The statistic above provides the ranking of countries by median self-reported household income. Between 2006 and 2012, the median household income in Norway was about 51,489 U.S. dollars.

In 2023, around 10.3 percent of U.S. private households had an annual income between 35,000 and 49,999 U.S. dollars in the United States. Income levels between 100,000 to 149,999 U.S. dollars made up the largest share of the population at 16.5 percent in 2023.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database 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 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current State House Districts for New Mexico as posted on the Census Bureau website for 2006.

This table shows the number of private households with income and the corresponding average disposable income, broken down by main source of income. The figures are broken down by part of the country, province, corop area, metropolitan agglomeration, urban region and municipality. The data come from the Regional Income Survey (RIO) 2006 of Statistics Netherlands. The reference date is 1 January 2007; the income data relate to the 2006 survey year. These are provisional figures. Frequency: one-off Because the municipal division can change annually, the results from the RIO are published for each separate research year; merging or splitting municipalities means that all information related to income in a newly formed or split municipality can change considerably, so that comparability over time is not possible.

This statistic shows the mean cat owner household income in the United States from 2006 to 2016. In 2016, the mean household income for cat owners in the U.S. amounted to about ****** U.S. dollars.

Presents socio-demographic information of York Region’s population and is aggregated from Statistics Canada’s Census data. For reference purposes, York Region data is compared to those of Ontario, Canada, the Greater Toronto Area and York Region local municipalities.

Abstract

The 2006 Household Income and Expenditure Survey (2006 HIES) was initiated by Vanuatu National Statistical Office (VNSO) to review its income and expenditure patterns for the national accounts system, to update the Consumer Price Index (CPI) and subsequently revise its Gross Domestic Products (GDP).

Although the 2006 HIES is primarily designed to satisfy the data requirements of the Vanuatu NSO, it is also expected to provide benchmark data for the Millennium Challenge Accounts' (MCA's) infrastructure projects for its impact assessment on the rural economy.

The main objectives of the survey are:

(a) To obtain expenditure weights and other useful data for the up-dating of the basket and weight of the CPI; (b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts; (c) To supply benchmark data needed for assessment for MCA infrastructure projects; (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 supply basic data needed for policy making in connection with social and economic planning; and (f) To gather information on poverty lines and incidence of poverty for determining nutritional level of people.

Geographic coverage

There are eight main populations of interest for which estimates are required for the 2006 HIES: the provincial rural areas of Torba, Sanma, Penama, Malampa, Shefa, Tafea and the urban areas of Luganville and Port Vila. For this reason, the detailed analysis focuses on households from each of the eight sub-populations. Based on the 2006 Agricultural Census, 78 percent of the households are located in rural areas and 22 percent in urban areas.

Owing to cost and time constraints, some remote areas were not considered eligible for selection for the survey. Therefore the scope of the survey was reduced to 82.5 percent of all households in the population. Substantial reductions in scope occurred in Torba (62% in scope) and Malampa (68%) provinces. No enumeration areas were excluded in urban areas. While this may introduce some systematic bias, especially for the areas affected, the reduction of scope is not expected to affect the overall representativeness of the sample.

Analysis unit

Private Household, individuals and expenditure items

Universe

The survey coverage included only persons living in private households during the survey period (September to November 2006). Persons living in institutions, such as school dormitories, hospital wards, hostels, prisons, as well as those households which had temporarily vacated their dwellings were excluded from the survey. Also excluded from the survey were ex-patriates, temporary residents and permanent residents who were not residing (and intending to reside) in Vanuatu for at least 12 months.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling method adopted for the survey was a two-stage approach. The first stage involved the selection of Enumeration Areas (EA) using probability proportional to size (PPS) sampling. The size measure was the number of expected households in the EA, based on 2006 population census estimates. Although it would be desirable to cover all of Vanuatu for this survey, due to cost and time constraints some EAs were excluded from the frame before the selections were made. The impact on sub-population estimates will differ, as some areas have had larger scope reductions.

The second stage of sampling adopted systematic sampling from a list of all households contained in the EA. These lists were produced in the field by enumerators during the first visit to the EA. Once the sample had been selected, a review of where the selections were made was conducted to see how well they covered the projects of interest to the MCA. A total of nine additional EAs were selected to better cover some of the project areas which were not suitably represented by the original sample. A sample size of 4,532 households was adopted for the survey representing around 10 percent of the total households in Vanuatu.

Eight target areas were identified as sub-populations for which estimates would be desirable. These eight areas included the six provinces with separate target areas for the urban centres of Port Vila and Luganville. In order to achieve the required level of accuracy, different sample allocations were produced to determine which allocation would produce estimates of similar level accuracy for each target area. The sample allocation resulted in approximately 600 households selected for each province, except for Luganville and Torba where less than 500 households were selected.

Within each target area, further stratification was adopted in order to enhance suitable representation within each of the different area types. Strata were determined by allocating Area Councils to area types based on the Area Council's accessibility. As a result, 21 strata were created for the final sample. Sample allocation to each stratum was performed by allocating proportionally to the population within each “target area”. The sample weights were calculated for each stratum separately and were adjusted for non-response and benchmarked against household counts from the 2006 agricultural census.

Sampling deviation

Although it would be desirable to cover all of Vanuatu for this survey, due to cost and time constraints some EAs were excluded from the frame before the selections were made. The impact on sub-population estimates will differ, as some areas have had larger scope reductions. The estimated number of households removed from scope of the survey, with the percentage remaining, can be found in the table below:

The survey was conducted over a three month period beginning in the first week of September and finishing at the end of November 2006. (Some EAs that had not been enumerated as planned were later enumerated in December).

Mode of data collection

Face-to-face [f2f]

Research instrument

• Household Control Form (HCF)- was designed to list all the members of households, their date of birth, sex, maritial status relationship to the head and typ of activity the person is involved in

• Household Questionnaire Form - Part 1: Dwelling Characteristics, Access to Transport, Communication, Health, Sanitation and Market Centres, Part II: Household Expenditure, Part III: Income and Production

• Person Questionnaire Form - Captures information regarding health, education and economic activity for individual perosn

• Diary - Captures information regarding items bought, consumption of items, gifts and winnings from betting, riffles and lotteries by household

Cleaning operations

Some initial editing was carried when the forms were coded and prepared for data entry.

There were then several strands of editing carried out after the data entry was completed.

A set of tables designed to identify missing, illegal or potentially incompatible values in the classificatory data was specified.

The development of the “Generate new records” program, described above, required extensive examination of the data. First, it was sometimes necessary to examine original questionnaires to obtain a better understanding of how households responded to certain questions, especially when the recorded responses were unexpected. Second, the development of some of the imputation functions implemented in the program required analysis of detailed data. Third, testing of the program required examination of data before and after transformation to ensure that the program was carrying out its intended functions. These and other more minor reasons for examining the data collectively also played an important editing function, even though it was unstructured from an editing point of view. Most of the editing actions flowing from this work are recorded in Queries.xls.

Outlier analysis is an important part of the editing process for household surveys. For the HIES, formal outlier analysis has largely been confined to examining households with very high income or expenditure. However, outliers were also detected during the processes described in the previous paragraphs.

Imputations

Some obvious errors were fixed and missing data supplied manually at the time of the initial coding and checking of the questionnaires prior to the data entry stage. Similarly changes were made as a result of editing queries described in the previous section.

A more automated form of imputation was implemented for certain instances of missing data.

For those transactions recorded in diaries where a quantity was supplied without a value, a value was imputed on the basis of transactions in the same commodity in the same province/urban area. Consideration was given to imputing separately for each transaction type (purchases, own account production, gifts given, gifts received) but there is not sufficient data to use a cross classification of province/urban and transaction type. Examination of differences in unit values between provinces/urban areas and between transaction types showed greater differences between provinces/urban areas than between transaction types. Where there was no required data for a commodity in a particular province/urban area, the unit value from a similar province/urban area was used. Calculations are included in value and quantity by prov city 2.xls. Transaction values imputed in this way are flagged on the file by means of the “data source”

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database 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 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries.

This shapefile represents the current State Senate Districts for New Mexico as posted on the Census Bureau website for 2006.

This table shows the number of people in the Netherlands, the number of people with 52 weeks' income and the corresponding average disposable income. The figures are broken down by part of the country, province, corop area, metropolitan agglomeration, urban region and municipality. The data come from the Regional Income Survey (RIO) 2006 of Statistics Netherlands. The reference date is 1 January 2007; the income data relate to the 2006 survey year. These are provisional figures. Frequency: one-off Because the municipal division can change annually, the results from the RIO are published for each separate research year; merging or splitting municipalities means that all information related to income in a newly formed or split municipality can change considerably, so that comparability over time is not possible.

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.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This table contains data on household income and poverty status from the American Community Survey 2006-2010 database for tracts. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

The name for table 'ACS10INCTRMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

Key information about Switzerland Household Income per Capita

• Switzerland Annual Household Income per Capita reached 60,075.602 USD in Dec 2022, compared with the previous value of 61,553.105 USD in Dec 2021.
• Switzerland Annual Household Income per Capita data is updated yearly, available from Dec 2006 to Dec 2022, with an averaged value of 54,698.146 USD.
• The data reached an all-time high of 61,553.105 USD in Dec 2021 and a record low of 36,733.069 USD in Dec 2006.
• In the latest reports, Retail Sales of Switzerland dropped 1.378 % YoY in May 2023.

CEIC calculates Annual Household Income per Capita from annual Average Household Income and Average Household Size and converts it into USD. Swiss Federal Statistical Office provides Average Household Income in local currency and Average Household Size. Federal Reserve Board average market exchange rate is used for currency conversions.

A map showing the Median Income of Private Households from the 2006 Statistics Canada Census Data.Size: 11" x 17"Colour: Full ColourFormat: PDF

Datasource: Statistics Canada. 2008. Profile for Canada, Provinces, Territories, Census Divisions and Census Subdivisions, 2006 Census (table). Cumulative Electronic Profiles. Statistics Canada Catalogue no. 95F0495XCB01001. Ottawa. May 01, 2008. Version modified July 29,2008. http://www12.statcan.ca/english/census06/data/profiles/release/ListProdu... (accessed November 7, 2008). Statistics Canada. 2009. 2006 Semi-custom Profile of Yukon CSD Aggregations, 2006 Census (table). CRO0104245. Ottawa. March 17, 2009. Footnotes: A value of 0 in any given cell represents one of the following: 1) value is actually zero; 2) value may be random rounded to zero; or 3) value is more than zero but is suppressed for confidentiality reasons. This table is based on 20% data. Values have been subjected to a confidentiality procedure known as random rounding. For Statistics Canada's definition of terms, http://www12.statcan.ca/english/census06/reference/dictionary/atoz.cfm.

Real average total household incomes before taxes for homeowner households in Canada, the provinces and selected Census Metropolitan Areas (CMAs). This table gives housing professionals a summary of changes in before-tax household income for homeowners from 2006 to 2017. Data source: Statistics Canada, Canadian Income Survey 2012 – 2017, Survey of Labour and Income Dynamics 2006 – 2011

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

This table contains data on household income and poverty status from the American Community Survey 2006-2010 database for states. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

The name for table 'ACS10INCSTMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

Find housing data on average median before-tax total household income. We organized these housing statistics by: renters owners combined total of renters and owners The geographies included in the data tables are: Canada provinces selected Census Metropolitan Areas (CMAs) Note: Data timeline is from 2006 to 2019 Data sources: Statistics Canada, Canadian Income Survey 2012 – 2020, Survey of Labour and Income Dynamics 2006 – 2011.

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

This statistic shows the average total weekly household income in the United Kingdom (UK) from 2006 to 2014, by type (gross income and disposable income). In the period from 2006 to 2008 the average household enjoyed steady growth in both gross and disposable income. Since 2009 this growth trend has reversed, dropping from *** British pounds gross income per week in 2008 to *** British pounds in 2014.

These data tables focus on the real median total household income, before-taxes. They summarize the changes in before-tax household income by tenure: renters owners combined total of renters and owners The data tables also provide information on the real median total household incomes before taxes for households in: Canada Provinces selected Census Metropolitan Areas (CMAs)