Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

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.

Geographic coverage

National coverage: whole island was covered for the survey.

Analysis unit

• Household;
• Individual.

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.

Kind of data

Sample survey data [ssd]

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.

Sampling deviation

No deviations from the sample design took place.

Mode of data collection

Face-to-face [f2f]

Research instrument

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.

Cleaning operations

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.

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%

Sampling error estimates

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.

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

Abstract

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.

Geographic coverage

National

Analysis unit

• Household
• Person
• Expenditure Commodity

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.

Kind of data

Sample survey data [ssd]

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.

Sampling deviation

No deviations from the sample design took place.

Mode of data collection

Face-to-face [f2f] for questionnaires, self-enumeration for the diaries

Research instrument

The survey schedules adopted for the 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.

Cleaning operations

There were 3 phases to the editing process for the 2006 Nauru HIES which included: 1. Data Verification operations 2. Data Editing operations 3. Data Auditing operations

For more information on what each phase entailed go the document HIES Processing Instructions attached to this documentation.

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.

Sampling error estimates

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 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 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

Abstract

The main purpose of a Household Income and Expenditure Survey (HIES) survey was to present high quality and representative national household data on income and expenditure in order to update Consumer Price Index (CPI), improve statistics on National Accounts and measure poverty within the country. These statistics are a requirement for evidence based policy-making in reducing poverty within the country and monitor progress in the national strategic plan in place.

Geographic coverage

Urban (Funafuti) and rural areas (outer islands).

Analysis unit

Household and Individual.

Universe

Private households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Tuvalu 2022 HIES consists in the random selection of the appropriate numbers of households (within each strata urban and rural) in order to be able to disaggregate HIES results at the strata level (in addition to National level). The urban strata of Tuvalu is made of the island of Funafuti (as a whole) and the rest of the country (all outer islands) compose the rural strata. The statistical unit used to run this sampling analysis is the household. The sample procedure is based on the following steps: - Assessment of the accuracy of the previous 2015 HIES in terms of per capita total expenditure (variable of interest) and check whether the sample size at that time were appropriate and correctly distributed among both stratas, - Update this assessment process by using the most recent population count to get the new sample size and distribution, - Proceed to the random selection of households using this most recent population count. The sampling frame (most recent household listing and population count) used to update and select is the 2021 Tuvalu Household Listing conducted by the Central Statistics Division of Tuvalu. At the National level, the 2015 Tuvalu HIES reported a good accuracy of the per capita total expenditure (less than 5%) but the disaggregation results by strata showed a lower quality of the result in Tuvalu urban. The Tuvalu 2021 household listing provides the most recent distribution of the households across all the islands of Tuvalu. This step consists in updating the accuracy of the previous 2015 HIES by using this recent household count and get the appropriate RSE by changing the sample size. For budget constraint, the total sample size cannot get increased, as the funding situation does not allow higher sample size. It means that the only parameter that can be modified is the distribution of the sample across the strata. Sample size by stratum: -Urban: 350 (out of 1,010 urban households as per the 2021 listing) -Rural: 310 (out of 835 rural households as per the 2021 listing) -National: 660 (out of 1,845 total households as per the 2021 listing)

2015 per capita mean total expenditure (AUD): -Urban: 3,190 -Rural: 2,780 -National: 3,000

Relative Standard Error (RSE): -Urban: 5.1% -Rural: 4.1% -National: 3.3%

It results from this new sample design a new distribution that shows an increase in Funafuti urban, mainly due to: - The low quality of the survey results from the 2015 HIES, - The number of households that have increased by more than 15% between 2015 and 2020 in Tuvalu urban area.

The household selection process is based on a simple random procedure within each stratum: - The 350 households in Funafuti are selected using the same probability of selection across all villages of the islands - The 310 household in rural Tuvalu are distributed proportionally to the size of each rural island of Tuvalu. This proportional allocation of the sample across rural Tuvalu islands generates the best accuracy at the strata level.

Distribution of sample accross strata: Urban: Funafuti 350 Rural: Nanumea 42
Nanumaga 37 Niutao 46
Nui 39
Vaitupu 75
Nukufetau 45
Nukulaelae 23
Niukalita 4

Non-response is a problem in surveys, and it is crucial that the field teams interview the selected households (the location on the map and the name of the household head are used to help to determine the selected households). During the first visit, interviewers must do their best to convince the household head to participate in the survey (and get his/her approval to proceed to interview). It may happen in the field that the first visit results in: I. A refusal: the household head does not show any interest in the survey and is reluctant to participate, II. The house is empty (household members away at the time of the visit).

(I) Refusal: if the interviewer cannot convince the household head to participate, he has to liaise with the survey management, and the supervisor will help in the discussion to convince the household head to respond. In this case, it is important to mention that all responses are kept confidential and insist on the importance of it for the benefit of Tuvalu population. (II) Empty house: the interviewer must investigate (checking with neighbours) whether or not the house is still inhabited by the family: o If it is not the case, the dwelling is then vacant, and the replacement procedure must be activated. o If the dwelling is still occupied, interviewer must come back later the same day or the day after at different time

Only in extreme cases of persistent refusal or empty house (household members away during the time of the collection) the replacement procedure must be activated. The replacement procedure consists in changing the selected household to the closest neighbour who is available.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022 Tuvalu Household Income and Expenditure Survey (HIES) questionnaire was developed in English language and it follows the Pacific Standard HIES questionnaire structure. It is administered on CAPI using Survey Solution, and the diary is no longer part of the form. All transactions (food, non food, home production and gifts) are collected through different recall sections during the same visit. The traditional 14 days diary is no longer recommended in the region. This new method of implementing the HIES present some interesting and valuable advantages such as: cost saving, data quality, time reduction for data processing and reporting. The 2022 HIES of Tuvalu was directly integrated to a census through a Long Form Census (LFC). The LFC was an experiment led by the World Bank and the Pacific Community to try and group a census and a HIES collection. All households were normally enumerated during the 2022 Census and households selected to participate to the HIES were then asked the HIES questions.

Below is a list of all modules in this questionnaire: -Household ID -Demographic characteristics -Education -Health -Functional difficulties -Communication -Alcohol -Other individual expenses -Labour force -Fisheries -Handicraft and home-processed food -Dwelling characteristics -Assets -Home maintenance -Vehicles -International trips -Domestic trips -Household services -Financial support -Other household expenditure -Ceremonies -Remittances -Food insecurity -Financial inclusion -Livestock & aquaculture -Agriculture parcel -Agriculture vegetables -Agriculture rootcrops -Agriculture fruits

The survey questionnaire can be found in this documentation.

Cleaning operations

Data was edited, cleaned and imputed using the software Stata.

Response rate

There was a total of 662 households from the original selection of the sample. 592 of them were contacted 528 accepted the interviews. The number of valid households is 464, or 70% of households before replacement. After replacement, 54 households were considered valid making the final completion rate at 78% (73% in urban and 85% in rural area).

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2018/19 is the fourth round of the survey with prior rounds conducted in 2010/11, 2012/13, and 2015/16. GHS-Panel households were visited twice: first after the planting season (post-planting) between July and September 2018 and second after the harvest season (post-harvest) between January and February 2019.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Agricultural plots
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS-Panel sample of 5,000 households across 500 enumeration areas (EAs) and was designed to be representative at the national level as well as at the zonal level. The complete sampling information for the GHS-Panel is described in the Basic Information Document for GHS-Panel 2010/2011. However, after a nearly a decade of visiting the same households, a partial refresh of the GHS-Panel sample was implemented in Wave 4.

For the partial refresh of the sample, a new set of 360 EAs were randomly selected which consisted of 60 EAs per zone. The refresh EAs were selected from the same sampling frame as the original GHS-Panel sample in 2010 (the “master frame”). A listing of all households was conducted in the 360 EAs and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximated 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS-Panel households from 2010 were selected to be included in the new sample. This “long panel” sample was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across the 6 geopolitical Zones. The systematic selection ensured that the distribution of EAs across the 6 Zones (and urban and rural areas within) is proportional to the original GHS-Panel sample. Interviewers attempted to interview all households that originally resided in the 159 EAs and were successfully interviewed in the previous visit in 2016. This includes households that had moved away from their original location in 2010. In all, interviewers attempted to interview 1,507 households from the original panel sample.

The combined sample of refresh and long panel EAs consisted of 519 EAs. The total number of households that were successfully interviewed in both visits was 4,976.

Sampling deviation

While the combined sample generally maintains both national and Zonal representativeness of the original GHS-Panel sample, the security situation in the North East of Nigeria prevented full coverage of the Zone. Due to security concerns, rural areas of Borno state were fully excluded from the refresh sample and some inaccessible urban areas were also excluded. Security concerns also prevented interviewers from visiting some communities in other parts of the country where conflict events were occurring. Refresh EAs that could not be accessed were replaced with another randomly selected EA in the Zone so as not to compromise the sample size. As a result, the combined sample is representative of areas of Nigeria that were accessible during 2018/19. The sample will not reflect conditions in areas that were undergoing conflict during that period. This compromise was necessary to ensure the safety of interviewers.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 4 consists of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provides information on demographics; education; health (including anthropometric measurement for children); labor; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income. Household location is geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets.

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicits information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; and household fishing activities. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicits information on access to infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

The Household Questionnaire is slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collects different information during each visit, but for the same plots and crops.

Cleaning operations

CAPI: For the first time in GHS-Panel, the Wave four exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires, household, agriculture and community questionnaires were implemented in both the post-planting and post-harvest visits of Wave 4 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Survey Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given tablets which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 4 was highly automated. Each field team was given a mobile modem allow for internet connectivity and daily synchronization of their tablet. This ensured that head office in Abuja has access to the data in real-time. Once the interview is completed and uploaded to the server, the data is first reviewed by the Data Editors. The data is also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file is generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files are communicated back to respective field interviewers for action by the interviewers. This action is done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following

Abstract

The purpose of this survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in the Republic of Palau. 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 consumer price indices. 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 Palau.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

All private households.

Households that had not been residing in Palau for the last 12 months and did not intend to stay in Palau for the next 12 months at the time of the survey, were still selected in the survey, but treated as out-of-scope.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample of 20 per cent was considered more than sufficient for Palau. An additional 10 per cent of sample was selected to allow for sample loss. As a result, a sample size of 1,041 households (20 per cent of 4,684, with a 10 per cent top-up) was considered suitable for the survey.

Six target areas were identified as sub-populations for which estimates would be desirable. These six areas, which also can be considered stratum were: 1) Koror 2) Airai 3) East Babeldaob 4) West Babeldaob 5) Peleliu 6) Kayangel/Angaur

To accommodate this requirement, the sample of 1,041 households needed to be distributed amongst each of these six strata in such a manner that the level of accuracy derived from each stratum would be roughly equal. The manner in which this is achieved is to over-sample (proportion wise) from the smaller strata to ensure they still have sufficient sample.

To make workloads even and manageable in the field for interviewers and supervisors, the final sample size was adjusted such that it was divisible by 15 within each stratum. The number 15 was chosen as it was considered a suitable number of dwellings for an interviewer to enumerate over a three week period.

Another modification to the sample was with Kayangel/ Angaur. Given the required sample for this area was derived to be 60 dwellings, and there are only 73 dwellings in these areas, it was decided to completely enumerate this stratum.

Although it would be desirable to cover all of Palau for this survey, due to cost and time constraints a couple of areas were excluded from the frame before the selections were made. The two areas removed from scope were: 1) Sonsorol 2) Tobi

The impact on final estimates is considered to be very small given the small populations on these two islands; 18 households on Sonsorol, and 10 households on Tobi. This accounts for less than 0.5 per cent of the population of Palau.

The sample of dwellings was selected independently within each stratum. A complete list of all dwellings identified during the recent census was used as a frame. The first task was to sort the dwellings within each stratum by two variables: 1) Hamlet (on Koror) and State (rest of Palau) 2) Household Size (number of persons)

Once the list had been sorted, systematic sampling was used to produce the sample of dwellings. A skip was produced by dividing the population size for each stratum by the required sample size (N/n). Having produced the skip, a random start was then generated between 0 and the skip to determine the starting point for the systematic sample.

For details please refer to the attached document entitled Documentation for Sample Selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey schedules adopted for the HIES included the following: • Household Control Form • Expenditure Questionnaire • Income Questionnaire • Diary (x2)

Information collected in the four schedules covered the following: a) Household Control Form: This form includes the following information: 1. Name 2. Sex 3. Date of Birth 4. Ethnicity 5. Marital Status 6. Educational Attainment 7. Activity Status 8. Literacy Status 9. Internet Usage

b) Income questionnaire: This questionnaire has 8 sections and includes the following information: 1. Working for Wage and / or Salary 2. Agriculture, livestock, fishing and other sales 3. Other Self Employed & Business Operations 4. Previous Jobs held in the last 12 months 5. Services Provided to Other Private Households 6. Receipts from Custom Occasions 7. Welfare Benefits/Allowances 8. Other Income, including Remittances

c) Expenditure Questionnaire: This questionnaire has 16 sections and includes the following information: 1. Dwelling characteristics 2. Dwelling tenure 3. Mortgages and loans for purchase of dwellings 4. Insurance policies 5. Construction of new dwellings 6. Major home improvements 7. Household operation 8. Transportation 9. Travel – Domestic & Overseas 10. Education, recreation, sport and culture 11. Loans 12. Credit Cards/ Charge accounts 13. Contribution to benefit schemes 14. Medical and health services 15. Customs Occasions 16. Miscellaneous payments 17. Agricultural Assets

d) Weekly Diary: This questionnaire has 4 sections and includes the following information: 1. Items Bought 2. Consumption of Items Produced by the Household 3. Gifts 4. Winnings from Betting, Raffles and Lotteries

For the household control form, expenditure questionnaire and income questionnaire, a face-to-face interview was conducted with the household to capture the information. For the two diaries, the first diary was left with the household for the first week, for the household to fill out. After the first week, the diary is picked up and the second week diary is dropped off to be filled out and picked up at the end of second week. Interviewers were required to contact each household every two to three days to make sure households were filling out their diaries appropriately.

Response rate

The overall response rate for Palau was 73%, which was a lower response rate than what was expected. The final response status for the 1,063 households selected in the HIES, 760 households fully responded to the survey, 28 partially responded (of which 16 could be included in the analysis) and 275 didn’t respond at all for various reasons.

For details please refer to section 4.2.1 NON-RESPONSE BIAS in the attached report entitled Republic of Palau Household Income and Expenditure Survey 2006.

Sampling error estimates

To determine the impact of sampling error on the survey results, relative standard errors (RSEs) for key estimates were produced.

The estimates for Total Income and Total Expenditure from the 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.

Some 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 30.1%), 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.

Relative Standard Errors for key estimates at the region level can be found in Appendix 2 of the survey report.

Data appraisal

Non-response Bias In was seen that 760 households fully responded to the survey, 28 partially responded (of which 16 could be included in the analysis) and 275 didn’t respond at all for various reasons. Despite the table indicating that the vast majority of nonresponses were “vacant/out-of-scope”, this was unlikely as the dwellings were occupied at the time of the census, only one year prior to the HIES. The assumption was therefore made that these households were more than likely mis-coded during the HIES collection, and would more likely have been a refusal or non-contact.

Abstract

Household Income and Expenditure Survey (HIES) collects a wealth of information on household income and expenditure, such as source of income by industry, HH expenditure on goods and services, and income and expenditure associated with subsistence production and consumption. In addition to this, HIES collects information on sectoral and thematic areas, such as education, health, labour force, primary activities, transport, information and communication, transfers and remittances, food expenditure (acquisition) and gender.

The Pacific Islands regionally standardized HIES instruments and procedures were adopted by Tonga Statistics Department (TSD) for the 2015/2016 HIES. These standards, were designed to feed high-quality data to HIES data end users for: deriving expenditure weights and other useful data for the revision of the CPI, supplementing the data available for use in compiling official estimates of various components in the System of NA, supplementing the data available for production of the balance of payments; and gathering information on poverty lines and the incidence of poverty in Tonga.

The 2015/2016 HIES was conducted to update the 2009 HIES data and aimed to estimate the total amount HH spent and earnt over the past 12 months at the national and island group level (total expenditure and income).

Geographic coverage

National coverage.

Analysis unit

Individuals and Households.

Universe

Household Income and Expenditure Survey (HIES) covered all persons who were considered to be usual residents of private dwellings (must have been living in Tonga for a period of 12-months, or have intention to live in Tonga for a period of 12-months in order to be included in the survey).

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2 stages sample method used in the 2015 Household Income and Expenditure Survey (HIES) aims to select randomly: Census blocks (with probability proportional to size - each blocks will have a different probability of selection) - based on the 2011 population census; and households (HHs) (within each selected blocks all the HHs have the same probability of selection) - based on a update of the HH listing conducted by the HIES field team.

To make the probability of selection more even, some small census blocks were merged and some large census blocks split. There's more detail in the methodological report on this process. Before each round, the field teams updated the HH listing in each randomly selected block (stage 1 sample selection) and, after the listing was updated, the team randomly selected 18 HHs, which were 12 HHs as primary target HHs to interview; and 6 HHs in case that a primary selected HH cannot participate and a replacement is needed (e.g., refusal, absence, etc.) - this is done in order to achieve a high response rate.

Sampling deviation

Two-stage selection is used in Tongatapu (urban and rural), Vava’u, Ha’apai and ‘Eua, with the selection of census blocks (merged and split) in the first stage and the selection of households (HHs) in the second stage. HHs in Ongo Niua were selected directly from the updated HH listing (one-stage).

Mode of data collection

Face-to-face [f2f]

Research instrument

The use of a common questionnaire developed by the Statistics for Development Division (SDD) of the Pacific Community (SPC) was adopted by Tonga Statistics Department (TSD) to conduct Household Income and Expenditure Survey (HIES) 2015. Addition to the 4 Modules was a section on Deprivation.

4 modules to collect socio-demographic information, and expenditure and income; and o a two-week diary to collect daily expenditure, gifts received and home produced items Four modules are completed by paper-based personal interview, including: 1. Demographic information – characteristics of household (HH) members, including activity and education profile; 2. Household characteristics and expenditure (Housing characteristics, Housing tenure expenditure, Utilities and communication...etc); 3. Individual expenditure (Education, Health, Clothing, Communication...etc); 4. Individual and HH income (Wages and salaries, Agricultural and Forestry activities, Fishing, gatehring and hunting activities...etc).

Depending on the information being collected, a recall period (ranging from the last 7 days to the last 12 months) is applied to various sections of the questionnaire.
The forms were completed by face-to-face interview, usually with the Household (HH) head providing most of the information, with other HH members being interviewed when necessary. The interviews took place over a 2-week period such that the HH diary, which is completed by the HH on a daily basis for 2 weeks, can be monitored while the module interviews take place.

The HH diary collects information on the HH’s daily expenditure on goods and services; and the harvest, capture, collection or slaughter of primary produce (fruit, vegetables and animals) by intended purpose (home consumption, sale or to give away).

The 4 Modules were published in English but with a Tongan version that was made available to enumerators to help them with the interview. For the diaries they were published in both Tongan and English which ever version that the household find easy to fill in.

Cleaning operations

A first set of edits was done on the raw data via questionnaire checks, which was the first set of corrections. A summary of findings includes: 1820 questionnaires retrieved from the field in which 17 households with no diaries were removed. The process for the manual cleaning included checking the following information in the questionnaire: Labour force section (waged job) declared in module 1 and salaries declared in module 4 (income section). Imputation of wages in households: 30011, 40176, 40190, 40097. Electricity connection and payment of electric bill (module 2). Imputation of electricity bill in households: 20290, 20302, 20304, 30051. Households who are using butane for cooking and payment for butane.Ages and relationship to the household head.Check all the government pension in the salary section. Check the remittances sent by household members who are currently picking fruits overseas (that have to be transferred in wages & salary section).Check if the household members who are in Australia/NZ for fruit picking have declared their resident status in the household accordingly (option 4). After the first clean of the raw data, 1803 valid questionnaires were kept.

Further edits that was done to the raw data set was done using the software STATA.

Response rate

The table below shows the response rates by strata: -Tongatapu - urban: 99.8% -Tongatapu - rura: 99.8% -Vava'u: 100.0% -Ha'apai: 94.3% -Eua: 96.4% -Ongo Niua: 99.0% -Total: 99%

Sampling error estimates

Standard error, the relative sampling errors (RSE) and 95 percentage confidence interval were calculated for the total household expenditure, total household consumption expenditure, total household non consumption expenditure, total household cash expenditure, total household subsistence expenditure, total household expenditure on COICOP division 1, total household net income, total household net cash income, total net wages and salary cash income and total net primary cash income by strata. At the national level the RSE calculated was of good quality however caution should be made at the strata level.

Details of the sampling errors are presented in the sampling errors appendix 2 of the report presented in the external resources.

Non-sampling errors cannot be readily measured, however it is worth noting the issues associated with non-sampling errors, including: both respondents and interviewers may not entirely understand the information required from the survey, which can result in misinterpretation of the question being asked and the incorrect response; enumerator and respondent fatigue, resulting in underreporting, especially in completion of the household (HH) diary; unwillingness to fully disclose information – especially in a small-island context - such as income and expenditure on some items (e.g., alcohol, tobacco and cash donations); the questionnaire being in English, which could be a second language for both the interviewers and respondents, and the need to complete a written diary (noting that: three-quarters of diaries were in Tongan; HHs were given the opportunity to complete a Tongan written diary; and enumerators could mostly converse in Tongan when required); and the inability to interview HHs members living abroad but remain dependent on the HH (e.g., students living in school dormitories) or are working to support the HH (e.g., seamen living on a ship), but who have not formed another HH outside of Tonga.

Abstract

The National Sustainable Development Plan (NSDP) Baseline Survey 2019 is an expanded Household Income and Expenditure Survey (HIES) and is inclusive of health educational, cultural, and productive dimensions previously uncollected or in need of updating. The results of this survey will inform directly more than 30 key indicators listed in the NSDP M&E (Monitoring and Evaluation) Framework, as well as more than 40 of the listed indicators for the United Nations Sustainable Development Goals (SDGs). The NSDP Baseline Survey presents an opportunity as well for Vanuatu to establish a comprehensive Melanesian Wellbeing baseline as well as an updated baseline for the calculation of the Consumer Price Index (CPI) and revising National Accounts.

Geographic coverage

National coverage. Below are the details of this national coverage: 1. National (Vanuatu); 2. Provinces (Torba, Sanma, Penama, Malampa, Shefa, Tafea); 4. Area Councils (Torres Area council right to Futuna & Aneityum Area Council); 5. Villages / Towns; 6. Urban/Rural.

Analysis unit

Household and Individual.

Universe

All de jure residents.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for this survey was determined using the previous 2010 Household Income and Expenditure Survey (HIES) outputs, and especially the per capita monthly total expenditure. From the 2010 HIES the mean, standard deviation and standard error were computed (per capita expenditure) and from the 2016 Census the distribution of the population across the 6 provinces of Vanuatu was used as a base. According to the accuracy of this variable of interest within each province the sample size per province were adjusted in order to get an expected sampling error around 5% within each province. The sampling frame used is the last 2016 Vanuatu census for the computation of the probability of selection of the Enumeration Areas (EAs) and the random selection method started with the random selection of EAs using the probability proportional to size. Then within each selected EAs 10 households were randomly selected using the sampling uniformed method. Within each selected EA the household listing were updated by the team before random selection and interview.

i) The only variable considered is per capita total household expenditure (variable of interest), as in addition to being one of the main indicators derived from the Household Income and Expenditure Survey (HIES), it is likely highly correlated with many other variables of interest (e.g. poverty). From the 2010 HIES dataset, using this variable of interest, a list of relevant indicators were calculated, those indicators provide information on: - (a)the status of the household expenditure distribution within each province, - (b) The efficiency provided by the 2010 HIES sample design - (c) The accuracy of the estimates calculated from the 2010 HIES dataset (especially the per capita household expenditure, our variable or interest)

ii) The original dataset has been trimmed using the variable of interest, the lowest and the highest percentiles (the 1% households with the lowest and highest per capita total household expenditure) were removed from the analysis (outliers). The dataset ends up with 4,289 households (given 4,377 households were completed).

iii) The 2010 Vanuatu HIES sample was based on a stratified multi stages selection - Stratification: geographical provinces (by urban / rural locations) - First stage of selection: Enumerations Areas (EAs) with probability of selection proportional to size - Second stage: households, with uniform probability of selection within the EAs

iv) The mean and standard deviation indicate the status of the variable of interest within each strata. The intracluster correlation (p), and the design effect (DEFF) highlight the efficiency of the sampling strategy, and the standard error/relative standard error (SE/RSE) of the variable of interest show its accuracy.

v) The purpose of this analysis is to get some insights from the 2010 HIES sample design in order to improve the 2019 survey. There is no point to improve the sample size in strata where the sample is not efficient (the gain in accuracy will be minor compared to the related cost).

vi) The challenge in the 2019 Vanuatu baseline survey: - Meet precision targets in each strata (provincial level) including Penama where Ambae island has been evacuated at the time of the sample design. - Acceptable sample size (due to budget constraints) - Following international recommendations (12 months of field operation) - Enhance the monitoring and supervision of the field staff and simplify management of the logistics in the field

==> Optimize the variance/cost ratio of the survey design vii) Table 1 from the Document Sample Design (provided as External Resources) presents the Vanuatu 2010 HIES survey specifications, efficiency and accuracy in each strata (for the variable of interest). It shows that some improvements can be done in Torba, and Shefa rural (where the RSE is higher than 5%), and it shows a high intraclass correlation in Malampa, Shefa rural and Tafea (that lead to a high design effect in those strata). In Torba, the high design effect comes from the high number of households interviewed in each selected EA (on average 33 households per selected EA in this strata were interviewed). - Torba: the sample size is good, there is just a need to reduce the number of households to interview within each strata (and in order to keep a similar sample size the number of EAs to select in the province will be increased) - Malampa: given the high intracluster correlation in this province, a higher number of EAs to select is required (with the same number of households per EA to interview). - Shefa rural: keep the same number of households to interview within each EA, and increase the number of EA to select (this will lead to a higher sample size) - Tafea: similar to Malampa province, the high intraclass correlation indicates that the number of EAs to select has to be increased (therefore the sample size as well). The sample size has to be increased in Malampa, Shefa rural and Tafea, for the rest, the 2019 design will have to be similar as 2010 (in order to provide at least the same level of accuracy). viii) The 2019 Vanuatu base line survey follows the international recommendations in terms of data collection schedule (12-month coverage) and considers a better management and supervision of the field staff. In this context, the field staff will work by team, given that: - A team is made of 1 supervisor (team leader) and 2 or 3 interviewers - Each interviewer will be responsible for 5 interview per round - A round of survey is a 1 week period - 1 EA is covered during 1 round, after the round completion, the team moves to the next EA for the next round. - A team complete 32 rounds during the 12 month field operation period (roughly every 2 rounds/2 weeks) of work is followed by 1 round/1 week of rest). ix) Table 3 from the Document Sample Design (provided as External Resources) presents a survey schedule starting February 2019 and ending February 2020. During this period of 32 working weeks (corresponding to 32 different selected EAs) the teams will be on the field (a 3 weeks period of rest during Christmas period).

x) The number of interviewer by team and number of team by province will determine the total sample size within each province. A team made of 3 interviewers can achieve 480 households over the period, while a team of 2 interviewers can achieve only 320 cases.

xi) The intraclass correlation is used to calculate the precision loss due to clustering. Like the standard deviation, the intracluster correlation is considered to be a true population parameter, and therefore transferable between designs. We have to accept the hypothesis that this correlation factor has not changed during the period 2010-2019, and therefore can be used to predict DEFF and RSE for the next survey given an adjusted design (based on the conclusions provided by the 2010 design). Table 2 from the Document Sample Design (provided as External Resources) predicts the design effect and sampling error of the variable of interest given the new sample design that is based on: - the sample size within each strata - the number of teams within each strata - the number of interviewers per team In order to allow more flexibility in the sample size, it is preferable to set up some teams of 3 interviewers, that can achieve 480 households, which represent a good sample size for Torba and Sanma urban and some teams of 2 interviewers that will achieve 320 households each (2 teams will be required in other provinces).

xii) The proposed design in Table 2 from the Document Sample Design (provided as External Resources) shows a total sample size of 4,640 households and a higher level of accuracy of the estimate of the variable of interest in all the stratas. Only Shefa rural shows a RSE higher than 5%, which will be still acceptable. The high intraclass correlation in Shefa rural impacts the variance of the estimates and lead to an increase the sample size or a decrease of the number of households to interview per EA which is logistically and financially not recommended.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was developed in English using the World Bank software Survey Solutions. This questionnaire is divided into 18 modules that are detailed below.

-Introduction (geographic areas, list of household members) -Module 1: Demographic characteristics: ethnicity, marital status; -Module 2: Wellbeing: culture

Abstract

The purpose of the Household Income and Expenditure Survey (HIES) survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Kiribati. 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 Kiribati.

Geographic coverage

National coverage and regional island groups (Northern Gilberts, South Tarawa, Central Giberts, Southern Gilberts, Linix).

Analysis unit

Household and individual

Universe

The survey covered all persons resident in private households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey scope of the 2023/24 Household Income and Expenditure Survey (HIES) was all occupied private households listed from the 2023 household listing conducted by Kiribati National Statistics Office (KNSO). The listing was intended to update the boundaries of PSUs (Primary Sampling Units, e.g. enumeration areas) with the location of occupied dwellings for survey purposes. There were no islands or PSUs excluded from the random selection in the sample design of the 2023/24 Kiribati HIES. The sample was designed to produce robust estimates on household's expenditure and income patterns at the island group levels, urban and rural areas and national level. The sample size was computed using the performance of the previous 2019 HIES at the island groups level. A two-stage, stratified sampling approach was adopted where: - Enumeration areas were the PSUs and randomly selected with probability proportional to their size in the first stage. - Households were randomly selected using simple random sampling within each selected PSU.

A total of 190 PSUs where selected, with a cluster size of 12 households (list A to interview in priority). The expected sample size was then 2,280 complete interviews. To achieve this objective, the sample size has been increased by additional 6 households (list B in case of replacement) within each selected PSU to address non-response. The main reason for non-response was the absence or the unavailability of the household members at the time of the interview.

Overall, 2,418 interviews were successfully completed, which is more that was expected. This high completion rate (106%) is due to the following factors: - Replacement procedure: that allowed interviewers to replace non-responding households with households selected in list B - Change of instructions during field work: KNSO did change the procedure and halfway through field work, field teams were supposed to increase cluster size by including replacements. It resulted in a higher final cluster size (12,7 households compared to 12 expected).

From round 24 field work instructions changes and fieldworkers were supposed to increase the cluster size by adding to the selected households the replacement ones. From round 24 to round 34, the average cluster size was 14.2.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires were developped both in English and in I-Kiribati.

The questionnaire was administered through face-to-face interviews, with data entry conducted using Computer-Assisted Personal Interviewing (CAPI) software. The questionnaire was divided into 2 main sections made of the following modules: · Individual sections: o Demographic characteristics o Education o Health (including functional difficulties) o Communication o Alcohol, tobacco and kava o Other individual expenses o Labour force o Food away from home (FAFH) o Remittances o Social protection o Migrant workers · Household level modules: o Food recall o Non-food recall o Partaker o Dwelling characteristics o Assets o Home maintenance o Vehicles o International trips o Domestic trips o Household services o Financial support o Other household expenditure o Ceremonies o Food insecurity o Fisheries o Livestock o Agriculture o Handicraft and home-processed food o Deprivation o Natural disasters & climate change impacts

Cleaning operations

Data editing was done using the Stata software. A total of 10 Stata do-files were created to clean the Kiribati Household Income and Expenditue Survey data.

Abstract

The main objective of this project is to collect household data for the ongoing assessment and monitoring of the socio-economic impacts of COVID-19 on households and family businesses in Vietnam. The estimated field work and sample size of households in each round is as follows:

Round 1 June fieldwork- approximately 6300 households (at least 1300 minority households) Round 2 August fieldwork - approximately 4000 households (at least 1000 minority households) Round 3 September fieldwork- approximately 4000 households (at least 1000 minority households) Round 4 December- approximately 4000 households (at least 1000 minority households) Round 5 - pending discussion

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. Out of the 15 households, 3 households have information collected on both income and expenditure (large module) as well as many other aspects. The remaining 12 other households have information collected on income, but do not have information collected on expenditure (small module). Therefore, estimation of large module includes 9396 households and are representative at regional and national levels, while the whole sample is representative at the provincial level.

We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement. The sample size of large module has 9396 households, of which, there are 7951 households having phone number (cell phone or line phone).

After data processing, the final sample size is 6,213 households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire for Round 1 consisted of the following sections Section 2. Behavior Section 3. Health Section 4. Education & Child caring Section 5A. Employment (main respondent) Section 5B. Employment (other household member) Section 6. Coping Section 7. Safety Nets Section 8. FIES

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Response rate

The target for Round 1 is to complete interviews for 6300 households, of which 1888 households are located in urban area and 4475 households in rural area. In addition, at least 1300 ethnic minority households are to be interviewed. A random selection of 6300 households was made out of 7951 households for official interview and the rest as for replacement. However, the refusal rate of the survey was about 27 percent, and households from the small module in the same EA were contacted for replacement and these households are also randomly selected.

Abstract

The objectives of the survey were to provide information regarding the following: a. Household use of, and expenditure patterns for, social services; b. Reasons for low levels of household investment in education and health services for children; c. The distribution of the benefits of public spending for social services and how to improve targeting; d. Households' evaluation of the social services available to them; e. The potential for demand-side interventions to increase human capital investment directly (especially for girls and the poor); and f. The feasibility of repeated national monitoring surveys to assess the impact of future Bank and government projects in the social sectors, and to increase Tanzania's capacity to perform household survey work.

Geographic coverage

National coverage

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 5,184 households

The HRDS is national in scope and uses all the 222 clusters of the National Master Sample (NMS) maintained by the Bureau of Statistics as its sampling frame.4 Two NMS clusters were not surveyed because of weather conditions. For example, Nyamburi village in the Mara region was inaccessible. Heavy rains had washed away a bridge 8 kms (14 miles) from the village. All household surveys conducted by the Bureau of Statistics (e.g. Agricultural Sample Survey since 1986/87, Labor Force Survey in 1990/91) have used the framework of the NMS. This permits obtaining estimates at the national level and by area: rural, Dar es Salaam (DSM), and other urban towns. The current NMS covers 222 clusters: 100 rural villages representing the rural areas, and 122 Enumeration Areas (EAs) representing the urban areas. Fifty-two EAs are from the capital city, itself, 40 EAs are from the nine municipalities (Arusha, Dodoma, Moshi, Tanga, Morogoro, Iringa, Mbeya, Tabora, and Mwanza), and 10 EAs are from the remaining regional headquarters.

Selection of households and non-response.

Household selection was done in the field. In each cluster the team supervisor would first obtain the list of ten-cell leaders from the local authorities, and then, from each ten cell-leader, the list of households belonging to his/her cell. Each household was assigned a unique number, and then, using a table of random numbers, randomly selected. In each cluster, a list of about 30 households was then obtained, the last households in the list being alternates. With the collaboration of local authorities, the field workers were able to have an almost 100 percent reponse rate, except for the cases in which no member of the household was present for intervieing, and returning to the household was not feasible. Refusals to cooperate were rare. In those cases--absent households or refusals--, new households were drawn from the list of alternates.

The survey covered a total of 4,953 households in the 20 regions of Mainland Tanzania: 2,135 rural and 2,818 urban (see Table 1). In a second stage, the survey was extended to Zanzibar, where 230 households, in 24 clusters, were interviewed.

Region / Rural / Urban / Total Dodoma / 100 / 80 / 180 Arusha / 118 / 121 / 239 Kilimanjaro / 124 / 154 / 278 Tanga / 132 / 167 / 299 Morogoro / 88 / 120 / 208 Coast / 79 / 88 / 167 Dar es Salaam / 0 / 1127 / 1127 Lindi / 84 / 50 / 134 Mtwara / 114 / 44 / 158 Ruvuma / 69 / 49 / 118 Iringa / 124 / 128 / 252 Mbeya / 174 / 153 / 327 Singida / 82 / 41 / 123 Tabora / 99 / 72 / 171 Rukwa / 59 / 56 / 115 Kigoma / 83 / 35 / 118 Shinyanga / 153 / 54 / 207 Kagera / 193 / 24 / 217 Mwanza / 163 / 192 / 355 Mara / 97 / 63 / 160 Mainland Tanzania / 2135 / 2818 / 4953 Zanzibar / 127 / 104 / 231

Mode of data collection

Face-to-face [f2f]

Research instrument

Development of Survey Instrument.

The first draft of the household survey was developed in English in July, 1993. Training of enumerators, based on this draft, began on August 2, 1993. The month of August was devoted to training the enumerators and pre-testing the questionnaire. The first pre-test of the questionnaire took place in mid-August. The household questionnaire was almost completely precoded to eliminate coding errors and time delays. A category labeled "other: specify" was added to several questions. For those questions for which answers were not mutually exclusive, we precoded them with letters, rather than numbers, to allow for unambiguously coding of multiple answers. To minimize nonsampling errors, the questionnaire was in a form that reduced to a minimum the number of decisions required of interviewers while in the field. In anticipation of pages becoming detached from the questionnaire, every page contained a space for the household number and the last digit of the cluster code. Despite the fact that questions were written exactly as they were supposed to be asked by the interviewer, interviewers were granted some flexibility to give the interview greater semblance to a conversation, rather than an inquisition.

Pre-Test of Questionnaire.

The "pre-pre-test" of the questionnaire (August 16, 1993) was done only to discern whether the questions were understood, how long the administration of the survey required, whether all responses had been anticipated, which sections needed to be stressed during the training, etc. In this pre-pre-test, each questionnaire required an average of 4 hours to complete, far longer than the planned 1.5 hour maximum. The survey was consequently shortened and streamlined.

The true pre-test was conducted in two different types of clusters: Ubungo ward in DSM (urban) and Kibaha in the Coast Region (rural) over a period of two days. We chose these clusters because they are representative of two distinct groups, so a broader spectrum of answers and problems with the instrument could be anticipated. In the pre-test each questionnaire required an average of 2.5 hours. After a couple weeks of interviewing, the enumerators became more familiar with the instrument, resulting in their spending an average of 1.5 to 2 hours per questionnaire.

During the pre-test, each supervisor was asked to comment on each interview. The supervisor was asked to pay special attention to questions that seemed to make the respondent uncomfortable, that the respondent had difficulty understanding, or that the respondent seemed to dislike. The supervisor also evaluated which sections seemed to go slowly, had the most difficult questions, or provided insufficient opportunity for a complete response.

Revision of questionnaire.

Given the results of the two pre-tests, several areas for improvement in the questionnaire were identified. Perhaps most importantly, the willingness-to-pay amounts were adjusted. The sample distributions of the maximum willingness-to-pay questions were analyzed, and, based on that analysis, we decided to change some of the values. For example, in the child spacing question, the "pay Tsh 1,000" responses unexpectedly accounted for a large share of the bids. Thus, we provided the option of paying more by introducing "pay Tsh 50,000" and "pay Tsh 25,000" as answer choices. For the other contigent valuation sections--health and education--the first pre-test determined that there was also a large lumping of responses at the high end of the scale. We adjusted the ranges accordingly, although there remains some lumping at the high end in the final data.

We also changed the order of the sections. Based on the pre-test and judgment of the field workers, we decided to first ask the questions in the individual section, then the contigent valuation questions, then the household questions. Because the respondents enjoyed the contigent valuation questions so much, this decision helped increase interest in the questionnaire and re-energized the respondent before proceeding with the household questions--the last part of the questionnaire. The final survey instrument, incorporating all of the changes dictated by the pre-tests and other expert advice, was completed on September 12, 1993.

Translation.

Translation of the survey instrument was a joint effort of the enumerators and supervisors. Given the specific characteristics of the Kswahili language, this was a much better approach than asking one translator to translate from English to Kswahili, and another one to translate from Kswahili to English. The "group" translation, involving those who would ask the questions, was intended to avoid different interpretations of the same question and achieve uniformity. In this way the enumerators were able to better convey the message/objective of each question.

The majority of the interviews were conducted in swahili. In very few cases, because no one in the selected household could speak swahili, the need arose to use interpreters.

Our initial plan called for the field work to start no later than August 29. However, unforeseen circumstances, including both financial and logistical problems, delayed the first field trip. Both the money and the materials were available by September 6, and five of the six teams left for Tanga region on that day. Initially we had planned to have the sixth team based full-time in Dar es Salaam; however, tighter time constraints imposed by the above and subsequent delays eventually made it necessary to send the sixth team into the field as well, as detailed below.

Description of questionnaires

The main objective of the survey was to obtain data on the use of, and spending on, the social sectors. The primary emphasis was on education and health--the areas in which the major gaps in availability of data were identified. The survey was divided into five major components, each of which was further subdivided, as described below:

I. Individual Questionnaire A. Household Roster; B. Information on

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

Abstract

The 2005/6 Household Income and Expenditure Survey is the second nationwide survey of households undertaken by Solomon Islands Statistics Office (SISO) since 1992.

The primary objectives of the HIES includes: • Re-basing of the weights of the current basket of goods and services in the Consumer Price Index (CPI). The survey also aimed to provide data on the behavior of household consumption expenditure patterns that will help form the weights that would reflect the relative importance that consumers attach to commodities and services; • Obtaining relevant data for purposes of updating the series of national accounts aggregates particularly the Gross Domestic Product.

The secondary objectives of the HIES were to: • Obtain data on housing and general demographic characteristics of households; • Obtain data on poverty measures, income and income inequality measures; • Obtain relevant data for the Millennium Development Goals (MDG), particularly health and education; and • Obtain other relevant data where necessary

The field data collecting exercise was undertaken from October 2005 to March 2006 and that seasonality effects on expenditure was not fully considered.

Geographic coverage

National. The HIES operation covered both the Urban and Rural areas focusing on Honiara, Other Urban Areas and the Rural Areas of the ten (9) provinces, and aimed to produce estimates at the country national and provincial levels only.

Analysis unit

• Households
• Individuals

Universe

The survey targeted private households whilst collective households in hospital, hotels, prison and educational institutions were excluded. A household is considered in the scope for the survey if the household have resided in the Solomon Islands for the last 12 months or more, or if not, they intend to live in Solomon Islands for the next 12 months.

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey Design The survey was based on a two-stage sampling strategy using probability proportional to size (PPS) selection and random selection. The strategy for selection of each area type is slightly different depending also on enumerator workload schedule and the need to accommodate estimates at the National and Provincial level as well as Urban and Rural splits.

The Survey was designed to collect data for national and provincial level estimates and covered both urban and rural areas. The survey covered Honiara, provincial centers and rural areas within these provinces.

The sampling scheme used was a stratified two stage design with the Enumeration Areas (EA) as the Primary Sampling Unit (PSU) and the households within the sample areas as the secondary sampling unit (SSU). In the first stage the EAs were selected with probability proportional to their population size based on the 1999 population census. In the second stage households were selected using systematic sampling with a random start. The next stage was allocating the sample to each provinces proportional to the square-root of the population. This should mean that estimates of each province would roughly have the same level of accuracy. The sample was then split for each province between the provincial centers (considered to be urban) and the remaining rural population. Given the need for urban and rural estimates the sample was split between the two areas proportional to the square-root of the population based on the 1999 census. The last stage in the process involved modifying the final counts to accommodate the workloads for interviewers during the fieldwork. The interviewers were expected in the field for six months and could accommodate 10 households per month (60 household in total). It was desirable to have the total workloads for each province divisible by 60 to give each interviewer an even sized workload and have the sample spread out evenly across each month.

Since Honiara (capital of Solomon Islands) consists of a mix of areas which covers high income, middle income and low income areas, it was advisable that the EAs be grouped based on the class best suited to their situation. Thus for Honiara the EA list was sorted by the income group category for selection. The number of EAs to select from Honiara is simply the desirable sample size (480 households) divided by the number of households to be selected for each EA. It was decided that 10 households should be selected from each selected EA. Therefore the number of EAs that were selected was equivalent to (480 / 10) = 48 EAs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The HIES is a relatively complex survey and the instruments to collect data was implemented through the following questionnaires and associated sections: • Household Control Form – household composition and particulars; • Household Expenditure Form – housing amenities, facilities and major household, expenditure on tenure, fixed capital, land, property etc; • Personal Income Form – Income pattern of household members and other income earning activities; • Household Dairy – Daily expenditure by type of goods and services • An additional health module was included – health facility utilization, immunization, motherhood, mortality, breast feeding & family planning, Malaria and miscellaneous

Cleaning operations

The Statistics Programme at the Secretariat of the Pacific Community (SPC) provided the assistance in data processing. A HIES data entry program was setup in CSPro version 2.6 and data entry started soon after the first workload was registered in the Statistics Office in November 2005 until May 2006. Logic procedures for data editing are prepared in Microsoft Access and data editing for all questionnaires were done in CSPro, except for the Diary where the editing is done in Microsoft Excel. Data management queries are done in Microsoft Access and the production of tables was done in Microsoft Excel. This report was prepared in Microsoft Word. Data verification of 5 per cent is done to check the accuracy of data input, though data edit checks are carried out for completeness, consistency and accuracy including the outliers. Anomalies of data were amended appropriately.

Response rate

Response Rates A sample of 4,320 households was planned for the country and about 3,822 households (88.5%) responded favorably satisfying the survey requirements.

Non-Response Despite efforts made by the enumerators and follow up attempts by the supervisors in most of the cases, there was non-response encountered during the survey.

The reasons for non response by the household were due mainly to the following: • The household was out of scope of the survey • Dwelling was vacant or not being lived in • The household could not be contacted after a number of attempts • Household excluded for other reasons like death in the family, refusals, customary reasons etc

Sampling error estimates

Error Measurements No formal measures of sample errors have been calculated for the survey results.

Non sampling errors cannot be readily measured. These included: o A response difficulty caused by misunderstanding of what was required from the survey and survey instruments by both households and interviewers. o The questionnaires were in English, which is at least a second language for interviewers and respondents. o The fact that some expenditure are seasonal and would not have been picked up in the survey period. o The exclusion of remote areas and institutions from the sampling frame.