Income and Expenditure Survey (HIES) manager, an assistant coordinator, one supervisor, one quality control officer and four interviewers. The HIES manager was responsible for overseeing the field work, with the aid of the assistant coordinator, both staff of the Bureau of Statistics. In turn the assistant coordinator was the main point of contact for the field supervisor, who provided the guidance and assistance to the four interviewers. The quality control officer was responsible for undertaking the data entry, and at the end of each round, feedback was given to the assistant coordinator about any errors which needed to be further addressed in the field.
Type of Research Instrument
A Questionnaire consisting of four Modules and a Weekly Diary covering 2 weeks was used for Nauru Household Income and Expenditure Survey (HIES) 2013. Each Module covers distinct but connected portion of the Household.
The Modules are as follows:
- Module 1: Demographic Information (basic demographic information from each member of the household);
- Module 2: Household Expenditure;
- Module 3: Individual Expenditures;
- Module 4: Income
- Diary Week 1: Covering the first 7 Days (1 - 7)
- Diary Week 2: Covering the second 7 Days (8 - 14).
The questionnaires were published in English.
The Modules are as follows:
- Module 1: Demographic Information (basic demographic information from each member of the household);
- Module 2: Household Expenditure;
- Module 3: Individual Expenditures;
- Module 4: Income
- Diary Week 1: Covering the first 7 Days (1 - 7)
- Diary Week 2: Covering the second 7 Days (8 - 14).
The questionnaires were published in English.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Nauru Bureau of Statistics | NBOS | Government of Nauru |
Data Processing
Cleaning Operations
Data editing was done using the program: CSPro 5.1.
-DATA CLEANING PHASE 1: MANUAL CLEANING
Although a heads-up interactive system was used in the processing of Household Income and Expenditure Survey (HIES) data, data errors are always inevitable. This can be contributed to a multitude of reasons and additional efforts must be made to have accurate data. Data cleaning Phase 1 was done in-country where access to original questionnaires allowed us to correct data by referencing source documents. Errors were identified using an edit specification program which checks keyed data for:
• missing information
• value specific ranges
• data item relations
• sequencing errors.
Steps were taken to ensure that missing items were not replaced with assumed values from those involved with data corrections. Data items such as these were addressed in Data Cleaning Phase 2.
-DATA RECODING PHASE 1: SUB_RECORDS OF RELATED DATA ITEMS
The objective of this phase was to combine similar data items in the questionnaire into 17 unique databases. The process involved identifying questions and sections in the questionnaire that were related either by subject area, classifications, and transaction types. The advantages of these databases are listed below:
• Simplify data corrections - when the data is organised into liked data items, it makes for easier data corrections when implementing sub-routines to check for further data inconsistencies, outliers and code verifications.
• Allow for area specific analysis - because the data is organised into similar subject areas, classifications and transaction types, the emerging databases then can be used independently or cross-referenced to other databases.
• Simplify the creation of aggregated data - the overall intention of these sub-databases is to create four main databases used in the analysis of HIES data.
-DATA CLEANING PHASE 2: VALIDATION, IMPUTATION AND REMOVAL
Before any data could be properly recoded into the main aggregated income and expenditure databases the information needed to be properly coded. After field collection this activity was very time consuming because each transaction needed to be verified for relevance and completeness. Activities in this process included:
• deletion of households having inefficient amount of diary transactions or missing large amount of data
• data item code validation and correction (e.g. COICOP, ISIC, and ISCO)
• imputation of missing or invalid data items
• deletion of transactions with insufficient data items.
-DATA RECODING PHASE 2: OUTPUT
The final phase was to recode the sub-databases into the four main databases (Income, Expenditure, Person and House). The activities in this phase included quality control measures that checked for consistencies and balancing between:
• categories, reference periods and transaction types
• income and expenditure
• sub-databases and aggregated databases.
-DATA CLEANING PHASE 1: MANUAL CLEANING
Although a heads-up interactive system was used in the processing of Household Income and Expenditure Survey (HIES) data, data errors are always inevitable. This can be contributed to a multitude of reasons and additional efforts must be made to have accurate data. Data cleaning Phase 1 was done in-country where access to original questionnaires allowed us to correct data by referencing source documents. Errors were identified using an edit specification program which checks keyed data for:
• missing information
• value specific ranges
• data item relations
• sequencing errors.
Steps were taken to ensure that missing items were not replaced with assumed values from those involved with data corrections. Data items such as these were addressed in Data Cleaning Phase 2.
-DATA RECODING PHASE 1: SUB_RECORDS OF RELATED DATA ITEMS
The objective of this phase was to combine similar data items in the questionnaire into 17 unique databases. The process involved identifying questions and sections in the questionnaire that were related either by subject area, classifications, and transaction types. The advantages of these databases are listed below:
• Simplify data corrections - when the data is organised into liked data items, it makes for easier data corrections when implementing sub-routines to check for further data inconsistencies, outliers and code verifications.
• Allow for area specific analysis - because the data is organised into similar subject areas, classifications and transaction types, the emerging databases then can be used independently or cross-referenced to other databases.
• Simplify the creation of aggregated data - the overall intention of these sub-databases is to create four main databases used in the analysis of HIES data.
-DATA CLEANING PHASE 2: VALIDATION, IMPUTATION AND REMOVAL
Before any data could be properly recoded into the main aggregated income and expenditure databases the information needed to be properly coded. After field collection this activity was very time consuming because each transaction needed to be verified for relevance and completeness. Activities in this process included:
• deletion of households having inefficient amount of diary transactions or missing large amount of data
• data item code validation and correction (e.g. COICOP, ISIC, and ISCO)
• imputation of missing or invalid data items
• deletion of transactions with insufficient data items.
-DATA RECODING PHASE 2: OUTPUT
The final phase was to recode the sub-databases into the four main databases (Income, Expenditure, Person and House). The activities in this phase included quality control measures that checked for consistencies and balancing between:
• categories, reference periods and transaction types
• income and expenditure
• sub-databases and aggregated databases.
Other Processing
Data entry was done using the program: CSPro 5.1.
DATA ENTRY:
As with all surveys it is always better to correct errors in the field and within the smallest lapse of time between enumeration and processing. The field collection methodology allowed for a two-week enumeration window that gave ample time for survey teams to validate data collected. The data entry process took advantage of this window and allowed for questionnaires to be checked manually as well as systematically using the data entry program.
Standard heads-down data entry practices use double-entry to ensure that data entered in the system truly mirrors information collected on the form. Because of the large amount of survey data collected in the Household Income and Expenditure Survey (HIES), tight project time frame, and a limited budget, a single-entry interactive system was used in the processing of HIES data. This data entry system aided in improved data quality by incorporating standard error checking specifications listed below.
• Validity - This process checked if the data entered in the system fell within established ranges associated with the data item.
• Consistency - This process checked the relation between data items and within acceptable characteristics of the country such as employment, education ages or gender specific expenditures.
DATA ENTRY:
As with all surveys it is always better to correct errors in the field and within the smallest lapse of time between enumeration and processing. The field collection methodology allowed for a two-week enumeration window that gave ample time for survey teams to validate data collected. The data entry process took advantage of this window and allowed for questionnaires to be checked manually as well as systematically using the data entry program.
Standard heads-down data entry practices use double-entry to ensure that data entered in the system truly mirrors information collected on the form. Because of the large amount of survey data collected in the Household Income and Expenditure Survey (HIES), tight project time frame, and a limited budget, a single-entry interactive system was used in the processing of HIES data. This data entry system aided in improved data quality by incorporating standard error checking specifications listed below.
• Validity - This process checked if the data entered in the system fell within established ranges associated with the data item.
• Consistency - This process checked the relation between data items and within acceptable characteristics of the country such as employment, education ages or gender specific expenditures.
Data Appraisal
Estimates of Sampling Error
Many factors contribute to the magnitude of the non-sampling errors associated with survey results. Unfortunately, unlike the sampling error, it is difficult to measure the extent of the impact. In order to better understand the reason behind this, one only needs to look at the different types of non-sampling errors to appreciate why it is difficult to measure their impact. Some of the more significant non-sampling errors which are discussed in the Methodology Report (provided as External Resource):
• Non response bias
• Reporting errors
• Data entry errors
• Changing economy.
• Non response bias
• Reporting errors
• Data entry errors
• Changing economy.
Data access
Contact
| Name | Affiliation | URI | |
|---|---|---|---|
| Director, Nauru Bureau of Statistics | Government of Nauru | [email protected] | https://nauru.prism.spc.int/our-contacts |
Confidentiality Declaration
Extracted from the Field Work Instruction Manaul (Attached):
1.3 Confidentiality
All information furnished will be kept confidential. The Nauru National Statistics Office currently operates under the guidance of the Statistics Act. The relevant sections of this Act, relating to the confidentiality issues of the survey are:
12. Every person employed in the execution of any duty under this Ordinance shall, before entering on his duties, make and subscribe before a magistrate, or other person authorized by law to administer oaths, an oath or affirmation in the form set out in Schedule 2.
13. (1) Any person, being employed in the execution of any duty under this Ordinance, who- (a) by virtue of such employment or duty becomes possessed of any information which might influence or affect the market value of any share, interest or other security, product or article, and who, before such information is made public, directly or indirectly uses it for personal gain; or (b) without lawful authority publishes or communicates to any person otherwise than in the ordinary course of his employment any information acquired by him in the course of such employment; or (c) knowingly compiles for issue any false statistics or information, Shall be guilty of an offence and shall be liable to imprisonment for 2 years and to a fine of $800.
(2) Any person , being in possession of any information which to his knowledge has been disclosed in contravention of this Ordinance, who publishes or communicates such information to any person shall be guilty of an offence and shall be liable to imprisonment for 2 years and to a fine of $800.
(3) Any person who- (a) hinders or obstructs an authorized officer in the lawful performance of any duties or in the lawful exercises of any powers imposed or conferred upon him under this Ordinance; or (b) refuses or neglects- (i) to complete and supply, within such time as may be specified in that behalf, the particulars required in any return, form or other document left with or sent to him; or (ii) to answer any question or inquiries put to or made of him, under this Ordinance; or (c) knowingly or recklessly makes in any return, form or other document completed by him under this Ordinance, or in any answer to any question or inquiry put to or made of him under this Ordinance, any statement which is untrue in any material particular; or
(d) without lawful authority or excuse, destroys, defaces or mutilates any return, form or other document containing particulars collected under this Ordinance; or (e) refuses without reasonable cause to grant access to records and documents in accordance with the provisions of section 9, shall be guilty of an offence and shall be liable to imprisonment for 1 year and to a fine of $400.
Breaches of this contract can have significant impacts on the successful conduct of not only this survey but future surveys undertaken by the Government of the Nauru.
Breaches of the contract will also have an impact on the individual/s concerned in the sense that their pay can be docked and any other employment opportunities of this nature in the future will be lost.
Conditions
Licensed datasets, accessible under conditions.
Citation requirement
"Nauru Bureau of Statistics Office, Household Income and Expenditure Survey 2012-2013 (HIES 2012-2013), Version 01 of the licensed datasets (November 2019), 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.
Copyright
© 2014, Nauru (NRU) Bureau of Statistics Office, Government of Nauru
Contacts
Contact
| Name | Affiliation | URI | |
|---|---|---|---|
| Nauru Bureau of Statistics | Government of Nauru | [email protected] | https://nauru.prism.spc.int/our-contacts |
Metadata production
Document ID
DDI_SPC_NRU_2012_HIES_v01_M_v01_A_PUF
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Fermin C. Sakisat | FCS | Secretariat of Pacific Communities (SPC) | Programming Officer |
| Statistics for Development Division | SDD | Pacific Community (SPC) | Review of the documentation |
Date of Production
2014-02-26
Document version
Version 1.0 - Dataset included; Reports Included. (2014-04-30)
Version 02: Review of the existing documentation. This review consists of replacing the de-identified datasets with the anonymized ones. Done in New Caledonia by the Statistics for Development Division (SDD), Pacific Community (SPC).
Version 02: Review of the existing documentation. This review consists of replacing the de-identified datasets with the anonymized ones. Done in New Caledonia by the Statistics for Development Division (SDD), Pacific Community (SPC).