Abstract

The work plan activities in Kiribati related to the updating of the listing of all households and institutions in Kiribati is to produce a sex and age disaggregated population count that forms the basis for a sampling frame for the upcoming Social Indicator Survey (SIS) and Household Income and Expenditure Survey (HIES). It also serves the purpose of digitalising and harmonising enumeration areas (EAs) to facilitate random sampling and census planning. To achieve this, SPC was engaged to conduct the following activities:

1. Planning and budgeting: prepare a comprehensive plan and budget for the household listing.
2. Mapping: prepare field maps to be used in the listing; digitalise EA boundaries and harmonisation of new EA framework; training and capacity building of the Ministry of Environment, Lands and Agricultural Development; prepare maps for the selected EAs in the SIS.
3. Listing questionnaire design, enumerator training and technology: develop a tablet-based household listing questionnaire and associated training resources, and set up of technology (e.g., server, tablet interviewer application, backup protocols); support Kiribati's National Statistics Office (KI-NSO) to conduct training of enumerators in all aspects of the collection; and administer South-South support to Kiribati for the duration of the listing.
4. Sample design: design the sample and field plan for the SIS; and build capacity of KI-NSO in sample design and field work planning.

Geographic coverage

National coverage (full coverage).

Analysis unit

Households/Institutions and Individuals.

Universe

Households, Institutions, de jure household members.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire, which is designed in English, is divided into three main sections:

1) Household ID and Building Type 2) Person Roster 3) Geographic Information and Photo

The questionnaire was generated by Survey Solutions and is provided as an external resource.

Cleaning operations

Data was processed using the software STATA. Corrections were made both automatically and by visual control: validation checks in the questionnaire as well as final editing of the raw data.

Abstract

The 1996 Papua New Guinea household survey is designed to measure the living standards of a random sample of PNG households. As well as looking at the purchases, own-production, gift giving/receiving and sales activities of households over a short period (usually 14 days), the survey also collects information on education, health, nutrition, housing conditions and agricultural activities. The survey also collects information on community level access to services for education, health, transport and communication, and on the price levels in each community so that the cost of living can be measured.

There are many uses of the data that the survey collects, but one main aim is for the results to help government, aid agencies and donors have a better picture of living conditions in all areas of PNG so that they can develop policies and projects that help to alleviate poverty. In addition, the survey will provide a socio-economic profile of Papua New Guinea, describing the access that the population has to agricultural, educational, health and transportation services, their participation in various economic activities, and household consumption patterns.

The survey is nationwide and the same questionnaire is being used in all parts of the country, including the urban areas. This fact can be pointed out if households find that some of the questions are irrelevant for their own living circumstances: there are at least some Papua New Guinean households for which the questions will be relevant and it is only by asking everyone the same questions that living standards can be compared.

Geographic coverage

The survey covers all provinces except Noth Solomons.

Analysis unit

• Household
• Individual
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Listing Form and Selection of the Sample Listing of households is the first job to be done after the team has settled in and completed the introductions to the community. Listing is best done by the whole team working together. This way they all get to know the community and its lay-out. However, if the census unit is too large this wastes too much time. So before beginning asks how many households there are, very roughly, in the census unit (noting that teams are supplied with the number of households that were there in the 1990 census). If the answer is 80 or more, divide the team into two and have each half-team work on one sector of the community/village. See the section below on what to do when the listing work is divided up.

If the census unit is a "line-up point" that does not correspond to any single village or community the number of households will often exceed 200 and frequently they are also quite dispersed. In this case it is not practical to attempt to list the whole census unit, so a decision is made in advance to split the census unit into smaller areas (perhaps groupings of clans). First, a local informant must communicate the boundaries of the census unit and for natural or administrative sub-units with the larger census unit (such as hamlets; or canyons/valleys). The sub-units should be big enough to allow for the selection of a set of households (about 30 or more), but should not be so large that excessive transport time will be needed each day just to find the household. Once the subunit is defined, its boundaries should be clearly described. Then one of the smaller units is randomly selected and the procedures outlined above are then followed to complete the listing. Note: only one of the sub-units are listed, sample chosen, and interviews undertaken.

The most important thing in the listing is to be sure that you list all the households and only the households belonging to the named village or census unit (or subset of the census unit if it is a line-up point). In rural areas, explain to village leaders at the beginning: "We have to write down all the households belonging to (Name) village." In case of doubt, always ask: "Does this household belong to (Name) village?" In the towns, the selected area is shown on a map. Check that the address where you are listing is within the same area shown.

Also explain: "We only write down the name of the head of household. When we have the list of all the households, we will select 12 by chance, for interview."

Procedure for Listing The listing team walks around in every part of the village, accompanied by a guide who is a member of the village. If possible, find a person who conducted the 1990 Census in this community or someone with similar knowledge of the community and ask them to be your guide. Make sure you go to all parts of the village, including outlying hamlets. In hamlets, on in any place far from the centre, always check: "Do these people belong to (Name) village?"

In every part of the village, ask the guide about every house: "Who lives in this house? What is the name of the household head?" Note that you do not have to visit every household. At best, you just need to see each house but you do not need to go inside it or talk to anyone who lives there. Even the rule of seeing each house may be relaxed if there are far away household for which good information can be provided by the guide.

Enter the names of household heads in the lines of the listing form. One line is used for each household. As the lines are numbered, the procedure gives a number to each household. When you come to the last house, check with the guide: "Are you sure we have seen all the houses in the village?"

NOTE: It does not matter in what order you list the households as long as they are all listed. After the listing is complete, check that all lines are numbered consecutively with no gaps, from start to finish. The number on the last line should be exactly the number of households listed.

Note: If the list is long (say more than 30 households) interviewer may encounter difficulties when looking for their selected household. One useful way to avoid this is to show the approximately the place in the list here certain landmarks come. This can be done by writing in the margin, CHURCH or STORE or whatever. You can also indicate where the lister started in a hamlet, for example.

Sample Selection The sampling work is done by the supervisor. The first steps are done at the foot of the first page of the listing form. The steps to be taken are as follows:

1. Fill in the numbers asked for at the foot of the last listing page, as follows:
2. M: enter the total number of households listed (same as last household number shown).
3. Interval L: calculate (M / 15) to the nearest whole number.
4. R: This is a random number with 3-digit decimals between 0.000 and 0.999.

5. MR: multiply M by R and round to the nearest whole number. (If decimal 0.5, round up).

6. MR gives the 1st selection. (Exception: If MR=0, L gives the first selection.) Enter S against this line in the selection column of the list.

7. Count down the list, beginning after the 1st selection, a distance of L lines to get the 2nd selection, then another L to get the 3rd, etc. When you come to the bottom of the list, jump back to the top as if the list were circular. Stop after the 15th selection. Mark the 13th, 14th, and 15th selections "RES" (for reserve). Mark the 1st - 12th selection "S" (for selection).

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1996 Papua New Guinea Household Survey questionnaire consists of three basic parts:

Household questionnaire first visit: asks a series of questions about the household, discovering who lives there, what they do, their characteristics, where they live, and a little about what kinds of things they consume. This questionnaire consists of the following sections. - Section 1. Household Roster - Section 2. Education - Section 3. Income Sources - Section 4. Health - Section 5. Foods in the Diet - Section 6. Housing Conditions - Section 7. Agricultural Assets, Inputs and Services - Section 8. Anthropometrics - Section 9. Household Stocks

Consumption recall (second visit questionnaire): is focused primarily on assessing the household's expenditure, gift giving and recieving, production, and level of wealth. The information in the first and second visits will provide information that can determine the household's level of consumption, nutrition, degree of food security, and ways in which it organizes its income earning activities. This questionnaire consists of the following sections. - Section 1. Purchases of Food - Section 2. Other Frequent Purchases - Section 3. Own-production of Food - Section 4. Gifts Received: Food and Frequent Purchases (START) - Section 5. Annual Expenses and Gifts - Section 6. Inventory of Durable Goods - Section 7. Inward Transfers of Money - Section 8. Outward Transfers of Money - Section 9. Prices - Section 10. Repeat of Anthropometric Measurements - Section 11. Quality of Life

Community Questionnaire: which is completed by the interview team in consultation with community leaders. This questionnaire also includes market price surveys that are carried out by the team when they are working in the community. Associated with this is a listing of all households in the community, which has to be done prior to the selection of the 12 households. This questionnaire consists of the following sections. - Section A. Listing of Community Assets - Section B. Education - Section C. Health - Section D. Town or Government Station - Section E: Transport and Communications - Section F. Prices - Section G. Changes in Economic Activity, Infrastructure, and Services

Mode of data collection

Face-to-face [f2f]

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Households and persons

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: No - Special populations: No

UNIT DESCRIPTIONS: - Households: A household is a group of people who normally live in the same household unit, who are or are not related to one another, and and who eat from the same pot.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Bureau of Statistics

SAMPLE DESIGN: The sample followed a two-stage design in which enumeration areas (EAs) served as the primary sampling units and households as the secondary sampling units. A total of 500 EAs were selected based on probability proportional to size (PPS) of the total EAs in each state and the total households listed in those EAs. In each EA, 10 households were selected randomly from a list of all households in the EA. In total, 4,851 households and 29,993 individuals were interviewed in 500 EAs.

SAMPLE UNIT: Enumeration area and household

SAMPLE FRACTION: 0.1%

SAMPLE SIZE (person records): 72,191

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires: household questionnaire, agricultural questionnaire, and community/prices questionnaire. The household questionnaire collected information on size and composition of the household, as well as demographic, migration, education, work, time use, household assets, income, savings, and food consumption and security. The agricultural questionnaire collected information on crop and livestock production, storage, and sales. The community/prices questionnaire collected information on community and prices components.

Abstract

The 2017-18 Albania Demographic and Health Survey (2017-18 ADHS) is a nationwide survey with a nationally representative sample of approximately 17,160 households. All women age 15-49 who are usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey. Women 50-59 years old were interviewed with an abbreviated questionnaire that only covered background characteristics and questions related to noncommunicable diseases.

The primary objective of the 2017-2018 ADHS was to provide estimates of basic sociodemographic and health indicators for the country as a whole and the twelve prefectures. Specifically, the survey collected information on basic characteristics of the respondents, fertility, family planning, nutrition, maternal and child health, knowledge of HIV behaviors, health-related lifestyle, and noncommunicable diseases (NCDs). The information collected in the ADHS will assist policymakers and program managers in evaluating and designing programs and in developing strategies for improving the health of the country’s population.

The sample for the 2017-18 ADHS was designed to produce representative results for the country as a whole, for urban and rural areas separately, and for each of the twelve prefectures known as Berat, Diber, Durres, Elbasan, Fier, Gjirokaster, Korce, Kukes, Lezhe, Shkoder, Tirana, and Vlore.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-4 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The ADHS surveys were done on a nationally representative sample that was representative at the prefecture level as well by rural and urban areas. A total of 715 enumeration areas (EAs) were selected as sample clusters, with probability proportional to each prefecture's population size. The sample design called for 24 households to be randomly selected in every sampling cluster, regardless of its size, but some of the EAs contained fewer than 24 households. In these EAs, all households were included in the survey. The EAs are considered the sample's primary sampling unit (PSU). The team of interviewers updated and listed the households in the selected EAs. Upon arriving in the selected clusters, interviewers spent the first day of fieldwork carrying out an exhaustive enumeration of households, recording the name of each head of household and the location of the dwelling. The listing was done with tablet PCs, using a digital listing application. When interviewers completed their respective sections of the EA, they transferred their files into the supervisor's tablet PC, where the information was automatically compiled into a single file in which all households in the EA were entered. The software and field procedures were designed to ensure there were no duplications or omissions during the household listing process. The supervisor used the software in his tablet to randomly select 24 households for the survey from the complete list of households.

All women age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for individual interviews with the full Woman's Questionnaire. Women age 50-59 were also interviewed, but with an abbreviated questionnaire that left out all questions related to reproductive health and mother and child health. A 50% subsample was selected for the survey of men. Every man age 15-59 who was a usual resident of or had slept in the household the night before the survey was eligible for an individual interview in these households.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the ADHS, one for the household and others for women age 15-49, for women age 50-59, and for men age 15-59. In addition to these four questionnaires, a form was used to record the vaccination information for children born in the 5 years preceding the survey whose mothers had been successfully interviewed.

Cleaning operations

Supervisors sent the accumulated fieldwork data to INSTAT’s central office via internet every day, unless for some reason the teams did not have access to the internet at the time. The data received from the various teams were combined into a single file, which was used to produce quality control tables, known as field check tables. These tables reveal systematic errors in the data such as omission of potential respondents, age displacement, inaccurate recording of date of birth and age at death, inaccurate measurement of height and weight, and other key indicators of data quality. These tables were reviewed and evaluated by ADHS senior staff, which in turn provided feedback and advice to the teams in the field.

Response rate

A total of 16,955 households were selected for the sample, of which 16,634 were occupied. Of the occupied households, 15,823 were successfully interviewed, which represents a response rate of 95%. In the interviewed households, 11,680 women age 15-49 were identified for individual interviews. Interviews were completed for 10,860 of these women, yielding a response rate of 93%. In the same households, 4,289 women age 50-59 were identified, of which 4,140 were successfully interviewed, yielding a 97% response rate. In the 50% subsample of households selected for the male survey, 7,103 eligible men age 15-59 were identified, of which 6,142 were successfully interviewed, yielding a response rate of 87%.

Response rates were higher in rural than in urban areas, which is a pattern commonly found in household surveys because in urban areas more people work and carry out activities outside the home.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017-18 Albania Demographic and Health Survey (ADHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017-18 ADHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017-18 ADHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix C of the survey final report.

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE

Sample size is 9,000 households. The sample design adopted for the study was a two-stage self-weighting design in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households. The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained, and weights had to be added. The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups. In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population.

(b) SAMPLE DESIGN

Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one. In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed. Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated, but this had little effect on the findings of the survey. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases, questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question. These responses are coded in the data files with the following values:

VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

Tsogolo la Thanzi (TLT) is a longitudinal study in Balaka, Malawi designed to examine how young people navigate reproduction in an AIDS epidemic. Tsogolo la Thanzi means "Healthy Futures" in Chichewa, Malawi's most widely spoken language. Data are being collected to develop better understandings of the reproductive goals and behavior of young adults in Malawi - the first cohort to never have experienced life without AIDS. To understand these patterns of family formation in a rapidly changing setting, TLT used the following approach: an intensive longitudinal design where respondents are interviewed every four months at TLT's centralized research center. Data collection began in May of 2009 and was completed in June of 2012. To assess changes on a longer time-horizon, a follow-up survey referred to as TLT-2 was fielded between June and August of 2015. The Household Listing Dataset are supplementary data related to the Tsogolo la Thanzi [Healthy Futures] longitudinal data series. The Household Listing includes data from the complete household census used to generate the sample for the TLT study. It includes data from all persons living within seven kilometers of the TLT research center.

Context

This dataset was created by our in-house web scraping and data mining teams at PromptCloud and DataStock. This dataset is a sample of the full dataset that can be seen on our data repository. The property listing is one of the key factors most people are on the lookout for these days. Real-Estate data is required by many to make sure they can quote the correct price and keep the competitive pricing is present.

You can download the full dataset from our data repository at DataStock. I am attaching the link of the dataset below. Link: https://app.datastock.shop/?site_name=Property_Listing_from_Homes.com

Content

Total Records Count : 798088  Domain Name : homes.com  Date Range : 01st Mar 2020 - 31st May 2020   File Extension : xml

Available Fields : uniq_id, crawl_timestamp, ad_title, location, price, bedrooms, bathrooms, sqft, overview, home_details, mls_number, listing_source, listing_agent, offered_by, image_urls

Acknowledgments

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud and DataStock.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The survey covers the urban area of two largest cities of Vietnam, Ha Noi and HCMCT.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The STEP target population is the population aged 15 to 64 included, living in urban areas, as defined by each country's statistical office. In Vietnam, the target population comprised all people from 15-64 years old living in urban areas in Ha Noi and Ho Chi Minh City (HCM).

The reasons for selection of these two cities include :

(i) They are two biggest cities of Vietnam, so they would have all urban characteristics needed for STEP study, and (ii) It is less costly to conduct STEP survey in these to cities, compared to all urban areas of Vietnam, given limitation of survey budget.

• The target population is not representative for the national urban population.

The following are excluded from the sample:

• Residents of institutions (prisons, hospitals, etc)
• Residents of senior homes and hospices
• Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc
• Persons living outside the country at the time of data collection

Kind of data

Sample survey data [ssd]

Sampling procedure

• The sample of 3405 households was selected from 227 urban Enumeration Areas (EAs) in Ha Noi (107 EAs) and Ho Chi Minh City (120 EAs). From each EA 15 households were selected, so the number of households selected in Ha Noi was 1245 HHs, and in HCM, 2160 HHs.
• The 2009 Population and Housing Census was used as a sample frame.
• Regarding PSUs (EAs), the sampling frame is the list of 15% of total EAs of the 2009 Population Census. Data items on the frame for PSU include provincecode, districtcode, commune code, and EA code; address of EA, number of households.
• Regarding ultimate sampling units (households), sampling frame is a list of (100) households in each EA. Data items on the frame for ultimate sampling units (households) include names of heads of households.

The sample frame includes the list of urban EAs and the count of households for each EA. Changes of the EAs list and household list would impact on coverage of sample frame. In a recent review of Ha Noi, there were only 3 EAs either new or destroyed from 140 randomly selected Eas (2%). GSO would increase the coverage of sample frame (>95% as standard) by updating the household list of the selected Eas before selecting households for STEP.

A detailed description of the sample design is available in section 4 of the NSDPR provided with the metadata. On completion of the household listing operation, GSO will deliver to the World Bank a copy of the lists, and an Excel spreadsheet with the total number of households listed in each of the 227 visited PSUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) a Background Questionnaire developed by the WB STEP team (ii) a Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. The WB STEP team and ETS collaborated closely with the survey firms during the process and reviewed the adaptation and translation to Vietnamese (using a back translation). - The survey instruments were both piloted as part of the survey pretest. - The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process 1. Raw data is sent by the survey firm 2. The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6. ETS scales the Reading Literacy Assessment data. 7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

The response rate for Vietnam (urban) was 62%. (See STEP Methodology Note Table 4).

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. All country weighting documentations are provided as an external resource.

Abstract

The main objective of the survey (TLSS-03) was to measure the level of living of the people of Turkmenistan with respect to various social and economic indicators and produce comparable statistics to the TLSS-98. The survey results formed an important database for building a system of monitoring of the living standards in the country.

The survey will focus on income level and expenditure pattern of households along with their social opportunity and access to public services. The survey will integrate the social and economic aspects of living standards and reveal the social strata that need more attention and protection from state. The survey will analyse the different factors affecting the living standards and will produce valuable information required in development planning and policy making.

A wide range of information collected from the survey was analysed to reveal the major socio-economic factors affecting the level of living. The basic survey approach and the questionnaire was designed to ensure the comparability of statistics with TLSS-98, so that data analysis can be made in cross-statistics as well as in time series.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Like in 1998, the survey was designed as a two-stage stratified cluster sampling. The principle of stratification into urban and rural for each 5 regions (Velayats) also remains unchanged. It created 11 independent strata (10 from 5 regions plus one stratum of Ashgabad). Primary sampling units (psu) were clusters formed of enumeration area units as described above. Households were listed in the selected clusters and sub-sampled by field staffs from the listing sheets.

TLSS-03 had a self-weighting design and samples were spread out over the wide area of the country. For this purpose, psu's were arranged in the order of geographical location across the different Etraps. Selection of PSU's was made systematically probability proportional to the number of households in clusters.

A fixed sample of 20 households was selected from each cluster using simple random sampling method. Selection of psu's by pps method at first stage and inversely proportional to the number of households at second stage resulted in a self-weighting sample, which was very important for this survey, especially because a large number of indicators are means and proportions. In a self-weighting design, sample means and sample proportions are unbiased estimators of population means and population proportions.

See detail sampling information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was collected using two type of questionnaires: - Household Questionnaire - Community Questionnaire

Cleaning operations

Prior to the data entry, questionnaires filled and returned from the field were checked and edited especially with regard to household identification numbers and data items. The questionnaire included, household listing form, household questionnaire and the community questionnaire. To facilitate the smooth data entry, the community questionnaires were folioed by Oblast, while the household questionnaires were folioed by the survey block. Each folio was provided with appropriate folio cover, which included the household identification and indicators to determine the status of every folio during machine processing. The total folios produced were as follows. - Community Questionnaire, 6 folios - Household Questionnare, 120 folios

The data entry programme was developed in CS Pro 2.3. The screen format for data entry was designed to make its look as similar as possible to the questionnaire. The form labels were made in both English and Russian versions. The programme also included the necessary control mechanism to ensure validity of entries. As mentioned above, there were two levels of questionnaires, so programme files were developed separately for community and household questionnaires.

Several department of TMH housed the data entry process. However, it was not felt necessary to install a network due to the relatively smaller size of the data load. An additional computer was designated for batch editing, form receipts and control and the monitoring purposes. The data entry was conducted from 4 January to 7 February 2004.

CSPro 2.3 was also used for editing. A batch edit program was developed to control the quality of data. Range checks were done on every data item. Additional consistency checks between data items were included in the edit programme. The program generated a list of errors for all questionnaires belonging to a particular household. The data items with error were manually compared with the corresponding questionnaire for verification. All necessary corrections were recorded in the error list and were later used for data correction. Since this is a sample based survey, automatic imputations were not done to preserve reliability of data.

Sampling error estimates

Estimation of the standard error was made based on the Balanced Repeated Replicates (BRR method). The method required exactly two psu’s per stratum. It takes half sample from each stratum and as many complements. The squared differences of two estimates provide an unbiased estimate of variance.

See detail estimation of the standard error and design effect information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Data appraisal

Limitations of the survey Although, the utmost attention was paid to ensure the quality of survey results, TLSS had some limitations. Users are strongly recommended to take these limitations into considerations while using the data of this survey. The limitations of the survey are broadly described below.

The survey frame 1. The main limitation of the survey was the quality of the frame used in the survey design. The last population census in Turkmenistan was conducted in 1995. Since then, a lot of demographic changes were observed mainly due the emigration of the Russian speaking population and internal replacement caused by massive housing reconstruction. Despite of all possible attempts directed to improve the frame, it must be recognised that the baseline data still came from the last census.

1. While the last population census results are no more a valid database for any kind of plausible statistical investigations, it is unfortunate that the upcoming Population census in 2005 has now been cancelled, which will be replaced by a “Mini-census of 5%”. Such census may produce the population figures, however, it will not provide so acutely required data for household surveys. Therefore, the problem of the frame is most likely to affect adversely also the quality of other household surveys to be conducted in future.

2. The problem of the frame is related also to the lack of maps of enumeration blocks used in the survey. The size of the earlier blocks in terms of the number of households has significantly changed, so new boundaries were fixed for this survey. However, there was no map available to show the recent changes. Field staffs prepared a new map by themselves for the selected blocks based on the list of households. However, the quality of such map could affect the accuracy of the size of blocks due to the omission or duplication that could occur in the absence of good map. In the absence of the decennial census, maps throughout the country are not updated in terms of the boundaries of enumeration blocks and the number of households. Again, it could also create difficulties in conducting other surveys in future.

Training and the fieldwork 4. During the data editing and consistency checking, several mistakes of field staffs were found in filling the questionnaire. These mistakes actually were the result of insufficient training of the field staffs. The supervisor’s training in the centre was limited only to those from TMH. Field staffs recruited from the centre and from the regional offices did not get the sufficient time of interaction on the various conceptual issues of the questionnaire, so could not sufficiently address much of the expected problems of the survey.

1. The effect of the poor training could have been minimised by an intensive and close supervision of the survey staffs. However, the number of supervisors deployed in the field was often below the initially planned number due to the constraints of time and manpower. There was no coordinated supervision of the fieldwork because the core survey staffs themselves were involved in data collection.

Total survey error 6. Although, sampling error of major variables of interest were at the accepted level, non-sampling errors of the survey were relatively high due to the poor quality of the frame, lack of sufficient training of the field staffs and weak supervision of data collection. Non-sampling error was also caused by measurement and non-response problem as mentioned in the earlier chapter. Therefore, the total margin of error of major estimates was higher, often substantially, than the estimated value of sampling error.

Profile of the living standard 7. The analysis of the living standards requires a statistically viable baseline that allows the results of the survey for comparison over time and territory. In international practice, such baseline is the subsistence minimum, which serves as an objective criterion of measuring the level of living of population. In Turkmenistan, the subsistence minimum is not used for living standard analysis

Abstract

The Jordan Population and Family Health Survey (JPFHS) is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 2012 Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, and fertility preferences, as well as maternal and child health and nutrition, that can be used by program managers and policymakers to evaluate and improve existing programs. The JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional, or cross-national studies.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 2012 JPFHS sample was designed to produce reliable estimates of major survey variables for the country as a whole, urban and rural areas, each of the 12 governorates, and for the two special domains: the Badia areas and people living in refugee camps. To facilitate comparisons with previous surveys, the sample was also designed to produce estimates for the three regions (North, Central, and South). The grouping of the governorates into regions is as follows: the North consists of Irbid, Jarash, Ajloun, and Mafraq governorates; the Central region consists of Amman, Madaba, Balqa, and Zarqa governorates; and the South region consists of Karak, Tafiela, Ma'an, and Aqaba governorates.

The 2012 JPFHS sample was selected from the 2004 Jordan Population and Housing Census sampling frame. The frame excludes the population living in remote areas (most of whom are nomads), as well as those living in collective housing units such as hotels, hospitals, work camps, prisons, and the like. For the 2004 census, the country was subdivided into convenient area units called census blocks. For the purposes of the household surveys, the census blocks were regrouped to form a general statistical unit of moderate size (30 households or more), called a "cluster", which is widely used in surveys as a primary sampling unit (PSU).

Stratification was achieved by first separating each governorate into urban and rural areas and then, within each urban and rural area, by Badia areas, refugee camps, and other. A two-stage sampling procedure was employed. In the first stage, 806 clusters were selected with probability proportional to the cluster size, that is, the number of residential households counted in the 2004 census. A household listing operation was then carried out in all of the selected clusters, and the resulting lists of households served as the sampling frame for the selection of households in the second stage. In the second stage of selection, a fixed number of 20 households was selected in each cluster with an equal probability systematic selection. A subsample of two-thirds of the selected households was identified for anthropometry measurements.

Refer to Appendix A in the final report (Jordan Population and Family Health Survey 2012) for details of sampling weights calculation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 JPFHS used two questionnaires, namely the Household Questionnaire and the Woman’s Questionnaire (see Appendix D). The Household Questionnaire was used to list all usual members of the sampled households, and visitors who slept in the household the night before the interview, and to obtain information on each household member’s age, sex, educational attainment, relationship to the head of the household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. Moreover, the questionnaire included questions about child discipline. The Household Questionnaire was also used to identify women who were eligible for the individual interview (ever-married women age 15-49 years). In addition, all women age 15-49 and children under age 5 living in the subsample of households were eligible for height and weight measurement and anemia testing.

The Woman’s Questionnaire was administered to ever-married women age 15-49 and collected information on the following topics: • Respondent’s background characteristics • Birth history • Knowledge, attitudes, and practice of family planning and exposure to family planning messages • Maternal health (antenatal, delivery, and postnatal care) • Immunization and health of children under age 5 • Breastfeeding and infant feeding practices • Marriage and husband’s background characteristics • Fertility preferences • Respondent’s employment • Knowledge of AIDS and sexually transmitted infections (STIs) • Other health issues specific to women • Early childhood development • Domestic violence

In addition, information on births, pregnancies, and contraceptive use and discontinuation during the five years prior to the survey was collected using a monthly calendar.

The Household and Woman’s Questionnaires were based on the model questionnaires developed by the MEASURE DHS program. Additions and modifications to the model questionnaires were made in order to provide detailed information specific to Jordan. The questionnaires were then translated into Arabic.

Anthropometric data were collected during the 2012 JPFHS in a subsample of two-thirds of the selected households in each cluster. All women age 15-49 and children age 0-4 in these households were measured for height using Shorr height boards and for weight using electronic Seca scales. In addition, a drop of capillary blood was taken from these women and children in the field to measure their hemoglobin level using the HemoCue system. Hemoglobin testing was used to estimate the prevalence of anemia.

Cleaning operations

Fieldwork and data processing activities overlapped. Data processing began two weeks after the start of the fieldwork. After field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman, where they were registered and stored. Special teams were formed to carry out office editing and coding of the openended questions.

Data entry and verification started after two weeks of office data processing. The process of data entry, including 100 percent reentry, editing, and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by early January 2013. A data processing specialist from ICF International made a trip to Jordan in February 2013 to follow up on data editing and cleaning and to work on the tabulation of results for the survey preliminary report, which was published in March 2013. The tabulations for this report were completed in April 2013.

Response rate

In all, 16,120 households were selected for the survey and, of these, 15,722 were found to be occupied households. Of these households, 15,190 (97 percent) were successfully interviewed.

In the households interviewed, 11,673 ever-married women age 15-49 were identified and interviews were completed with 11,352 women, or 97 percent of all eligible women.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2012 Jordan Population and Family Health Survey (JPFHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2012 JPFHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2012 JPFHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer

Abstract

Household Income and Expenditure Survey (HIES) collects a wealth of information on HH 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 (as a proxy for HH food consumption and nutrition analysis), and gender.

The Pacific Islands regionally standardized HIES instruments and procedures were adopted by the Government of Tokelau for the 2015/16 Tokelau HIES. These standards were designed to feed high-quality data to HIES data end users for:

1. deriving expenditure weights and other useful data for the revision of the consumer price index;
2. supplementing the data available for use in compiling official estimates of various components in the System of National Accounts;
3. supplementing the data available for production of the balance of payments; and
4. gathering information on poverty lines and the incidence of poverty in Tokelau.

The data allow for the production of useful indicators and information on the sectors covered in the survey, including providing data to inform indicators under the UN Sustainable Development Goals (SDGs). This report, the above listed outputs, and any thematic analyses of HIES data, collectively provide information to assist with social and economic planning and policy formation.

Geographic coverage

National coverage.

Analysis unit

Households and Individuals.

Universe

The universe of the 2015/16 Tokelau Household Income and Expenditure Survey (HIES) is all occupied households (HHs) in Tokelau. HHs are the sampling unit, defined as a group of people (related or not) who pool their money, cook and eat together. It is not the physical structure (dwelling) in which people live. The HH must have been living in Tokelau for a period of six months, or have had the intention to live in Tokelau for a period of twelve months in order to be included in the survey.

Household members covered in the survey include: -usual residents currently living in the HH; -usual residents who are temporarily away (e.g., for work or a holiday); -usual residents who are away for an extended period, but are financially dependent on, or supporting, the HH (e.g., students living in school dormitories outside Tokelau, or a provider working overseas who hasn't formed or joined another HH in the host country) and plan to return; -persons who frequently come and go from the HH, but consider the HH being interviewed as their main place of stay; -any person who lives with the HH and is employed (paid or in-kind) as a domestic worker and who shares accommodation and eats with the host HH; and -visitors currently living with the HH for a period of six months or more.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015/16 Tokelau Household Income and Expenditure Survey (HIES) sampling approach was designed to generate reliable results at the national level. That is, the survey was not designed to produce reliable results at any lower level, such as for the three individual atolls. The reason for this is partly budgetary constraint, but also because the HIES will serve its primary objectives with a sample size that will provide reliable national aggregates.

The sampling frame used for the random selection of HHs was from December 2013, i.e. the HH listing updated in the 2013 Population Count.

The 2015/16 Tokelau HIES had a quota of 120 HHs. The sample covered all three populated atolls in Tokelau (Fakaofo, Nukunonu and Atafu) and the sample was evenly allocated between the three atoll clusters (i.e., 40 HHs per atoll surveyed over a ten-month period). The HHs within each cluster were randomly selected using a single-stage selection process.

In addition to the 120 selected HHs, 60 HHs (20 per cluster) were randomly selected as replacement HHs to ensure that the desired sample was met. The replacement HHs were only approached for interview in the case that one of the primarily selected HHs could not be interviewed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for this Household Income and Expenditure Survey (HIES) are composed of a diary and 4 modules published in English and in Tokelauan. All English questionnaires and modules are provided as external resources.

Here is the list of the questionnaires for this 2015-2016 HIES: - Diary: week 1 an 2; - Module 1: Demographic information (Household listing, Demographic profile, Activities, Educational status, Communication status...); - Module 2: Household expenditure (Housing characteristics, Housing tenure expenditure, Utilities and communication, Land and home...etc); - Module 3: Individual expenditure (Education, Health, Clothing, Communication, Luxury items, Alcohonl & tobacco); - Module 4: Household and individual income (Wages and salary, Agricultural and forestry activities, Fishing gathering and hunting activities, livestock and aquaculture activities...etc).

Cleaning operations

All inconsistencies and missing values were corrected using a variety of methods: 1. Manual correction: verified on actual questionnaires (double check on the form, questionnaire notes, local knowledge, manual verifications) 2. Subjective: the answer is obvious and be deducted from other questions 3. Donor hot deck: the value is imputed based on similar characteristics from other HHs or individuals (see example below) 4. Donor median: the missing or outliers were imputed from similar items reported median value 5. Record deletion: the record was filled by mistake and had to be removed.

Several questions used the hotdeck method of imputation to impute missing and outlying values. This method can use one to three dimensions and is dependent on which section and module the question was placed. The process works by placing correct values in a coded matrix. For example in Tokelau the “Drink Alcohol” questions used a three dimension hotdeck to store in-range reported data. The constraining dimensions used are AGE, SEX and RELATIONSHIP questions and act as a key for the hotdeck. On the first pass the valid yes/no responses are place into this 3-dimension hotdeck. On the second pass the data in the matrix is updated one person at a time. If a “Drink Alcohol” question contained a missing response then the person's coded age, sex and relationship key is searched in the “valid” matrix. Once a key is found the result contained in the matrix is imputed for the missing value. The first preferred method to correct missing or outlying data is the manual correction (trying to obtain the real value, it could have been miss-keyed or reported incorrectly). If the manual correction was unsuccessful at correcting the values, a subjective approach was used, the next method would be the hotdeck, then the donor median and the last correction is the record deletion. The survey procedure and enumeration team structure allow for in-round data entry, which gives the field staff the opportunity to correct the data by manual review and by using the entry system-generated error messages. This process was designed to improve data quality. The data entry system used system-controlled entry, interactive coding and validity and consistency checks. Despite the validity and consistency checks put in place, the data still required cleaning. The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database, consisting of: Person level record - characteristics of every (household) HH member, including activity and education profile; HH level record - characteristics of the dwelling and access to services; Final aggregated income - all HH income streams, by category and type; Final aggregated expenditure - all HH expenditure items, by category and type.

The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database.

Response rate

Overall, 99% of the response rate objective was achieved.

Sampling error estimates

Refer to Appendix 2 of the Tokelau 2015/2016 Household Income and Expenditure Survey report attached as an external resource.

Abstract

The objectives of the Smallholder Household Survey in Tanzania were to:

• Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships • Segment smallholder households in Tanzania according to the most compelling variables that emerge • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value

Geographic coverage

National

Analysis unit

Households

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING FRAME

The smallholder household survey in Tanzania is a nationally representative survey with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level. The sampling frame is the list of enumeration areas (EAs) containing agricultural households. These EAs were created in preparation for the 2012 population and housing census. The census questionnaire included a question on whether any household member operated any land for agricultural purposes during the 2011-2012 agricultural year. The information collected helped to identify agricultural households during the census.

(b) SAMPLE ALLOCATION AND SELECTION.

For the sample allocation, regions were combined into the following zones: • Border: Ruvuma, Iringa, Mbeya, Rukwa, and Kigoma • Coastal: Tanga, Pwani, Dar es Salaam, Lindi, and Mtwara • Inland: Dodoma, Arusha, Kilimanjaro, Morogoro, Singida, Tabora, Manyara, Njombe, and Katavi • Lake: Shinyanga, Kagera, Mwanza, Mara, Simiyu, and Geita • Zanzibar: all regions

To take nonresponse into account, the target sample size was increased to 3,158 households assuming a nonresponse rate of 5 percent observed in similar national household surveys. The total sample size was first allocated to the zones in proportion to the number of agricultural households in the sampling frame. Within each zone, the resulting sample was then distributed to urban and rural areas in proportion to number of agricultural households. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total of 212 EAs were selected. The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each zone into urban and rural areas. The urban/rural classification is based on the 2012 population census. Therefore, 10 strata were created, and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with probability proportional to size, the size being the number of agricultural households in the EAs. A household listing operation was conducted in all selected EAs to identify smallholder households and to provide a frame for selecting smallholder households to be included in the sample. In the second stage, 15 smallholders were sampled in each EA with equal probability. In each sampled household, the household questionnaire was administered to the head of the household, the spouse, or any knowledgeable adult household member to collect information about household characteristics. The multiple respondent questionnaire was administered to all adult members in each sampled household to collect information on their agricultural activities, financial behaviours, and mobile money use. In addition, in each sampled household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

The smallholder survey in Tanzania is the third survey in the series, following the surveys in Mozambique and Uganda. Fieldwork in those two countries experienced a lot of failed call backs where identified eligible households and household members could not be interviewed during the time allocated to fieldwork in each country. As a result, the final sample size fell slightly short of the target. For this reason, in Tanzania the number of households selected in each EA was increased from 15 to 17 following the household listing operation in all sampled EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible. Following the finalization of questionnaires, a script was developed using Dooblo to support data collection on smart phones. The script was thoroughly tested and validated before its use in the field.

Response rate

The study achieved a household response rate of 99.1 percent, 84.8 percent for the Multiple Respondent questionnaire and 93.4 percent for the Single Respondent questionnaire.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors considering the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Abstract

Institute for Strategic Studies and Prognoses (ISSP) in Montenegro has undertaken several household surveys in an effort to provide timely and relevant data that is useful for policy makers and analysts. While data constraints have limited the ability to evaluate poverty and living standards in recent years, new household surveys collected by ISSP in 2002, 2003 and 2004 allow baselines to be established in regards to the living standards of the Montenegrin population and against which we can monitor changes in the future. Furthermore, with these data on household living standards, analysis can evaluate the role of social policies in supporting the poor as well as the potential impact of major policy reforms.

The ISSP surveys drew attention, once again, to the need for accurately measuring household living conditions according to well accepted standards, and for monitoring these trends on a regular basis. These surveys have provided the country with an invaluable training ground towards the development of a permanent household survey system to support the government strategic planning in its fight against poverty.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Persons aged 15 and above
• Consumption Expenditure Commodities / Items

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2004 Household Survey consists of a sample of about 1,000 households interviewed in all municipalities. Of these, 600 households are considered to be the Core Sample. In addition there are two booster samples (200 households each).

The Republic of Montenegro is divided geographically into 3 regions and into 21 municipalities which are, in turn, divided into settlements. Since the last census in Montenegro was undertaken in November 2003, the data were not fully available to be utilized for all stages of sample design. The preliminary results from the Census were used to compute the population share of each of the 21 municipalities in the total population. In turn, these population shares were used identify the target number of households for the Core sample.

In order to create a sample listing of households for each municipality and given the limited availability of the current Census data, the ISSP team had to look beyond the Census data. The research team identified two possible sources for developing the sample frame. The first is the Voting Registration list. The second source is the Mass Voucher Privatization (MVP) listing of all people compiled in order to distribute vouchers among the population of citizens over 18 years in the summer of 2001. Both lists exclude IDPs (which includes the Roma population in its definition). At the time when sampling was done, the MVP list was newer than the voting registration list. ISSP concluded that these two lists were fairly comparable. In addition, list of the households paying the bill to the Electricity Company was available as well, but with double entries included due to the almost 60,000 of weekend houses registered in Montenegro.

The MVP list was used to randomly list Core sample households such that the sample proportion in each municipality was equal to the overall population proportions. Households were interviewed based on this random sampling list for the municipality, with no clustering design in the sample within municipality, thereby reducing survey design effects which increase standard errors. The exception for this procedure was for Roma and displaced persons. The sample of Roma and displaced households in the Core sample were listed based on additional data sources (Roma NGOs and UNHCR list of displaced persons) since they are missing from the MVP. Roma and displaced persons in the Core sample listing are from Podgorica only since the largest share of these populations live in the central part of Montenegro (68% of Roma and 36% of displaced persons).

Of the Core sample of 600 households, 93% (559) are resident households, 3% (18) are Roma and 4% (23) are displaced households.

In addition to the Core sample, the 2004 Household Survey sample included two booster samples. A booster sample of 200 households was created in 3 municipalities defined as areas with certain ecological problems: Pljevlja (70), Mojkovac (60), and Zeta Valley (70). In order to have enough vulnerable and poor families for analytical purposes, the second booster sample of another 200 households was created from the listing of Family Material Support (FMS) program.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2004 Household Survey by ISSP consists of a detailed household questionnaire. The questionnaire is divided into several modules. These modules were aimed at matching as much as possible the specificity of Montenegro in terms of data needs, as driven by pressing policy questions. Their design (e.g. questions asked, their sequence, units and time-frames used) was adapted to fit the Montenegro reality. The questions covered in the 2004 survey were revised from the previous rounds with considerable input from policy-makers and analysts concerned with living standards measurement in Montenegro.

The questionnaire was divided in eight sections based on the topics covered, and was administered to households in one visit.

Cleaning operations

Data entry (DE) program was developed to facilitate the data entry process. The data entry program was developed using Microsoft Access software. Technical support of the World Bank was provided in order to develop ISSP capacities in this area. Among the useful features of the DE program which allowed for prompt and accurate entry were: a) The data entry form page was identical with the questionnaire page, which facilities data entry. b) Range checks for most variables where appropriate. c) Skip rules. The cursor of data entry jumps to the necessary box depending on the entered value of the previous variable.

Training for the data entry operators ran from May 25 to May 30, 2004.

Abstract

The objectives of the Smallholder Household Survey in Cote d'Ivoire were to: - Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; - Segment smallholder households in Cote d'Ivoire according to the most compelling variables that emerge; - Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, - Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The smallholder household survey in Cote d’Ivoire is a nationally-representative survey, with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level.

Sampling Frame In preparation for the 2014 population census, the country was divided into 22,600 census enumeration areas (EAs). For the ongoing 2015 agricultural census, the National Statistical Office (INS) has identified 18,321 EAs that contain agricultural households. The sampling frame for the smallholder survey is the list of these enumeration areas (EAs) containing agricultural households.

Sample allocation and selection In order to take nonresponse into account, the target sample size was increased to 3,333 households assuming a nonresponse rate of 10%. The total sample size was first allocated to the zones based on their population counts using the power allocation method. Within each zone, the resulting sample was then distributed to urban and rural areas in proportion to their population. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total of 223 EAs were selected. The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each zone into urban and rural areas. The urban/rural classification is based on the 2014 population census. Therefore, 6 strata were created, and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with probability proportional to size, the size being the population count in the EAs. A household listing operation was conducted in all selected EAs to identify smallholder households and to provide a frame for selecting smallholder households to be included in the sample. In the second stage, 15 smallholders were sampled in each EA with equal probability.

In each sampled household, the household questionnaire was administered to the head of the household, the spouse, or any knowledgeable adult household member to collect information about household characteristics. The multiple respondent questionnaire was administered to all adult members in each sampled household to collect information on their agricultural activities, financial behaviors, and mobile money use. In addition, in each sampled household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

After the selection of the EAs and the printing of the EA maps, it was necessary to reduce the number of EAs to be listed to 212 for budgetary reasons. Therefore, 212 EAs were randomly selected among the previously 223 sampled EAs and were finally included in the survey sample.

The smallholder survey in Cote d’Ivoire is the fifth survey in the series, following the surveys in Mozambique, Uganda, Tanzania and Bangladesh. Fieldwork in the first countries experienced a lot of failed call backs where identified eligible households and household members could not be interviewed during the time allocated to fieldwork in each country. As a result, the final sample size fell slightly short of the target. For this reason, in Cote d’Ivoire the number of households selected in each EA was increased from 15 to 17 following the household listing operation in all sampled EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

To capture the complexity of smallholder households, the smallholder household survey was divided into three questionnaires: 1) The Household questionnaire; 2) the Multiple Respondent questionnaire; and 3) the Single respondent questionnaire. It was designed in this way to capture the complete portrait of the smallholder household, as some members of the household may work on other agricultural activities independently and without the knowledge of others.

The household questionnaire collected information on the following: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head). • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires. • Household assets and dwelling characteristics.

Both the Multiple and Single Respondent questionnaires collected different information on the following: • Agricultural practices—farm information such as size, crop types, livestock, decision-making, farming association, and markets. • Household economics—employment, income, expenses, shocks, borrowing and saving habits, and investments.

The Single respondent questionnaire also collected the following information: • Mobile phones—attitudes toward phones, use, access, ownership, desire, and importance. • Financial services—attitudes toward financial products and services such as banking and mobile money, including ownership, usage, access and importance.

The questionnaires were translated into French and then pretested. After the pretest, debriefing sessions were held with the pretest field staff and the questionnaires were modified based on the observations from the pretest. After the questionnaires were finalized, a script was developed to support data collection on mobile phones. The script was tested and validated before it was use in the field.

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The user guide to the data set provides detailed tables on household and household member response rates for the Cote d’Ivoire smallholder household survey. A total of 3,415 households were selected for the survey, of which 3,109 were found to be occupied during data collection. Of these, 3,019 were successfully interviewed, yielding a household response rate of 97.1 percent.

In the interviewed households, 6,659 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,706 eligible household members, yielding a response rate of 85.7 percent for the Multiple Respondent questionnaire.

Among the 3,019 eligible household members selected for the Single Respondent questionnaire, 2,949 were successfully interviewed yielding a response rate of 97.7 percent.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Abstract

The CSES is a household survey with questions to households and the household members. In the household questionnaire there are a number of modules with questions relating to the living conditions, e.g. housing conditions, education, health, expenditure/income and labour force. It is designed to provide information on social and economic conditions of households for policy studies on poverty, household production and final consumption for the National Accounts and weights for the CPI.

The main objective of the survey is to collect statistical information about living standards of the population and the extent of poverty. Essential areas as household production and cash income, household level and structure of consumption including poverty and nutrition, education and access to schooling, health and access to medical care, transport and communication, housing and amenities and family and social relations. For recording expenditure, consumption and income the Diary Method was applied for the first time. The survey also included a Time Use Form detailing activities of household members during a 24-hour period.

Another main objective of the survey is also to collect accurate statistical information about living standards of the population and the extent of poverty as an essential instrument to assist the government in diagnosing the problems and designing effective policies for reducing poverty, and in evaluating the progress of poverty reduction which are the main priorities in the "Rectangular Strategy" of the Royal Government of Cambodia.

Geographic coverage

National Phnom Penh/Other Urban/Other Rural Provinces/Groups of provinces

Analysis unit

Households

Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

In this section the sampling design and the sample selection for CSES 2009, is described. The sampling design for the 2009 survey is the same as that used for the CSES 2004. The sampling design for the 2004 CSES is described in for instance National Institute of Statistics (2005a).

The sampling frame for the 2009 survey is based on preliminary data from the General Population Census conducted in 2008. The sample is selected as a three stage cluster sample with villages in the first stage, enumeration areas in the second stage and households in the third.

The Sampling Frame

Preliminary data from the General Population Census 2008 was used to construct the sampling frame for the first stage sampling, i.e. sampling of villages. All villages except 'special settlements' were included in the frame. In all, the first stage sampling frame of villages consisted of 14,073 villages, see Appendix 1. Compared to previous years the frame used for the 2009 survey based on the census 2008 was more up to date than in previous surveys which were based on the population census 1998.

The following variables were used from the census; Province code, province name, district code, district name, commune code, commune name, village code, village name, urban-rural classification of villages, the number of households per village and, the number of enumeration areas in the village.

In the second-stage Enumeration Areas (EA) are selected in each selected village. In most villages only one EA was selected but in some large villages more than one was selected.

For the third stage, the sampling of households, a frame was constructed in field. For selected EAs the census map of the village, including EAs and residences, was given to enumerator who updated the map and listed the households in the selected EA. A sample of households was then selected from the list.

Stratification

The sampling frame of villages was stratified by province and urban and rural. There are 24 provinces and each village is classified as either urban or rural which means that in total we have 48 strata, see Appendix 1. Each stratum of villages was sorted by district, commune and village code.

Sampling

The sampling design in the CSES 2009 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two. The sampling designs used in the three stages were:

Stage 1. A systematic pps sample of villages, Primary Sampling Units (PSUs) was selected from each stratum,

i.e. without replacement systematic sampling with probabilities proportional to size. The size measure used was the number of households in the village according to the sampling frame.

Stage 2. One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

As mentioned above, in a few large villages more than one EA was selected.

Stage 3. In each selected EA a sample of households was selected by systematic sampling.

The selection of villages and EAs were done at NIS while the selection of households in stage three was done in field. As mentioned in section 1.1 all households in selected EAs were listed by the enumerator. The sample of households was then selected from the list.

Sample sizes and allocation

The sample size of PSUs, were, as in the 2004 survey, 720 villages (or EAs). In urban villages 10 households were selected and in rural 20 households. In all 12,000 households were selected.

Urban and rural villages were treated separately in the allocation. The allocation was done in two steps. First the sample sizes for urban and rural villages in the frame were determined and then sample sizes for the provinces within urban and rural areas were determined, i.e. the strata sample sizes.

The total sample size was divided into to two, one sample size for urban villages and the other for rural villages. The calculation of the sample sizes for urban and rural areas were done using the proportion of consumption in the two parts of the population. Data on consumption from the CSES 2007 survey was used. The resulting sample sizes for urban villages was 240 and for rural 480. (Some adjustments of the calculated sample sizes were done, resulting in the numbers 240 and 480).

Allocation of the total sample size on the strata within urban and rural areas respectively, was done in the following way. The sample size, i.e. the number of PSUs, villages, selected from stratum h, is proportional to the number of households in stratum h, i.e.

n(Ih)=n1(Mh/Sum of Mh) (1.1)

where,

is the sample size in stratum h, i.e. the number villages selected in stratum h,

is the total sample size of villages for urban or rural villages,

H is the number of strata in urban or rural areas,

is the number of households in stratum h according to the frame.

As mentioned above, the sample size calculations are done separately for urban and rural villages, i.e. for strata with urban villages (1.1) is used with nI = 240 and is the number of households in urban villages in the frame and for rural villages (1.1) is used with nI = 480 and is the

number of households in rural villages in the frame.

Monthly samples

In section 1.3 the selection of the annual sample was described. The annual sample was divided into 12 monthly samples of equal sizes. The monthly samples consisted of 20 urban and 40 rural villages. The division of the annual sample into monthly samples was done so that as far as possible each province would be represented in each monthly sample. Since the sample size of villages in some provinces is smaller than 12, all provinces were not included in all monthly samples. Also, the outline of the fieldwork with teams of 4 enumerators and one supervisor puts constraints on how to divide the annual sample into monthly samples. The supervisors must travel between the villages in a team and therefore the geographical distance between the villages surveyed by a team cannot be too large.

Estimation

Totals, ratios such as means or proportions were estimated for the population or for subgroups of population, i.e. domains of study. The domains were defined by e.g. region or sex. Means and proportions were estimated by first estimating totals and then calculating the ratio of two estimated totals. To estimate totals from a sample survey weights are needed.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four different questionnaires or forms were used in the survey:

1. Household listing form

The Household listing and mapping were done prior to the sampling. During the household listing the enumerator recorded household information on e.g. location, number of members and principal economic activity.

1. Village questionnaire

The Village questionnaire was used to gather basic common information on:

    1. Demographic information

    2. Economy & Infrastructure

    3. Rainfall & Natural disasters

    4. Education

    5. Health

    6. Retail prices (food and non-food items)

    7. Employment & Wages

    8. Access to common property resources during the last 5 years

    9. Sale prices of agricultural land in the village

    10. Recruitment of children for work outside the village 

1. Household questionnaire

The following modules were included in the Household questionnaire:

1. Initial visit

01A. List of household member

01B. Food, beverages and tobacco consumption during the last 7 days

01C. Recall non-food expenditures

01D. Vulnerability

1. Education & Literacy

2. Information on migration (includes past and current migration)

   1. Housing

3. Household economic activities

05A.Land ownership

05B.Production of

Abstract

The Côte d'Ivoire Living Standards Survey (LSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (LSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN The principal objective of the sample selection process for the LSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households. A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time. It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

(b) SAMPLE FRAME 1. Sampling Procedures for Block 1 Data The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type. Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

1. Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census. Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.) Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The Household Questionnaire was almost entirely pre-coded, thus reducing errors involved in the coding process. Also, the decentralized data entry system allowed for immediate follow-up on inconsistencies that were detected by the data entry program. Household and personal identification codes were recorded in each section, facilitating merging data across sections

Sampling error estimates

(a) ACCURACY The general consensus is that the quality of the LSS household data is very good. An informal review of data quality conducted by Ainsworth and Mehra (1988) assessed the 1985 and 1986 LSS data in terms of their accuracy, completeness, and internal consistency. The LSS household data were found to score high marks on each of these three counts. One measure of data quality is the extent to which individuals in question respond for themselves during the interview, rather than having proxy responses provided for them by other household members. The investigation of CILSS household survey data for 1985 and 1986 showed that 93 percent of women responded for themselves to the fertility section and that 79 to 80 percent of all adult household members responded for themselves to the employment module. The percent of children responding for themselves to the employment module was far less, 43 to 45 percent. Nevertheless, these rates were found to be higher than for the Peru Living Standards Survey (29 percent).

(b) COMPLETENESS

Investigation of several variables and modules in the LSS (sex, age, parental characteristics, schooling, health, employment, migration, fertility, farming and family business), found that missing data in the household survey are rare. Rates for missing data were found to be close to 0 (0.01 to 0.05 percent) in many cases, but in any case, no higher than 0.76 percent.

Abstract

The main purpose of the KPMS surveys is to provide data for the study of multiple aspects of household welfare and behavior, analysis of poverty, and understanding the effect of government policies on households.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to expedite the survey process, NATSTATCOM used much of the same sample design and survey instruments as those used for the 1993 Baseline Survey. However, the Fall 1996-1998 KPMS surveys used a new sampling frame based on the Kyrgyz Household Registration System. This system was taken from the Census Posts intended for use by the first National Census of the Kyrgyz Republic. Using this system, NATSTATCOM updated the central household registration files effective January 1, 1996, and the information that was used for the sampling frame was as up to date as possible. The procedures followed in the stratification and identification of Primary Sampling Units (PSUs) were similar for all rounds of the KPMS as discussed below.

Formation of Strata

Initially the country was divided into seven (7) strata defined by oblasts (Oblasts are administrative divisions of the country which in turn are sub-divided in to Rayons) and by residence location (i.e. urban vs. rural) within oblasts. The rural portion of Bishkek oblast was combined with the rural portion of neighboring Chui oblast for stratification purposes as Bishkek has practically no rural population.

Selection of PSUs and Households

For the 1998 KPMS, a total of 255 PSUs (of which 178 were urban and 77 rural) were identified. The estimated total population was around 1.1 million of which about 421,000 was classified as urban. A minimum of 384 households per oblast was targeted in order to get a representative data at the oblast level11. This translated in to a targeted sample size of 2,688 households for the whole of the Kyrgyz Republic (i.e. 384*7 oblasts=2,688). These households were divided into urban (887 households) and rural (1,801 households). The overall projected response rate for the 1998 KPMS was also set at somewhat above 0.90. With an overall sampling rate of 1/336, this resulted in to a sample close to a target size of 3,000 households for the whole survey.

Once the strata and PSUs were formed and identified, selection of sample PSUs and households was then carried out in the following order:

1) Selection of large and small towns12 [Note: For the 1998 KPMS, large towns were defined as those with a population size of 41,125 or larger. Small towns are those with population less than 41,125. This number, according to a NATSTATCOM document was calculated as follows: n=4.7*350*25. This calculation was based on an estimated household size of 4.7, an estimated interval rate of 350 and an average work load per interviewer of 25 households. No further information is available regarding the bases of such an assumption. At the moment, we do not have information about the cut off number that separates large towns from small ones for the other two KPMS.]

2) Selection of Census Posts in urban areas

3) Selection of Ayil Kenshes (village authorities) and population points in rural areas, and

4) Selection of households from selected Census Posts and Ayil Kenshes. In the rural stratum of each oblast, villages were used as the listing units and within these listing units, equal probability sampling methods were used to select the ultimate sampling units (households). In urban areas, the centralized computer listings from various sources of household registration were used for the selection of households. These lists are categorized into four: Type 1 - Private house resident households listed by BTIs Type 2 - Public house residents listed with other organizations with dormitories only Type 3 - Public and private households listed by JSKs Type 4 - Public and private households listed by all other organizations. In some cases, private households were included in the last three public categories (Types 2, 3 and 4). However, only public households were selected from these types since it was believed that any private households listed in these category types were also included in the Type 1 category. The counts for Type 2, 3, and 4 lists were then adjusted based on the oblast estimates of all urban households.13 Prior to actual household sample selection, lists from types 2 to 4 were updated and adjusted to remove private households, so that any potential double eligibility was eliminated. Urban strata were then formed within each oblast based on type of household listing. In most cases, types had to be combined to form strata of a reasonable size.

Within the limits of rounding and requiring at least one sampling unit per stratum, the allocation of sampling units to urban strata was proportional to the number of households projected for that stratum after allowing for removal of duplicates (private households appearing on a BTI and other lists).

As for rural households, selection of urban households was done using systematic random sampling within each stratum except that more subdividing of urban lists was required before selecting the final list sample that defines each sampling unit.

Even though the list sources were identified and sampled using data as of January 1, 1996 (and using projections of unduplicated counts in some cases), the final listings were updated in the field just prior to the survey period. Therefore, the sample households in selected areas were drawn from the most current available listings.

Mode of data collection

Face-to-face [f2f]

Research instrument

The KPMS surveys were carried out using a household questionnaire and a community (population point) questionnaire. The household questionnaires were used to collect demographic information on the composition of the household, housing, household consumption including home production, as well as economic activities in agricultural and non-agricultural sectors. For each household member, individual level data on health, education, migration and labor was collected using the household questionnaires. Community questionnaires were used to collect price data and the presence of social services and infrastructure in the community (population point) where the sampled household is located.

The household questionnaire was extensive and required several hours of intense interviewing to gather all that was needed from each household and its embers. The household questionnaire was split into two parts. The first part was used to collect data through a face to face interview on household roster, dwelling, education, health, migration, etc. At the end of the first part, members who shop for food for the whole household and those who know most about income, expenditure and savings of other household members were identified and designated as respondents for the next part (second round). The second round of interview was administered two weeks after the first half and collected data on crops, food and animal products produced by the household, food expenditure and home produced food consumption.

Some sections of the household questionnaire such as those that deal with dwelling and expenditure information were administered to the person most knowledgeable of the family's overall expenditures, income and other finances as well as about the family's business activities and employment. In other sections, each adult in each sample household was interviewed individually. The information gathered from each household included extensive data on education, health, employment, migration, reproduction and reproductive health (for women aged 15 to 49), land use, expenditure, revenue and other financial matters, as well as anthropometric measurements (for children 5 years and younger). Information about children under 14 years of age was collected by asking the relevant questions to the adult household member who is primarily responsible for each child's care.

The community (Population Point) questionnaires were administered to each sample cluster. They were used to collect data on prices of goods and services, distance to schools, shopping and medical facilities, types of housing, commercial and private land use and availability of infrastructure.

HOUSEHOLD QUESTIONNAIRE

The KPMS household questionnaires generally contain 15 major sections, and each of these sections covers a separate aspect of household activity. In some cases, the section has sub-sections. These household questionnaires were designed to better assess the changing environment brought about by the advent of a market economy and to enable a more in depth analysis of topics such as housing, health, and education. The various sections of the KPMS household questionnaire are described below.The household questionnaires administered in the KPMS surveys are more or less similar with minor modifications and additions in the successive rounds of the KPMS.

POPULATION POINT QUESTIONNAIRE

The community (population point) questionnaire was used to collect information and data that are relevant to the community/population point where the household is located. The questionnaire was designed to be administered in the geographical area of each sample cluster. It was used to collect data regarding prices of goods and services in the local area and data on community infrastructure. Respondents to these questionnaires are those believed to be well informed members of the community that the interviewers identified by going to the rayon, city, oblast administration or other governmental agency located in the population point6. The

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 main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.