The purpose of this project was to measure and estimate the distribution of personal income and related economic factors in both rural and urban areas of the People's Republic of China. The principal investigators based their definition of income on cash payments and on a broad range of additional components. Data were collected through a series of questionnaire-based interviews conducted in rural and urban areas at the end of 2002. There are ten separate datasets. The first four datasets were derived from the urban questionnaire. The first contains data about individuals living in urban areas. The second contains data about urban households. The third contains individual-level economic variables copied from the initial urban interview form. The fourth contains household-level economic variables copied from the initial urban interview form. The fifth dataset contains village-level data, which was obtained by interviewing village leaders. The sixth contains data about individuals living in rural areas. The seventh contains data about rural households, as well as most of the data from a social network questionnaire which was presented to rural households. The eighth contains the rest of the data from the social network questionnaire and is specifically about the activities of rural school-age children. The ninth dataset contains data about individuals who have migrated from rural to urban areas, and the tenth dataset contains data about rural-urban migrant households. Dataset 1 contains 151 variables and 20,632 cases (individual urban household members). Dataset 2 contains 88 variables and 6,835 cases (urban households). Dataset 3 contains 44 variables and 27,818 cases, at least 6,835 of which are empty cases used to separate households in the file. The remaining cases from dataset 3 match those in dataset 1. Dataset 4 contains 212 variables and 6,835 cases, which match those in dataset 2. Dataset 5 contains 259 variables and 961 cases (villages). Dataset 6 contains 84 variables and 37,969 cases (individual rural household members). Dataset 7 contains 449 variables and 9,200 cases (rural households). Dataset 8 contains 38 variables and 8,121 cases (individual school-age children). Dataset 9 contains 76 variables and 5,327 cases (individual rural-urban migrant household members). Dataset 10 contains 129 variables and 2,000 cases (rural-urban migrant households). The Chinese Household Income Project collected data in 1988, 1995, 2002, and 2007. ICPSR holds data from the first three collections, and information about these can be found on the series description page. Data collected in 2007 are available through the China Institute for Income Distribution.

[ARCHIVED] Community Counts data is retained for archival purposes only, such as research, reference and record-keeping. This data has not been maintained or updated. Users looking for the latest information should refer to Statistics Canada’s Census Program (https://www12.statcan.gc.ca/census-recensement/index-eng.cfm?MM=1) for the latest data, including detailed results about Nova Scotia.

This table reports household income before tax by income group. This data is sourced from the Census of Population (long form). Geographies available: provinces, counties, communities, municipalities, district health authorities, community health boards, economic regions, police districts, school boards, municipal electoral districts, provincial electoral districts, federal electoral districts, regional development authorities, watersheds

Abstract

The Statistics Division of the Department of National Planning (DNP/SD) conducts Household Income and Expenditure Surveys (HIES) in the Maldives. HIES 2009-2010 is the second such nationwide survey conducted in the country. 39 islands were randomly selected from all 20 Atolls and the capital Male' with a sample of 2,060 households.

The main objective of HIES is to produce reliable statistics on different components of income and expenditure of households in Male' and the Atolls to assess the economic well-being of the population. Specifically, the results will be used to bring about improvements in the national accounts, consumer price index and the vulnerability and poverty statistics of the country.

HIES results will be particularly essential and used for following purposes: • To show the most recent composition of consumption expenditure of households which will be used to update the CPI weights • To improve GDP estimates particularly for the components of final consumption expen diture of households, income and outlay and savings. • To measure living standard and indicate the gap between different social strata • To analyze distribution of households in terms of income groups and proper statistical measure of income inequality such as Gini coefficient. • To measure the poverty situation of households and update the existing poverty esti mates and indicators.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/ services

Kind of data

Sample survey data [ssd]

Sampling procedure

Required data for sampling were obtained from the population and housing census 2006. The country consists of 20 administrative atolls comprising of 194 inhabited islands. For political purpose these 20 administrative atolls are grouped as 7 regions. The capital Male' has separate administrative status. The frame for Male' consists of 6 wards and 324 enumeration blocks. HIES uses the area frame as a basis, to make the sample representative for the administrative and geographic structure of the country. All the inhabited islands have clearly marked census enumeration blocks, which were used in the sampling. Major characteristics of the HIES sampling frame are given below. A total of 880 blocks and 45,993 households were in the 194 inhabited islands of the country.

Note: Detailed sampling information is presented in APPENDIX ONE in the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were 8 different questionnaires. This includes: • Listing form (Form 1) is used to enumerate all the structures and households in the se lected Enumeration block in preparation for the actual household survey. One set of forms to be completed for each selected enumeration block. • Household form, (Form 2) consists of information on housing, household composition, household durables, and travel by members of the household, investment and financial status of household. One form has to be completed for each household. • Household member form (Individual form), (Form 3) consists of basic demographic char acteristics on all household members, education for those aged 6 years and above and identifies the labour force. One column on the form needs to be completed for each member of the household. • Employment and income form (Form 4) consists of information on employment and in come, one form to be completed for each member of the household who is aged fif teen years and over and who is working or is an income recipient. • Expenditure forms (Form 5) and, (Form 6), For Male' and the Atoll Islands, Form 5 is used to record the household expenditures and Form 6 to record the personal expendi tures of individual household members over the age of 15. Thus, a Form 5 will be filled for each household, while every individual member 15 years of age and above, who earns, fills a Form 6 to record his/her personal expenditure diary. • Summary form (Form 7) consist the summary information of the household. After all the information for the household and its members were received, this form was used to calculate the household income and expenditure and to calculate the expenditure per day and expenditure per person for a household. • ICT form (Form 8) consists of information related to the information communication technology (ICT). Accessibility, usage and expenditures on ICT by the household's mem bers aged 4 years and above were recorded in this form.

Sampling error estimates

Sampling Errors Sampling errors refers to the difference between the estimate based on a sample and its 'true' population value that would result if the whole population has been surveyed. The extent of sampling error of an estimate under a particular sample design is assessed by the variability of the estimate across all possible samples under the design. One common measure of this variability is given by the standard error (SE), which is the standard deviation of the sampling distribution of the estimate. Another measure is the relative standard error (RSE), which is obtained by expressing the standard error as a percentage to the estimate. The smaller the RSE, the more precise is the estimate.

The difference between standard error (SE) and relative standard error (RES) are that the standard error (SE) measure indicates the extent to which a survey estimate is likely to deviate from the true population and is expressed as a number. The relative standard error (RSE) is the standard error expressed as a fraction of the estimate and is usually displayed as a percentage. Estimates with a RSE of 25% or greater are subject to high sampling error and should be used with caution.

The reliability of estimates can also be assessed in terms of a confidence interval. Confidence intervals represent the range in which the population value is likely to lie. They are constructed using the estimate of the population value and its associated standard error. For example, there is approximately a 95% chance (i.e. 19 chances in 20) that the population value lies within two standard errors of the estimates, so the 95% confidence interval is equal to the estimate plus or minus two standard errors.

Note: Estimated sampling errors of some selected estimates in the HIES 2009/10 report are in Table 1.7.1.

Data appraisal

Sample surveys are limited in that they are assumed to represent the part of the population that was not included in the sample. Surveys have various sampling and non sampling errors, such an assumption may not always be correct. In the HIES 2009/2010 an important limitation is that no conclusions can be drawn from the information on the situation in any particular atoll; as the survey was designed to represent for Male' and at the 7 regions at the most disaggregated level. Also the regions in HIES 2009/2010 is different from previous HIES, hence the two HIESs is not comparable at regional level.

The survey design of HIES does not include resorts and industrial islands. Hence the direct incomes and expenditures of this particular population will not be accounted. If a person was not living in the household during the survey period the income the income of that person was recorded as transfer income. This limitation resulted in the employment in tourism industry lower compared to census 2006.

Abstract

The Household Income and Expenditure Survey is a survey collecting data on income, consumption and expenditure patterns of households, in accordance with methodological principles of statistical enquiries, which are linked to demographic and socio-economic characteristics of households. A Household Income and expenditure Survey is the sole source of information on expenditure, consumption and income patterns of households, which is used to calculate poverty and income distribution indicators. It also serves as a statistical infrastructure for the compilation of the national basket of goods used to measure changes in price levels. Furthermore, it is used for updating of the national accounts.

The main objective of the NHIES 2009/2010 is to comprehensively describe the levels of living of Namibians using actual patterns of consumption and income, as well as a range of other socio-economic indicators based on collected data. This survey was designed to inform policy making at the international, national and regional levels within the context of the Fourth National Development Plan, in support of monitoring and evaluation of Vision 2030 and the Millennium Development Goals. The NHIES was designed to provide policy decision making with reliable estimates at regional levels as well as to meet rural - urban disaggregation requirements.

Geographic coverage

National Coverage

Analysis unit

Individuals and Households

Universe

Every week of the four weeks period of a survey round all persons in the household were asked if they spent at least 4 nights of the week in the household. Any person who spent at least 4 nights in the household was taken as having spent the whole week in the household. To qualify as a household member a person must have stayed in the household for at least two weeks out of four weeks.

Kind of data

Sample survey data [ssd]

Sampling procedure

The targeted population of NHIES 2009/2010 was the private households of Namibia. The population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in the survey. However, private households residing within institutional settings were covered. The sample design for the survey was a stratified two-stage probability sample, where the first stage units were geographical areas designated as the Primary Sampling Units (PSUs) and the second stage units were the households. The PSUs were based on the 2001 Census EAs and the list of PSUs serves as the national sample frame. The urban part of the sample frame was updated to include the changes that take place due to rural to urban migration and the new developments in housing. The sample frame is stratified first by region followed by urban and rural areas within region. In urban areas further stratification is carried out by level of living which is based on geographic location and housing characteristics. The first stage units were selected from the sampling frame of PSUs and the second stage units were selected from a current list of households within each selected PSU, which was compiled just before the interviews.

PSUs were selected using probability proportional to size sampling coupled with the systematic sampling procedure where the size measure was the number of households within the PSU in the 2001 Population and Housing Census. The households were selected from the current list of households using systematic sampling procedure.

The sample size was designed to achieve reliable estimates at the region level and for urban and rural areas within each region. However the actual sample sizes in urban or rural areas within some of the regions may not satisfy the expected precision levels for certain characteristics. The final sample consists of 10 660 households in 533 PSUs. The selected PSUs were randomly allocated to the 13 survey rounds.

Sampling deviation

All the expected sample of 533 PSUs was covered. However a number of originally selected PSUs had to be substituted by new ones due to the following reasons.

Urban areas: Movement of people for resettlement in informal settlement areas from one place to another caused a selected PSU to be empty of households.

Rural areas: In addition to Caprivi region (where one constituency is generally flooded every year) Ohangwena and Oshana regions were badly affected from an unusual flood situation. Although this situation was generally addressed by interchanging the PSUs betweensurvey rounds still some PSUs were under water close to the end of the survey period. There were five empty PSUs in the urban areas of Hardap (1), Karas (3) and Omaheke (1) regions. Since these PSUs were found in the low strata within the urban areas of the relevant regions the substituting PSUs were selected from the same strata. The PSUs under water were also five in rural areas of Caprivi (1), Ohangwena (2) and Oshana (2) regions. Wherever possible the substituting PSUs were selected from the same constituency where the original PSU was selected. If not, the selection was carried out from the rural stratum of the particular region. One sampled PSU in urban area of Khomas region (Windhoek city) had grown so large that it had to be split into 7 PSUs. This was incorporated into the geographical information system (GIS) and one PSU out of the seven was selected for the survey. In one PSU in Erongo region only fourteen households were listed and one in Omusati region listed only eleven households. All these households were interviewed and no additional selection was done to cover for the loss in sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

The instruments for data collection were as in the previous survey the questionnaires and manuals. Form I questionnaire collected demographic and socio-economic information of household members, such as: sex, age, education, employment status among others. It also collected information on household possessions like animals, land, housing, household goods, utilities, household income and expenditure, etc.

Form II or the Daily Record Book is a diary for recording daily household transactions. A book was administered to each sample household each week for four consecutive weeks (survey round). Households were asked to record transactions, item by item, for all expenditures and receipts, including incomes and gifts received or given out. Own produce items were also recorded. Prices of items from different outlets were also collected in both rural and urban areas. The price collection was needed to supplement information from areas where price collection for consumer price indices (CPI) does not currently take place.

Cleaning operations

The questionnaires received from the regions were registered and counterchecked at the survey head office. The data processing team consisted of Systems administrator, IT technician, Programmers, Statisticians and Data typists.

Data capturing

The data capturing process was undertakenin the following ways: Form 1 was scanned, interpreted and verified using the “Scan”, “Interpret” & “Verify” modules of the Eyes & Hands software respectively. Some basic checks were carried out to ensure that each PSU was valid and every household was unique. Invalid characters were removed. The scanned and verified data was converted into text files using the “Transfer” module of the Eyes & Hands. Finally, the data was transferred to a SQL database for further processing, using the “TranScan” application. The Daily Record Books (DRB or form 2) were manually entered after the scanned data had been transferred to the SQL database. The reason was to ensure that all DRBs were linked to the correct Form 1, i.e. each household’s Form 1 was linked to the corresponding Daily Record Book. In total, 10 645 questionnaires (Form 1), comprising around 500 questions each, were scanned and close to one million transactions from the Form 2 (DRBs) were manually captured.

Response rate

Household response rate: Total number of responding households and non-responding households and the reason for non-response are shown below. Non-contacts and incomplete forms, which were rejected due to a lot of missing data in the questionnaire, at 3.4 and 4.0 percent, respectively, formed the largest part of non-response. At the regional level Erongo, Khomas, and Kunene reported the lowest response rate and Caprivi and Kavango the highest. See page 17 of the report for a detailed breakdown of response rates by region.

Data appraisal

To be able to compare with the previous survey in 2003/2004 and to follow up the development of the country, methodology and definitions were kept the same. Comparisons between the surveys can be found in the different chapters in this report. Experiences from the previous survey gave valuable input to this one and the data collection was improved to avoid earlier experienced errors. Also, some additional questions in the questionnaire helped to confirm the accuracy of reported data. During the data cleaning process it turned out, that some households had difficulty to separate their household consumption from their business consumption when recording their daily transactions in DRB. This was in particular applicable for the guest farms, the number of which has shown a big increase during the past five years. All households with extreme high consumption were examined manually and business transactions were recorded and separated from private consumption.

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.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

Abstract

The 2009-2010 HIES collected information on key topics such as family demography, education, health, employment and consumption. The scope of 2009-2010 HIES was broader than the previous survey, with more indicators and more households surveyed, including questions on living standards and other related subject areas. One key goal was to collect data on income and expenditure, and to enable a rebasing of the Consumer Price Index (CPI). This will assist future policy decisions and analysis to be better based on reliable evidence, such that they can better support improvements in the living standards of Papua New Guinea’s people.

As the 2009-2010 HIES was a multi-topic survey, the following types of information were collected:

1. Household level information on housing characteristics, ownership of consumer durables, non- food consumption, access to various types of public services, and incidence and resolution of different types of disputes.

2. Person-level information on age, sex, education, health, employment status, receipt of remittances, and personal security. This was supplemented by anthropometric data for children aged six years or younger.

3. Personal record of all food and nonfood purchases for 14 consecutive days for all household.

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design PNG is the largest nation in the South Pacific in both land area and population. It is comprised of around 600 islands, and the interior of the country is mountainous. Administratively, the country is divided into 22 provinces, within four geographic regions: Southern Region comprising of the following provinces: Western, Gulf, Central, Milne Bay, Northern Oro, and the National Capital District Highlands Region comprising of the following provinces: Southern Highlands, Enga, Western Highlands, Chimbu, Eastern Highlands, Hela, and Jiwaka Momase Region comprising of the following provinces: Moroboe, Madang, East Sepik, and West Sepik Island Region comprising of Manus, New Ireland, East New Britain, West New Britain, and the Autonomous Region of Bougainville.

A two-stage stratified cluster sample design was used in order to ensure independent estimates for the "rural", "urban", and "metro" areas of each of these regions. "Metro" denotes the two large urban centres of Port Moresby in Southern region and Lae in the Momase region. All the other urban areas in all regions were included in the "urban" stratum. The sample was thus divided into 10 strata. Sample size in each cluster was set to ensure reliable strata-level representative estimates; in addition, the sample size in the metropolitan areas was set large enough to support the development of a new CPI expenditure basket. In each stratum, households were chosen in two stages. In the first stage, using the sampling frame of the 2000 Census, clusters (or Primary Sampling Units) (PSU) were chosen using probability proportional to size. A full listing of households was done in all chosen clusters and a pre-set number of households were then selected randomly from this list. In the non-metropolitan areas, 18 households were chosen per cluster whereas in the metropolitan areas, 6 households were chosen per PSU. Replacement census units or replacement households in each cluster are used in the case of emergencies or when selected households could not be interviewed because of refusal, absence, illness in the family etc, were also selected using the same methodology.

Mode of data collection

Face-to-face [f2f]

Research instrument

The HIES is a complex application with a hierarchical set of questionnaires. For example, the main questionnaire consists of a household roster and other household information, and there are separate questionnaires for eligible members in the household. The data entry application may then contain two levels-one for the household and one for each eligible member in the household. The set of forms corresponding to the household, make up level one. The set of forms corresponding to each eligible member make up level two. Each case would consist of a level one and a variable number of level occurrences for level two. Most applications consist of a single level.

The survey questionnaires contain the following FORMS: 1) From A: Household Control Form 2) Form B: Household Schedule Form 3) Form C: Personal Schedule 4) Form D: Personal Diary 5) Form E: Personal Notepad

Abstract

The survey was conducted during December 2006, following an initial mini census listing exercise which was conducted about two months earlier in late September 2006. The objectives of the HIES were as follows: a) Provide information on income and expenditure distribution within the population; b) Provide income estimates of the household sector for the national accounts; c) Provide data for the re-base on the consumer price index; d) Provide data for the analysis of poverty and hardship.

Geographic coverage

National coverage: whole island was covered for the survey.

Analysis unit

• Household;
• Individual.

Universe

The survey covered all private households on the island of Nauru. When the survey was in the field, interviewers were further required to reduce the scope by removing those households which had not been residing in Nauru for the last 12 months and did not intend to stay in Nauru for the next 12 months. Persons living in special dwellings (Hospital, Prison, etc) were not included in the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size adopted for the survey was 500 households which allowed for expected sample loss, whilst still maintaining a suitable responding sample for the analysis.

Before the sample was selected, the population was stratified by constituency in order to assist with the logistical issues associated with the fieldwork. There were eight constituencies in total, along with "Location" which stretches across the districts of Denigamodu and Aiwo, forming nine strata in total. Although constituency level analysis was not a priority for the survey, sample sizes within each stratum were kept to a minimum of 40 households, to enable some basic forms of analysis at this level if required.

The sample selection procedure within each stratum was then to sort each household on the frame by household size (number of people), and then run a systematic skip through the list in order to achieve the desirable sample size.

Sampling deviation

No deviations from the sample design took place.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey schedules adopted for the Household Income and Expenditure Survey (HIES) included the following: · Expenditure questionnaire; · Income questionnaire; · Miscellaneous questionnaire; · Diary (x2).

Whilst a Household Control Form collecting basic demographics is also normally included with the survey, this wasn't required for this HIES as this activity took place for all households in the mini census.

Information collected in the four schedules covered the following: -Expenditure questionnaire: Covers basic details about the dwelling structure and its access to things like water and sanitation. It was also used as the vehicle to collect expenditure on major and infrequent expenditures incurred by the household. -Income questionnaire: Covers each of the main types of household income generated by the household such as wages and salaries, business income and income from subsistence activities. -Miscellaneous questionnaire: Covers topics relating to health access, labour force status and education. -Diary: Covers all day to day expenditures incurred by the household, consumption of items produced by the household such as fish and crops, and gifts both received and given by the household.

All questionnaires are provided as External Resources.

Cleaning operations

There were 3 phases to the editing process for the 2006 Household Income and Expenditure Survey (HIES) of Nauru which included: 1. Data Verification operations; 2. Data Editing operations; 3. Data Auditing operations.

The software used for data editting is CSPro 3.0. After each batch is completed the supervisor should check that all person details have been entered from the household listing form (HCF) and should review the income and expenditure questionnaires for each batch ensuring that all items have been entered correctly. Any omitted or incorrect items should be entered into the system. The supervisor is required to perform outlier checks (large or small values) on the batched diary data by calculating unit price (amount/quantity) and comparing prices for each item. This is to be conducted by loading the data into Excel files and sorting data by unit price for each item. Any changes to prices or quantities will be made on the batch file.

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

Response rate

The survey response rates were a lot lower than expected, especially in some districts. The district of Aiwo, Uaboe and Denigomodu had the lowest response rates with 16.7%, 20.0% and 34.8% respectively. The area of Location was also extremely low with a responses rate of 32.2%. On a more positive note, the districts of Yaren, Ewa, Anabar, Ijuw and Anibare all had response rates at 80.0% or better.

The major contributing factor to the low response rates were households refusing to take part in the survey. The figures for responding above only include fully responding households, and given there were many partial responses, this also brought the values down. The other significant contributing factor to the low response rates was the interviewers not being able to make contact with the household during the survey period.

Unfortunately, not only do low response rates often increase the sampling error of the survey estimates, because the final sample is smaller, it will also introduce response bias into the final estimates. Response bias takes place when the households responding to the survey possess different characteristics to the households not responding, thus generating different results to what would have been achieved if all selected households responded. It is extremely difficult to measure the impact of the non-response bias, as little information is generally known about the non-responding households in the survey. For the Nauru 2006 HIES however, it was noted during the fieldwork that a higher proportion of the Chinese population residing in Nauru were more likely to not respond. Given it is expected their income and expenditure patterns would differ from the rest of the population, this would contribute to the magnitude of the bias.

Below is the list of all response rates by district: -Yaren: 80.5% -Boe: 70% -Aiwo: 16.7% -Buada: 62.5% -Denigomodu: 34.8% -Nibok: 68.4% -Uaboe: 20% -Baitsi: 47.8% -Ewa: 80% -Anetan: 76.5% -Anabar: 81.8% -Ijuw: 85.7% -Anibare: 80% -Meneng: 64.3% -Location: 32.2% -TOTAL: 54.4%

Sampling error estimates

To determine the impact of sampling error on the survey results, relative standard errors (RSEs) for key estimates were produced. When interpreting these results, one must remember that these figures don't include any of the non-sampling errors discussed in other sections of this documentation

To also provide a rough guide on how to interpret the RSEs provided in the main report, the following information can be used:

Category  Description
RSE < 5%  Estimate can be regarded as very reliable
5% < RSE < 10% Estimate can be regarded as good and usable
10% < RSE < 25% Estimate can be considered usable, with caution
RSE > 25%  Estimate should only be used with extreme caution

The actual RSEs for the key estimates can be found in Section 4.1 of the main report

As can be seen from these tables, the estimates for Total Income and Total Expenditure from the Household Income and Expenditure Survey (HIES) can be considered to be very good, from a sampling error perspective. The same can also be said for the Wage and Salary estimate in income and the Food estimate in expenditure, which make up a high proportion of each respective group.

Many of the other estimates should be used with caution, depending on the magnitude of their RSE. Some of these high RSEs are to be expected, due to the expected degree of variability for how households would report for these items. For example, with Business Income (RSE 56.8%), most households would report no business income as no household members undertook this activity, whereas other households would report large business incomes as it's their main source of income.

Data appraisal

Other than the non-response issues discussed in this documentation, other quality issues were identified which included: 1) Reporting errors Some of the different aspects contributing to the reporting errors generated from the survey, with some examples/explanations for each, include the following:

a) Misinterpretation of survey questions: A common mistake which takes place when conducting a survey is that the person responding to the questionnaire may interpret a question differently to the interviewer, who in turn may have interpreted the question differently to the people who designed the questionnaire. Some examples of this for a Household Income and Expenditure Survey (HIES) can include people providing answers in dollars and cents, instead of just dollars, or the reference/recall period for an “income” or “expenditure” is misunderstood. These errors can often see reported amounts out by a factor of 10 or even 100, which can have major impacts on final results.

b) Recall problems for the questionnaire information: The majority of questions in both of the income and expenditure questionnaires require the respondent to recall what took place over a 12 month period. As would be expected, people will often forget what took place up to 12 months ago so some

Abstract

The Household Income and Expenditure Survey (HIES) is conducted by National Bureau of Statistics (NBS) with the most recent HIES conducted in 2016. In HIES 2016, 330 enumeration blocks were randomly selected from all 20 administrative Atolls and Male' with a sample of 4,985 households. HIES 2016 is the first such survey where the sample was designed in such a way that the results are representative at the level of each Atoll in addition to Male'. The survey was conducted in 172 administrative islands (excluding Male') in the country at the time. The high coverage of the islands and the resulting travel costs increased the total cost.

The first nationwide HIES conducted in 2002-2003 covered 834 households from the capital Male' and 40 islands randomly selected from all the Atolls. And the second national wide HIES was conducted in 2009-2010 covered 600 households from the capital Male' and 1,460 households from the islands randomly selected from all the Atolls.

NBS plans to conduct a nationwide HIES every 5 years in the future. Due to extensive revisions in the design of the survey instrument, results on poverty are not comparable to previous years.

Geographic coverage

The geographic domains of analysis for the HIES are the 21 atolls of the Maldives, as well as the national level. There is also interest in obtaining HIES results at the national level for the following administrative island size groups: (1) less than 500 population; (2) 501 to 1000 population; (3) 1001 to 2000 population; and (4) greater than 2000 population. Data were not collected in resort and industrial islands.

Analysis unit

household and individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the HIES 2016 is based on the summary data and cartography from the 2014 Maldives Population and Housing Census. The survey covers all of the household-based population in the administrative islands of each atoll of the Maldives, but excluded the institutional population (for example, persons in prisons, hospitals, military barracks and school dormitories).

A stratified two-stage sample design is used for the HIES. The primary sampling units (PSUs) selected at the first stage for the administrative islands are the enumeration blocks (EBs), which are small operational areas defined on maps for the 2014 Census enumeration. The average number of households per EB is 65.

Sampling deviation

Data were not collected in resort and industrial islands

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by the National Bureau of Statistics (NBS) in consultation with the World Bank (WB), International Labour Organization (ILO) and United National Economic and Social Commission for Asia and the Pacific (UNESCAP). Several meetings were conducted to discuss the HIES questionnaire during 2015, beginning with a data users workshop held on 22 April 2015. After conducting several pretests (K.Gulhu, K. Dhiffushi, K.Himmafushi, and Male') during the period June 2015 to January 2016, the questionnaire was finalized in January 2016.

In order to accommodate important data requirements of other government agencies, meetings were held with relevant personnel. In this regard focused discussions were held with Ministry of Tourism to incorporate the domes??c tourism into the HIES Questionnaire. Similarly, meetings were held with Ministry of Health to formulate the questions to capture details of health expenditure required to compile National Health Accounts.

During the HIES questionnaire design, International Labour Organization (ILO) provided the technical guidance in the development of Labour Force module, which was newly introduced in HIES 2016 according to the most recent ILO guidelines. World Bank (WB) provided the technical guidance to improve the methodology to better capture the poverty aspects, with a special focus on including questions relevant to capture the ownership of durable goods and their user value, capture food consumption and food away by a newly introduction food consumption module, and to better capturing the rental value of owner occupied housing. Technical experts from World Bank were involved in some of the pretests and during the questionnaire finalization process. United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), Statistics advisor provided overall technical guidance in development of the questionnaire, during the data users workshop and participated in initial pretests. This work was led by the technical team of NBS.

Cleaning operations

As the survey was on hold during the Ramadan period, the manual editing and coding of the 3 batch of the forms was carried out during Ramadan period. The coding of data started during June 2016 and was able to complete by the end of July 2016 using 10 coders who also worked as data collection officers in the survey. In order to reduce the coding errors and also to maintain consistency, 4 staff from the NBS was assigned as supervisors during the coding operation.

Coding of the second batch of the questionnaires started during December 2016 using 6 coders and additional staff from NBS were actively involved in the coding.

The classification used to code industry was International Standard Industrial Classification of all Economic Activities (ISIC) Rev. 4 and to code occupation, International Standard Classification of Occupation (ISCO) 08 was used. Classification of Individual Consumption According to purpose (COICOP), 2003 was used to give code for food and non-food items in the forms. COICOP codes were given at 7-digit level for food items and non-food items. Most of the COICOP was already pre-coded in the questionnaire and only few needed to be coded. Revision of the international Standard.

During the manual editing, all the questionnaires by household level were stamped together and assigned a serial number to the household which was provided by the data entry team. Form 4(Individual form) and Form 3 (Expenditure Unit form) information was verified with Form 2 (member listing form) information. Coders verified if all the members in Form 2 was recorded in Form 4. If the and sex was not filled in Form 4 (Individual form) than coders transferred this information from Form 2 to Form 4. In form 3 (expenditure unit form) if the expenditure unit number was missing this information also was transferred from form 2 to form 3. These checks were necessary to done before sending to data entry as Form 2 (member listing form) was decided not to enter. Classification of Education (ISCED) 39c/19, resolution 20 was used to identified the field of education. ISCED code was given at 4-digit level code with first two digits was from ISCED and last two digits was localized one code produced by the NBS to detail out the field of education. Atoll Island codes were the codes used in Census 2014. ISIC, ISCO and Atoll Island codes were in four-digit level.

Response rate

98.5% response rates for the number of sampled households

[ARCHIVED] Community Counts data is retained for archival purposes only, such as research, reference and record-keeping. This data has not been maintained or updated. Users looking for the latest information should refer to Statistics Canada’s Census Program (https://www12.statcan.gc.ca/census-recensement/index-eng.cfm?MM=1) for the latest data, including detailed results about Nova Scotia. This table reports median and average household income by household structure. This data is sourced from the Census of Population (long form). Geographies available: provinces, counties, communities, municipalities, district health authorities, community health boards, economic regions, police districts, school boards, municipal electoral districts, provincial electoral districts, federal electoral districts, regional development authorities, watersheds

VITAL SIGNS INDICATOR
Income (EC4)

FULL MEASURE NAME
Household income by place of residence

LAST UPDATED
January 2023

DESCRIPTION
Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 4Pb (1970)
Form STF3 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B19001 (2005-2021; household income by place of residence)
Form B19013 (2005-2021; median household income by place of residence)
Form B08521 (2005-2021; median worker earnings by place of employment)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Income derived from the decennial Census data reflects the income earned in the prior calendar year, whereas income derived from the American Community Survey (ACS) data reflects the prior 12 month period; note that this inconsistency has a minor effect on historical comparisons (see Income and Earnings Data section of the ACS General Handbook - https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020_ch09.pdf). ACS 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

Quintile income for 1970-2000 is imputed from decennial Census data using methodology from the California Department of Finance. Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index (CPI) for 2021 specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data uses national CPI for 1970. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 1999/2000 Household Income, Consurnption, and Expendi.ture Survey covered both the urban and the sedentary rural parts of the country. The survey has not covered six zones in Somalia Region and two zones in Afar Region that are inhabited mainly by nomadic population. For the purpose of the survey, the country was divided into three categories . That is, the rural parts of the country and the urban areas that were divided into two broad categories taking into account sizes of their population. Category I: Rural parts of nine Regional States and two administrative regions were grouped in this category each of which were the survey dornains (reporting levels). These regions are Tigrai,Afar, Amhara, Oromia, Sornalia, Eenishangul-Gunuz, SNNP,Gambela, Flarari, Addis Ababa and Dire Dawa.

Category II: All Regional capitals and five major urban centers of the country were grouped in this category. Each of the urban centers in this category was the survey domain (reporting level) for which separate survey results for rnajor survey characteristics were reported.

Category III: Urban centers in the country other than the urban centers in category II were grouped in this category and formed a single reporting level. Other than the reporting levels defined in category II and category III one additional domain, namely total urban (country level) can be constructed by eombining the basic domains defined in the two categories. All in all 35'basie rural and urban domains (reporting levels) were defined for the survey. In addition to the above urban and rural domains, survey results are to be reported at regional and eountry levels by aggregating the survey results for the conesponding urban and rural areas. Definition of the survey dornains was based on both technical and resource considerations. More specifically, sample size for the domains were determined to enable provision of major indicators with reasonable precision subject to the resources that were available for the survey.

Selection Scheme and Sample Size in Each Category CategoryI : A stratified two-stage sample design was used to select the sample in which the primary sampling units (PSUs) were EAs. Sample enumeration areas( EAs) from each domain were selected using systematic sampling that is probability proportional to the size being number of households obtained from the 1994 population and housing census.A total of 722 EAs were selected from the rural parts of the country. Within each sample EA a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the survey questionnaire 12 households per sample EA for rural areas were systematically selected.

Category II: In this category also,a stratified two-stage sample design was used to select the sample. Here a strata constitutes all the "Regional State Capitals" and the five "Major Urban Centers" in the country and are grouped as a strata in this category. The primary sampling units (PSUs) are the EA's in the Regional State Capitals and the five Major Urban Centers and excludes the special EAs (non-conventional households). Sample enumeration areas( EAs) from each strata were selected using systematic sampling probability proportional to size, size being number of households obtained from the 1994 population and housing census. A total of 373 EAs were selected from this domain of study. Within each sample EAs a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the questionnaire 16 household per sample EA were systematically selected-

Category III: Three-stage stratified sample design was adopted to select the sample from domains in category III. The PSUs were other urban centers selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. The secondary sampling units (SSUs) were EAs which were selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. A total of 169 sample EAs were selected from the sample of other urban centers and was determined by proportional allocation to their size of households from the 1994 census. Ultimately, 16 households within each of the sample EAs were selected systematically from a fresh list of households prepared at the beginning of the survey's fieldwork for the administration of the survey questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Household Income, Consumption and Expenditure Survey questionnaire contains the following forms: - Form 1: Area Identification and Household Characteristics - Form 2A: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for first and second week. - Form 2B: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for third and fourth week . - Form 3A: All transaction (income, expenditure and consumption) for the first and second weeks except what is collected in Forms 2A and 2B - Form 3B: All transaction (income, expenditure and consumption) for the third and fourth weeks except what is collected in Forms 2A and 2B - Form 4: All transaction (expenditure and consumption) for last 6 months for Household expenditure on some selected item groups - Form 5: Cash income and receipts received by household and type of tenure. The survey questionnaire is provided as external resource.

VITAL SIGNS INDICATOR
Income (EC4)

FULL MEASURE NAME
Household income by place of residence

LAST UPDATED
January 2023

DESCRIPTION
Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 4Pb (1970)
Form STF3 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B19001 (2005-2021; household income by place of residence)
Form B19013 (2005-2021; median household income by place of residence)
Form B08521 (2005-2021; median worker earnings by place of employment)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Income derived from the decennial Census data reflects the income earned in the prior calendar year, whereas income derived from the American Community Survey (ACS) data reflects the prior 12 month period; note that this inconsistency has a minor effect on historical comparisons (see Income and Earnings Data section of the ACS General Handbook - https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020_ch09.pdf). ACS 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

Quintile income for 1970-2000 is imputed from decennial Census data using methodology from the California Department of Finance. Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index (CPI) for 2021 specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data uses national CPI for 1970. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

Consolidated tax deduction details by decile of income tax return statistics Table in thousands of dollars

Abstract

The purpose of the HIES survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in FSM. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people. The 2005 FSM HIES asked income of all persons 15 years and over. It referred to income received during the calendar year 2004, and includes both cash and in-kind income. The survey has five primary objectives, namely to:

1) Rebase the FSM Consumer Price Index (CPI); 2) Provide data on the distribution of income and expenditures throughout the FSM; 3) Provide data for national accounts, particularly regarding income from home production activities and the consumption of goods and services derived form home production activities; 4) Provide nutritional information and food consumption patterns for the FSM families; and 5) Provide data for hardship study.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Expenditure items

Universe

The survey universe covered all persons living in their place of usual residence at the time of the survey. Income data were collected from persons aged 15 years and over while expenditure data were obtained from all household members at a household level. Persons living in institutions, such as school dormitories, hospital wards, hostels, prisons, as well as those whose usual residence were somewhere else were excluded from the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2005 FSM Household Income and Expenditure Survey (HIES) used a sampling frame based on updated information on Enumeration Districts (ED) and household listing from the 2000 FSM Census. Based on this sampling frame, the four states of FSM were then classified as the domains of the survey. Each of the states was further divided into 3 strata, except for Kosrae which was not divided at all because it doesn't possess any outer islands and it has relatively good access to goods and services. The entire island was therefore classified under stratum 1. Each stratum was defined as follows:

1) State center and immediate surrounding areas:
- High 'living standard' and has immediate access to goods and services.

2) Areas surrounding state center (rest of main island):
- Medium 'living standard' and sometime limited access to goods and services

3) Outer islands:
- Low 'living standard' and rare access to goods and services.

Within each stratum, the HIES used a two-stage stratified sampling approach from which the sample was selected independently. First, enumeration districts (EDs) were drawn from each stratum using Proportion Probability to Size (PPS) sampling. Thus, the larger the ED size, the higher its probability of selection. About 69 EDs out of a total of 373 EDs were selected nationwide for the survey. Generally, one enumerator is assigned to each ED. Second, 20 households were systematically selected from an updated household listing for each of the selected EDs using a random start to come up with a total sample size of 1,380 households, or roughly 8.4 percent of all households in the state. Although it offered a fairly good representation of the total households in the nation, the final sample size showed a reduction of nearly 180 households from the 1,560 households, or 10 percent, initially selected for the survey.

Detailed information on the changes made to the sample size can be found in the next section under "Deviations from Sample Design."

Sampling deviation

The original plan to sample 1,560 households, or about 9.5 percent of all households in the nation was eventually reduced to 1,380 households, or about 8.4 percent of all households. The reduction of the sample size was due to fuel unavailability for transportation and uncertainty of field trip schedules to some of the selected outer islands. Dropping some of these islands from the sample was not expected to impact significantly on the accuracy of the survey results because independent weighting took place within each stratum, where islands were considered to be sufficiently homogenous.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires and forms used for the 2005 FSM HIES consisted of 1) HIES Questionnaire and 2) Weekly Diaries. The HIES Questionnaire were provided to enumerators and should be filled out during the first visit to the household. Its main objective was to collect housing information, basic demographic information about members of the household and general household expenditures over the previous year. On the other hand, the weekly diaries, was an attempt to record household expenditure on a daily basis over the course of a 2 week period. Both the HIES questionnaire and the weekly diary were developed and modeled after similar forms from the 1998 FSM HIES Survey and the 2004 Palau HIES Survey. Dr. Micheal Levin from the US Census Bureau, International Program Center (IPC), Ms. Brihmer Johnson of the FSM Division of Statistics and Mr. Glenn McKinlay, statistics advisor to FSM Division of Statistics, provided crucial inputs to the overall design of these forms. All questionnaires and diaries used during the HIES were printed in English so it was extremely important that field interviewers understand the instuctions and questions contained within. Testing of the questionnaire were carried out by FSM Division of Statistics staffs who conducted "real" interviews with certain households in their neighborhood as well as having their own household be interviewed by a different office staff. Specific sections for both the HIES questionnaire and the weekly diaries are outlined below:

I. HIES Questionnaire

1) General Household characteristics 2 ) Individual Person Characteristics 3) General Expenditure Listings - 12 Months Recall Period

II. 2 Week Daily Diaries

1) Daily Expenditure Diary - Day1 (Mon) thru Day7 (Sun) 2) Home Produced Items 3) Gifts Given Away 4) Gifts Received 5) Unusual Expenses for Special Events

Cleaning operations

Data editing of the 2005 FSM HIES data occurred over several instances during the data processing phase of the project and afterwards prior to putting together the final report. After a two weeks office review and call backs right after the enumeration phase, the initial phase of data editing took place on July 18, 2005 when the data processing phase of the survey commenced. Training for editing and coding took place on the same day along with the signing of contracts for 10 office clerks recruited to carry out this phase of the survey. As part of their contract, these individuals were also hired to key in the data at a later time. One of their primary responsibily was to match geographic ids for questionnaire with corresponding diaries and ensure consistencies and valid entries accordingly. No computer consistency edit checks were run against the data during the keying/verification process since the programs for these processes were not available at the time. All data quality checks and edits were done at the US Bureau of Census. Further edits were applied to the data during the data analysis and report writing process.

There were five types of checks performed: Structural check, Verification check, Consistency check, Macro Editing check, Data Quality assessment. Edit lists were also produced for health module, income and expenditure questionnaire which needed to be checked against the questionnaires. On the edit list, corrections of errors were made by crossing out incorrect or missing values and entering the correct values in red. Missing amounts that were also missing on the questionnaire will need to be estimated using estimates from questionnaires in the same Enumeration District (ED) batch. For the diaries, the batch files were concatenated for each state and exported to tab delimited files. These files were imported into Excel and the unit price for each item was calculated using quantities and weights where possible. Records for each item were then filtered out and check for outlier unit price values (both large and small values as well as missing values). Values for missing amounts were imputed from estimated using average prices from the items within the same ED.

The office operations manual used for editing and coding the questionnaires and diaries is provided under "Technical Documents/Data Processing Documents/Office Editing & Coding."

Response rate

Original Sample Size: 1,560 Households Original Sampling fraction 9.5%

Final Sample Size: 1,380 Households Final Sampling fraction 8.4%

The response rate for the final sample size of 1,380 households is 100 percent. The majority of households originally selected for the survey did respond to the survey. Households which have moved to other unselected areas or elsewhere and those who refused to respond were replaced with nearby households that were willing to participate in the survey.

Sampling error estimates

No sampling error analysis of the survey was calculated.

Data appraisal

The questionnaire design of the 2005 HIES vary from that of the 1998 HIES rendering comparison of the data to the 2005 HIES limited. However, when the data permits, comparisons were made.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Abstract

The main purpose of a Household Income and Expenditure Survey (HIES) 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.

The main objectives of this survey - update the weight of each expenditure item (from COICOP) and obtain weights for the revision of the Consumer Price Index (CPI) for Funafuti - provide data on the household sectors contribution to the National Accounts - design the structure of consumption for food secutiry - To provide information on the nature and distribution of household income, expenditure and food consumption patterns household living standard useful for planning purposes - To provide information on economic activity of men and women to study gender issues - To generate the income distribution for poverty analysis

The 2010 Household Income and Expenditure Survey (HIES) is the third HIES that was conducted by the Central Statistics Division since Tuvalu gained political independence in 1978.

This survey deals mostly with expenditure and income on the cash side and non cash side (gift, home production). Moreover, a lot of information are collected:

at a household level: - goods possession - description of the dwelling - water tank capacity - fruits and vegetables in the garden - livestock

at an individual level: - education level - employment - health

Geographic coverage

National Coverage: Funafuti and /Outer islands.

Analysis unit

• Household level
• Individual level

Universe

The scope of the 2010 Household Income and Expenditure Survey (HIES) was all occupied households in Tuvalu. Households are the sampling unit, defined as a group of people (related or not) who pool their money, and cook and eat together. It is not the physical structure (dwelling) in which people live. HIES covered all persons who were considered to be usual residents of private dwellings (must have been living in Tuvalu for a period of 12-months, or have intention to live in Tuvalu for a period of 12-months in order to be included in the survey). Usual residents who are temporary away are included as well (e.g., for work or a holiday).

All the private household are included in the sampling frame. In each household selected, the current resident are surveyed, and people who are usual resident but are currently away (work, health, holydays reasons, or border student for example. If the household had been residing in Tuvalu for less than one year: - but intend to reside more than 12 months => he is included - do not intend to reside more than 12 months => out of scope.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tuvalu 2010 Household Income and Expenditure Survey (HIES) outputs breakdowns at the domain level which is Funafuti and Outer Islands. To achieve this, and to match the budget constraint, a third of the households were selected in both domains. It was decided that 33% (one third) sample was sufficient to achieve suitable levels of accuracy for key estimates in the survey. So the sample selection was spread proportionally across all the islands except Niulakita as it was considered too small. The selection method used is the simple random survey, meaning that within each domain households were directly selected from the population frame (which was the updated 2009 household listing). All islands were included in the selection except Niulakita that was excluded due to its remoteness, and size.

For selection purposes, in the outer island domain, each island was treated as a separate strata and independent samples were selected from each (one third). The strategy used was to list each dwelling on the island by their geographical position and run a systematic skip through the list to achieve the 33% sample. This approach assured that the sample would be spread out across each island as much as possible and thus more representative.

Population and sample counts of dwellings by islands for 2010 HIES Islands: -Nanumea: Population: 123; sample: 41 -Nanumaga: Population: 117; sample: 39 -Niutao: Population: 138; sample: 46 -Nui: Population: 141; sample: 47 -Vaitupu: Population: 298; sample: 100 -Nukufetau: Population: 141; sample: 47 -Nukulaelae: Population: 78; sample: 26 -Funafuti: Population: 791; sample: 254 -TOTAL: Population: 1827; sample: 600.

Mode of data collection

Face-to-face [f2f]

Research instrument

3 forms were used. Each question is writen in English and translated in Tuvaluan on the same version of the questionnaire. The questionnaire was highly based on the previous one (2004 survey).

Household Schedule This questionnaire, to be completed by interviewers, is used to collect information about the household composition, living conditions and is also the main form for collecting expenditure on goods and services purchased infrequently.

• composition of the household and demographic profile of each members
• dwelling information
• dwelling expenditure
• transport expenditure
• education expenditure
• health expenditure
• land and property expenditure
• household furnishing
• home appliances
• cultural and social payments
• holydays/travel costs
• Loans and saving
• clothing
• other major expenditure items

Individual Schedule There will be two individual schedules: - health and education - labor force (individual aged 15 and above) - employment activity and income (individual aged 15 and above): wages and salaries working own business agriculture and livestock fishing income from handicraft income from gambling small scale activies jobs in the last 12 months other income childreen income tobacco and alcohol use other activities seafarer

Diary (one diary per week, on a 2 weeks period, 2 diaries per household were required) The diaries are used to record all household expenditure and consumption over the two week diary keeping period. The diaries are to be filled in by the household members, with the assistance from interviewers when necessary. - All kind of expenses - Home production - food and drink (eaten by the household, given away, sold) - Goods taken from own business (consumed, given away) - Monetary gift (given away, received, winning from gambling) - Non monetary gift (given away, received, winning from gambling).

Cleaning operations

Consistency of the data: - each questionnaire was checked by the supervisor during and after the collection - before data entry, all the questionnaire were coded - the CSPRo data entry system included inconsistency checks which allow the National Statistics Office staff to point some errors and to correct them with imputation estimation from their own knowledge (no time for double entry), 4 data entry operators. 1. presence of all the form for each household 2. consistency of data within the questionnaire

at this stage, all the errors were corrected on the questionnaire and on the data entry system in the meantime.

• after data entry, the extreme amount of each questionnaire where selected in order to check their consistency. at this stage, all the inconsistency were corrected by imputation on CSPRO editing.

Response rate

The final response rates for the survey was very pleasing with an average rate of 97 per cent across all islands selected. The response rates were derived by dividing the number of fully responding households by the number of selected households in scope of the survey which weren't vacant.

Response rates for Tuvalu 2010 Household Income and Expenditure Survey (HIES): - Nanumea 100% - Nanumaga 100% - Niutao 98% - Nui 100% - Vaitupu 99% - Nukufetau 89% - Nukulaelae 100% - Funafuti 96%

As can be seen in the table, four of the islands managed a 100 per cent response, whereas only Nukufetau had a response rate of less than 90 per cent.

Further explanation of response rates can be located in the external resource entitled Tuvalu 2010 HIES Report Table 1.2.

Sampling error estimates

The quality of the results can be found in the report provided in this documentation.

Abstract

The purpose of this survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in the Republic of Palau. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people.

Some more specific outputs from the survey are listed below: a) To obtain expenditure weights and other useful data for the revision of consumer price indices. b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts. c) To supply basic data needed for policy making in connection with social and economic planning d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption e) To gather information on poverty lines and incidence of poverty throughout Palau.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

All private households.

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

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

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

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

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

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

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

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

Response rate

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

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

Sampling error estimates

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

The estimates for Total Income and Total Expenditure from the HIES can be considered to be very good, from a sampling error perspective. The same can also be said for the Wage and Salary estimate in income and the Food estimate in expenditure, which make up a high proportion of each respective group.

Some of the other estimates should be used with caution, depending on the magnitude of their RSE. Some of these high RSEs are to be expected, due to the expected degree of variability for how households would report for these items. For example, with Business Income (RSE 30.1%), most households would report no business income as no household members undertook this activity, whereas other households would report large business incomes as it’s their main source of income.

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

Data appraisal

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

This analysis, produced by the Office for National Statistics (ONS), examines how taxes and benefits redistribute income between various groups of households in the United Kingdom. It shows where different types of households and individuals are in the income distribution and looks at the changing levels of income inequality over time. The main sources of data for this study are:

• Family Expenditure Survey (FES) from 1977-2001
• Expenditure and Food Survey (EFS) from 2001-2007
• Living Costs and Food Survey (LCF) from 2008 onwards

Some variables have been created by combining data from the LCF (previously FES or EFS) with control totals from a variety of different government sources, including:

• United Kingdom National Accounts (ONS Blue Book)
• HM Revenue and Customs (HMRC)
• Department for Transport (DfT)
• Department of Health (DH)
• Department for Education and Employment (DfEE)
• Department for Communities and Local Government (DCLG)

The Effects of Taxes and Benefits on Household Income (ETB) has been produced each year since 1961 and is an annual analysis looking at how taxes and benefits affect the income of households in the UK. The estimates in this analysis are based mainly on data derived from the LCF Survey, which replaced the Family Expenditure Survey (FES) from 2001/02, and was known as the EFS until 2008. The LCF is an annual survey of the expenditure and income of private households. People living in hotels, lodging houses, and in institutions such as old people’s homes are excluded. Each person aged 16 and over keeps a full record of payments made during 14 consecutive days and answers questions about hire purchase and other payments; children aged 7 to 15 keep a simplified diary. The respondents also give detailed information, where appropriate, about income (including cash benefits received from the state) and payments of Income Tax. Information on age, occupation, education received, family composition and housing tenure is also obtained. The survey is continuous, interviews being spread evenly over the year to ensure that seasonal effects are covered. The Family Spending publication also includes an outline of the survey design.

The LCF data used in this analysis are grossed so that totals reflect the total population of private households in the UK. The weights are produced in two stages. First the data is weighted to compensate for non-response (sample-based weighting). The non-response weights are then calibrated so that weighted totals match population totals for males and females in different age groups and for different regions and countries (population-based weighting). The results in the analysis are weighted so that statistics represent the total population in private households in the UK based on 2011 Census data. In 2013/14, an additional calibration to Labour Force Survey (LFS) employment totals was also applied.

There are a number of different measures of income used, the most common of which is probably household disposable income. This is the total income households receive from employment (including self-employment), income from private pensions, investments and other sources, plus cash benefits (including the state pension), minus direct taxes (including income tax, NI and council tax). Income is normally analysed at the household level as this provides a better measure of people's economic well-being; while income is usually received by individuals, it is normally shared with other household members (e.g. spouse/partner and children).

In 2018/19 a further adjustment was applied to the data to adjust for the under coverage and under-reporting of income of the richest individuals. This method is often referred to as the 'SPI adjustment' owing to its use of HM Revenue and Customs (HMRC's) Survey of Personal Incomes (SPI). For further details please see the ETB Quality and methodology information webpage and the Effects of taxes and benefits on household income technical report.

The Living Costs and Food Survey (LCF) is the source of the microdata on households from 2008-09 onwards. Previously, the Expenditure and Food Survey (EFS) was the data source. Derived variables are created using information from LCF and control totals from a variety of different government sources including the United Kingdom National Accounts (ONS Blue Book), HM Revenue and Customs, Department for Transport, Department of Health, Department for Education and Employment, and Department for Communities and Local Government.

For further information, see the ONS Effects of taxes and benefits on household income webpage.

Variables available in the Secure Access version
The Secure Access version of the ETB datasets include additional variables not included in the standard End User Licence (EUL) versions (available under GN 33299). Extra variables include:

• CASENO (case number): all years
• CESAGE (age of chief economic supporter): 1991-2015
• CESEMPST (economic position of chief economic supporter): 1991-2015
• GGOR (Government Office Region): 2000-2015
• CES (chief economic supporter flag): 2001-2015

Prospective users of a Secure Access version of the ETB will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version.

The second edition (June 2021) includes data files for 2016/17, 2017/18 and 2018/19. The documentation has been updated accordingly.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.