Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for New York County, NY (MWACL36061) from 2009 to 2023 about New York County, NY; adjusted; New York; average; NY; wages; real; and USA.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

In 2023, the United States had the highest gross household disposable income per capita in OECD countries adjusted for purchasing power parity. Their disposable income per capita was over 58,000 U.S. dollars. Luxembourg followed in second with around 50,500 U.S. dollars, with Switzerland in third.

Of the most populous cities in the U.S., San Jose, California had the highest annual income requirement at 288,953 U.S. dollars annually for homeowners to have an affordable and comfortable life in 2024. This can be compared to Houston, Texas, where homeowners needed an annual income of 87,991 U.S. dollars in 2024.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

Abstract copyright UK Data Service and data collection copyright owner.

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 small area model-based income estimates are the official estimates of average (mean) household income at the middle layer super output area (MSOA) level in England and Wales for 2011/12, 2013/14 and 2015/16. For 2015-16 the figures are average annual income. For 2013/14 and 2011/12 the figures are average weekly income. They are calculated using a model based method to produce the following four estimates of income using a combination of survey data from the Family Resources Survey, and previously published data from the 2011 Census and a number of administrative data sources. The four different measures of income are: Total household income Net household income Net household income (equivalised) before housing costs Net household income (equivalised) after housing costs Total annual household income is the sum of the gross income of every member of the household plus any income from benefits such as Working Families Tax Credit. Net annual household income is the sum of the net income of every member of the household. It is calculated using the same components as total income but income is net of: income tax payments; national insurance contributions; domestic rates/council tax; contributions to occupational pension schemes; all maintenance and child support payments, which are deducted from the income of the person making the payments; and parental contribution to students living away from home. Net annual household income before housing costs (equivalised) is composed of the same elements as net household weekly income but is subject to the OECD’s equivalisation scale. Net annual household income after housing costs (equivalised) is composed of the same elements of net household weekly income but is subject to the following deductions prior to the OECD’s equivalisation scale being applied: rent (gross of housing benefit); water rates, community water charges and council water charges; mortgage interest payments (net of any tax relief); structural insurance premiums (for owner occupiers); and ground rent and service charges. For detailed information on aspects of the quality and methodology behind these statistics, see the Technical Report. This dataset is included in the Greater London Authority's Night Time Observatory. Click here to find out more.

The annual salary received by members of the United States Congress in 2025 is 174,000 U.S. dollars. This has been the case since 2009. The Government Ethics Reform Act of 1989 provides an automatic cost of living adjustment increase in line with the

This table shows the opinion of households about their income position and the extent to which they experience financial constraints. The extent to which households can make ends meet on disposable income, the difficulty they have with paying monthly housing costs and with repaying loans and repaying items purchased on installment are discussed. The table also contains information about being able to pay for certain common goods and services, such as clothes or an annual holiday, and being in arrears with payments. A revision of the Income Statistics has led to a downward adjustment of the number of low-income households. An important adjustment is the revision of the economic rental value. In the European Union-Statistics on Income and Living Conditions (EU-SILC) study, the source for data on the subjective assessment of income, the new methodology has been applied from 2016. In 2016, this led to a downward adjustment of the number of low-income households. In addition, the composition of this group has changed: the adjustment resulted in a smaller share of homeowners among low-income households. Compared to tenants, homeowners say they are better able to get by on their income and they experience financial constraints less often. Households that have a low income on the basis of the revised income data therefore more often report payment arrears and experience financial tightness more often than households at risk of poverty according to the old method. The derivation method of the household's main source of income has been adjusted from 2018. Before 2018, income from own business was always regarded as the main source on the basis of a priority rule. If another source of income, for example wages, comprises a substantial amount and if this amount exceeds the income from self-employment, then wages and no longer income from self-employment constitute the main source of household income according to the new methodology. The new derivation rule means that the number of households with income from their own business as their main source of income will be almost halved. Fieldwork in 2022 ran from March to July. Although energy prices were already rising at that time, energy prices were still relatively low compared to the second half of 2022. In addition, some households still had to deal with advances/tariffs under existing contracts. As a result, the consequences of the energy crisis will not be fully visible in the figures for 2022 and in particular in the perceived housing costs. Data available from: 2005. Status of the figures: The figures in this table are final up to and including 2021. The figures for 2022 are provisional. Changes as of February 28, 2023: Figures for 2021 and 2022 added The categories "Source: social security benefits: other" and "Source: income from own business" have been removed because no data is available for the entire period. When will new numbers come out? March 2024.

Based on the government's forecast from October 2024, the real household disposable income per person in the United Kingdom fell by 2.1 percent in the 2022/23 fiscal year, the biggest fall in living standards since 1956 when this type of data was first produced. Living standards did, however, rise in 2023/24 by approximately two percent.

The purpose of this project was to measure and estimate the distribution of personal income 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: payments in kind valued at market prices, agricultural output produced for self-consumption valued at market prices, the value of food and other direct subsidies, and the imputed value of housing services. The rural component of this collection consists of two data files, one in which the individual is the unit of analysis (Part 1) and a second in which the household is the unit of analysis (Part 2). Individual rural respondents reported on their employment status, level of education, Communist Party membership, type of employer (e.g., public, private, or foreign), type of economic sector in which they were employed, occupation, whether they held a second job, retirement status, monthly pension, monthly wage, and other sources of income. Demographic variables include relationship to householder, gender, age, and student status. Rural households reported extensively on the character of the household and residence. Information was elicited on type of terrain surrounding the house, geographic position, type of house, and availability of electricity. Also reported were sources of household income (e.g., farming, industry, government, rents, and interest), taxes paid, value of farm, total amount and type of cultivated land, financial assets and debts, quantity and value of various crops, amount of grain purchased or provided by a collective, use of chemical fertilizers, gasoline, and oil, quantity and value of agricultural machinery, and all household expenditures (e.g., food, fuel, medicine, education, transportation, and electricity). The urban component of this collection also consists of two data files, one in which the individual is the unit of analysis (Part 3) and a second in which the household is the unit of analysis (Part 4). Individual urban respondents reported on their economic status within the household, Communist Party membership, sex, age, nature of employment, and relationship to the household head. Information was collected on all types and sources of income from each member of the household whether working, nonworking, or retired, all revenue received by owners of private or individual enterprises, and all in-kind payments (e.g., food, durable goods, and nondurable goods). Urban households reported total income (including salaries, interest on savings and bonds, dividends, rent, leases, alimony, gifts, and boarding fees), all types and values of food subsidies received, and total debt. Information was also gathered on household accommodations and living conditions, including number of rooms, total living area in square meters, availability and cost of running water, sanitary facilities, heating and air-conditioning equipment, kitchen availability, location of residence, ownership of home, and availability of electricity and telephone. Households reported on all their expenditures including amounts spent on food items such as wheat, rice, edible oils, pork, beef and mutton, poultry, fish and seafood, sugar, and vegetables by means of coupons in state-owned stores and at free market prices. Information was also collected on rents paid by the households, fuel available, type of transportation used, and availability and use of medical and child care. 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.

Abstract copyright UK Data Service and data collection copyright owner.

The Family Expenditure Survey (FES), which closed in 2001, was a continuous survey with an annual sample of around 10,000 households. They provided information on household and personal incomes, certain payments that recurred regularly (e.g. rent, gas and electricity bills, telephone accounts, insurances, season tickets and hire purchase payments), and maintained a detailed expenditure record for 14 consecutive days.

The original purpose of the FES was to provide information on spending patterns for the United Kingdom Retail Price Index (RPI). The survey was a cost-efficient way of collecting a variety of related data that the government departments required to correlate with income and expenditure at the household, tax unit and person levels. The annual FES began in 1957 (with an earlier large scale survey conducted in 1953/54) and was one of the first Department of Employment (DE) systems to be computerised in the early 1960s. The UKDA holds FES data from 1961-2001. The Northern Ireland Family Expenditure Survey (NIFES), which ran from 1967-1998, was identical to the UK FES and therefore used the same questionnaires and documentation. However, starting in 1988, a voluntary question on religious denomination was asked of those aged 16 and over in Northern Ireland. The UKDA holds NIFES data from 1968-1998, under GN 33240.

Significant FES developments over time include:

• 1968: the survey was extended to include a sample drawn from the Northern Ireland FES and a new computer system was introduced which was used until 1985
• 1986: DE and the Office of Population Censuses and Surveys (OPCS) converted the FES into a new database system using the SIR package
• 1989: the Central Statistical Office (CSO) took over responsibility for the survey
• 1994: in April, computerised personal interviewing was introduced using lap-top computers, the database system changed to INGRES and the survey changed from a calendar year to financial year basis
• 1996: in April, OPCS and CSO were amalgamated into the Office for National Statistics (ONS), who assumed responsibility for the FES
• 1998: from April onwards information from expenditure diaries kept by children aged 7 to 15 was included in data, and grossing factors were made available on the database

From 2001, the both the FES and the National Food Survey (NFS) (held at the UKDA under GN 33071) were completely replaced by a new survey, the Expenditure and Food Survey (EFS). Prior to the advent of the EFS, there had previously been considerable overlap between the FES and NFS, with both surveys asking respondents to keep a diary of expenditure. Thus, the 2000-2001 FES was the final one in the series. The design of the new EFS was based on the previous FES; further background to its development may be found in the 1999-2000 and 2000-2001 Family Spending reports. From 2008, the EFS became the Living Costs and Food Survey (LCF) (see under GN 33334).


Main Topics:
Household Schedule:
This schedule was taken at the main interview. Information for most of the questions was obtained from the head of household or housewife, but certain questions of a more individual character were put to every spender aged 15 or over (or 16 or over from 1973 onwards). Until the introduction of the community charge, information on rateable value and rate poundage was obtained from the appropriate local authority, as was information on whether the address was within a smokeless zone. Information was collected about the household, the sex and age of each member, and also details about the type and size of the household accommodation. The main part of the questionnaire related to expenditure both of a household and individual nature, but the questions were mainly confined to expenses of a recurring nature, e.g.:

• Household: housing costs, payment to Gas and Electricity Boards or companies, telephone charges, licences and television rental
• Individual: motor vehicles, season tickets for transport, life and accident insurances, payments through a bank, instalments, refund of expenses by employer, expenditure claimed by self-employed persons as business expenses for tax purposes, welfare foods, education grants and fees

Income Schedule:
Data were collected for each household spender. The schedule was concerned with income, national insurance contributions and income tax. Income of a child not classed as a spender was obtained from one or other of his parents and entered on the parent's questionnaire. Information collected included: employment status and recent absences from work, earnings of an employee, self-employed earnings, National Insurance contributions, pensions and other regular allowances, occasional benefits - social security benefits and other...

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2023. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 117.5 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

Abstract

The main objective of the survey is to assess the living conditions of the population of St Lucia and to develop a national basket of goods and services for updating of the consumer price index. The survey contains information on housing conditions, cost of accomodation, cost of rountine household maintenance and repairs, annual cost of purching furniture and furishings for the household, cost of vehicle operations, where items are purchased, migration, anthopometric data, demographics, health, education, labour force, crime, clothing expenses, health expenses and income.

Geographic coverage

National coverage, all Administrative Districts

Analysis unit

Individuals, households, spenders (defined as persons age 18 and over and employed)

Universe

The survey covered all de jure household members (usual residents), the fertility section of the person questionnaire covers all women aged 15-49 years resident in the household, the anthropometic section covers all children aged 0-4 years (under age 5) resident in the household and the expenditure data covers all spenders 18 year of age and over and employed.

Kind of data

Sample survey data [ssd]

Sampling procedure

1302 households were selected for the sample. Of these, 1222 were occupied households and were successfully interviewed for a response rate of 94%. Within these households, 4319 persons were successfully interviewed (response rate 93.9%).

The stratification is done by district and is based on the percentage of agricultural workers for rural EDs (Enumeration Districts) and percentage of professional workers for urban EDs. There are two stages of selection, firstly the selection of EDs in all Districts then the selection of households using a random start and systematic selection proceedure. Households which refused or could not be contacted were replaced.

The sample frame used was based on the May 2001 Census and the sample size was 2.78% of the frame. Stratification was done on the district (District) and then by ED (Enumeration District) and finally by household (hhno).

Sampling deviation

There was no deviation from the sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four Questionnaires were administered to each household; each household was visited at least three times. On the first visit the Household and the Individual Questionnaires were administered. At the start of the first week one Daily Diary of Expenditure Questionnaire for the household and a Memory Jogger notebook for each spender in the household was left with household respondents to record all purchases over the next one-week period. The 2nd visit to the household occurred at the end of the first week at which point the Daily Diary completed by the household for the first week and all memory jogger notebooks were collected and the second week’s Diary and memory joggers were left. The 3rd visit to the household occurred at the end of the second week at which point the Daily Diary completed by the household and memory joggers from each spender for the second week was collected.

THE HOUSEHOLD EXPENDITURE SURVEY QUESTIONNAIRES

There are four (4) questionnaires to be administered in the survey. 1. S.L.C.H.B Household Questionnaire 2. S.L.C.H.B Individual/Person Questionnaire 3. S.L.C.H.B Memory Jogger 4. S.L.C.H.B Household Daily Diary of Expenditure

THE STRUCTURE OF THE QUESTIONNAIRES Household Questionnaire Front Page: Identification and control Section 1: Housing conditions and household assets Section 2 Part 1,2,3: Expenditure on accommodation, owned and rented Section 2 Part 4: Expenditure on accommodation - Repair and maintenance of dwelling Section 3 : Major types of household expenses Section 4 Part 1: Furniture, furnishings and household equipment Section 4 Part 2: Repairs and servicing of household articles Section 5 : Agriculture products produced and consumed at home Section 6: Transportation Section 7: Regularity of purchase and main type of outlet Section 8: For Heads of households only (Status of previous household head) Section 9: To be completed for all former household members living away from the household in the past five years Section 10: For children under the age of five years

Person Questionnaire
Control: Identification and control Section 1: Characteristics – For all persons Section 2: Migration – For all persons Section 3: Health – For all persons Section 4: Education – For all persons Section 5: Employment – For person 15 years and over Section 6: Marital, union status and fertility for persons – For persons over the age of 15 years Section 7: Crime Section 8: Clothing and footwear consumed in the last 3 months Section 9: Other expenses Section 10: Other Disbursements Section 11: Income

Memory Jogger
Front Page: Identification and control Daily Record: Pages 1 to 7 Back Page: Notes on the method of completing the daily diary

Daily Diary of Expenditure
Front Page: Identification and control Pages 2 – 3: Notes on the method of completing the daily diary Example: Example of method of completion (Pages 4, 5, 6) Day One: Daily expenditures (Pages 7, 8, 9, 10) Day Two: Daily expenditures (Pages 11, 12, 13, 14) Day Three: Daily expenditures (Pages 15, 16, 17, 18) Day Four: Daily expenditures (Pages 19, 20, 21, 22) Day Five: Daily expenditures (Pages 23, 24, 25, 26) Day Six: Daily expenditures (Pages 27, 28, 29, 30) Day Seven: Daily expenditures (Pages 31, 32, 33, 34)

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data capture which involved the scanning and verification of the data c) Structure checking and completeness was done in SQL 2000 Enterprise Server d) Secondary editing was done in SPSS e) Structural checking of SPSS data files Detailed documentation of the editing of data can be found in the "Data Editing and coding guidelines" document provided as an external resource.

Response rate

Response rates by Administrative District follow: Castries Urban: 98.5% Castries Rural: 94.8% Anse-La-raye/Canaries: 98.4% Soufriere: 86.7% Choiseul: 100.0% Laborie: 94.5% Vieuxfort: 89.1% Micoud: 88.1% Dennery: 97.0% GrosIslet: 92.4%

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the SLC/HBS 2005-2006 sample is the result of a multi-stage stratified design, and consequently needs to use more complex formulae. The CENVAR module of the IMPS 4.1 has been used to calculate sampling errors for the SLC/HBS 2005-2006. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions.

Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Details of the sampling errors are presented in the sampling errors appendix to the report and in the sampling errors table presented in the external resources.

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and include the following: - Age distribution of the household population - Age distribution of eligible children and children for whom the mother or caretaker was interviewed - Age distribution of children under age 5 by 3 month groups - Presence of mother in the household and person interviewed for the under 5 questionnaire - School attendance by single year age - Sex ratio at birth among children ever born, surviving and dead by age of respondent - Distribution of women by time since last birth - Scatter plot of weight by height, weight by age and height by age - Graph of male and female population by single years of age - Population pyramid

The results of each of these data quality tables are shown in the appendix of the final report and are also given in the external resources section.

The general rule for presentation of missing data in the final report tabulations is that a column is presented for missing data if the percentage of cases with missing data is 1% or more. Cases with missing data on the background characteristics (e.g. education) are included in the tables, but the missing data rows are suppressed and noted at the bottom of the tables in the report (not in the SPSS output, however).

Click here for research on the effects of land use planning and gentrification on Portland’s communities of color and other vulnerable populations. Economic Vulnerability Assessment:This map identifies census tracts in Portland where residents are more vulnerable to changing economic conditions, making resisting displacement more difficult. These areas have residents who are more likely to:Be "housing cost-burdened", meaning they pay 30% or more of their income on housing costs.Belong to communities of color, particularly Black and Indigenous communities.Lack college degrees, andHave Lower Incomes.This dataset provides an update to the vulnerability risk analysis that Dr. Lisa Bates prepared for the Bureau of Planning and Sustainability in 2012.This latest dataset includes the following changes in methodology:Low income households were replaced with a size-adjusted median household income. This helps account for how different household sizes experience living with different incomes.Renter households were replaced with households that are housing cost-burdened (pay 30%+ on housing costs). This acknowledges that homeowners who pay a high percentage of their income on housing can be vulnerable to displacement as well.A new variable, Black and Indigenous population, was added to better incorporate past harms to these communities.The vulnerability score was rescaled from 0 to 100. A score of 60 or greater is considered a vulnerable tract.Data sources: U.S. Census Bureau, 2022 ACS 5-year estimates, Tables B25106, B25010, B03002, B19013, B15002. Prepared Summer 2024 by the Portland Bureau of Planning and Sustainability.Download dataset from City of Portland Open Data siteAbout the Bureau of Planning and SustainabilityThe Portland Bureau of Planning and Sustainability (BPS) develops creative and practical solutions to enhance Portland’s livability, preserve distinctive places and plan for a resilient future.Need more information about this data? Email bpsgis@portlandoregon.gov-- Additional Information: Category: Planning Purpose: Map the areas susceptible to gentrification pressure. Update Frequency: Yearly-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=54141

Abstract

The Household Budget Survey conducted by the State Statistical Committee of the Republic of Azerbaijan is the main source of information for analysis of living standards of separate population groups, income differentiation and poverty levels in the country. The survey was introduced in 2001 and has been carried out annually since then.

The Azerbaijan HBS is based on a random probability sample, which was designed to give nationally representative results and allow comparison between main regions of the country and different categories of the population. Approximately 8,700 households are interviewed annually. The annual sample is divided into about 2,200 households per quarter, with a full rotation of households occurring each quarter.

The survey collects information on household income and expenditure, housing conditions, ownership of consumer durables, access to agricultural land and demographic characteristics of household members.

Results of HBS 2001 served as the basis for estimates of poverty in Azerbaijan, using a relative poverty line and a new revised absolute poverty line. Using an absolute poverty line of 120,000 AZM (25.8 USD) per capita per month, it was estimated that 49% of the country population was living in poverty. Using a relative poverty line set at 72,000 AZM (15.5 USD) it was estimated that 17% of the population was living in extreme poverty.

Geographic coverage

National

Analysis unit

• Private households;

A household is defined as a single person or a group of persons with a common budget and residence (house, flat, etc.). The members of the household may not be relatives even if living together and sharing a common household. Persons living in institutional households (elderly houses, hospitals, military barracks etc.) are excluded from the survey.

Since the first half of 90-ties about 800,000 persons migrated within Azerbaijan because of the war in Nagorno-Karabach region. There have been some 250,000 refugees mainly from the other republics of previous USSR, too. This population part is included in the sampling frame according to their actual living place at the time of the population census in 1999.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of Azerbaijan HBS is based on territorial random probability principles. This allows stratifying the population by urban/rural category and by geographical characteristics (8 regions - economic zones). Taking into account that one fourth of the population is concentrated in the capital city Baku this population was included into a separate stratum.

Data from the population census 1999 was used in the survey. Three-stage sampling was implemented to select participating households.

Detailed description of the sampling procedure is available in "Azerbaijan HBS: Methodology" (p.2-6) in external resources.

Sampling deviation

In 2001 the State Statistical Committee of the Republic of Azerbaijan (SSC) had to re-allocate existing interviewer staff to new sampling regions. However, existing employment legislation did not allow them to fire existing interviewers, or to re-hire them on more flexible contract basis. This led to compromises in the original sample implementation, with some interviewers having to work nearer to the place of residence. The compromises have led to some distortions in the final sample, with perhaps the most damaging being the under-representation of IDPs (internally displaced persons) in the 2001 sample. Throughout the year, the SSC has worked to re-allocate and re-employ interviewers in accordance with the new sample, and from 2002 there were no compromises.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are used in Azerbaijan HBS:

1) Household Composition Checklist (to be filled for each household at the outset of the survey). If a household has agreed to participate in the survey, an interviewer must complete a household composition checklist.

2) Main Interview Questionnaire (also to be filled at the outset of the survey). It is completed during an interview with the head of the household at the outset of the survey. The questionnaire contains four chapters: - Housing conditions; - House-side plot; - Education and employment of household members; - Health conditions.

3) Daily Expenditure Diary (to be filled by the household during two weeks). The interviewer must explain to the household how to properly record expenses, namely: - Expenses are recorded on the date they are incurred. - Every expense is recorded in a separate line. - Records must be as accurate and detailed as possible.

4) Quarterly Expenditure Register (to be used throughout the entire quarter and as a supplement for the quarterly expenditure and income interview). The interviewer asks the surveyed households about their regular expenses and income on a quarterly basis. He/she poses questions about main (large) buys and regular expenses over the quarter. Since the family would have problems recollecting all expenses incurred over this period it is assumed that during the quarter the household will record expenses exceeding a certain amount in this document.

5) Expenditure and Income Questionnaire (to be filled quarterly in the course of the interview with the household members). The expenditure and income questionnaire includes the following chapters: - Clothing and shoe expenditure; - Household commodity expenditure; - Furniture, service and other large expenditure; - Housing and utility expenditure; - House-side land plot; - Health care expenditure; - Other expenses; - Individual questionnaire; - Control of completing the individual questionnaire; - Household's income.

While the questionnaires were piloted in the last quarter of 2000, there was not sufficient time to analyze the results of the pilot before launching the survey in January 2001. It was considered vital to begin data collection in January, in order to start the pattern of obtaining calendar year survey results. However, as the first results were entered and analyzed, it became clear that some of the questions were being interpreted in different ways by different interviewers. This was corrected through repeated training sessions and a revision of the questionnaires. The updated questionnaires were introduced in January 2002.

Response rate

Interviewers under the old (before 2001) survey were asked to interview the same households indefinitely. In 2001, they were asked to contract new households each quarter. Given that households were paid only a nominal sum for their participation, interviewers were required to develop and use communication skills in gaining the trust of the households.

The first 2001 survey results showed that too much emphasis and control was being made on overall response rate, but response rates to individual questions were very low. Particularly damaging was the fact that interviewers were allowed to submit questionnaires with incomplete expenditure diaries, since household per capita expenditure was the main indicator used to evaluate welfare levels.

Luxembourg had the highest average monthly salary of employees in the world in 2024 in terms of purchasing power parities (PPP), which takes the average cost of living in a country into account. Belgium followed in second, with the Netherlands in third.

Survey of Household Spending (SHS), average household spending, Canada, regions and provinces.