In 2023, about 26.9 percent of Asian private households in the U.S. had an annual income of 200,000 U.S. dollars and more. Comparatively, around 13.9 percent of Black households had an annual income under 15,000 U.S. dollars.

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 1.310 % in 2016. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 1.310 % from Dec 2016 (Median) to 2016, with 1 observations. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

China Calculator & Money Specialized Equip Mfg: Selling &Distribution Cost data was reported at 2,430.000 RMB mn in Dec 2019. This records an increase from the previous number of 2,210.000 RMB mn for Dec 2018. China Calculator & Money Specialized Equip Mfg: Selling &Distribution Cost data is updated monthly, averaging 155.072 RMB mn from Jan 2003 (Median) to Dec 2019, with 76 observations. The data reached an all-time high of 2,430.000 RMB mn in Dec 2019 and a record low of 20.940 RMB mn in Mar 2003. China Calculator & Money Specialized Equip Mfg: Selling &Distribution Cost data remains active status in CEIC and is reported by Ministry of Industry and Information Technology. The data is categorized under Global Database’s China – Table CN.RFF: Electronic Mfg Industry: Computer: Monthly: Calculator & Money Specialized Equipment.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

A breakdown of annual household incomes in Japan showed that around ***** percent of households earned less than *** million Japanese yen per year as of 2024. That year, the average annual household income of Japanese households was approximately *** million yen compared to a median household income of *** million yen.

The relationships between cigarette affordability, consumer income levels and distribution, and tax increases are complex and underexplored. This study investigates different ways of calculating the Relative Income Price (RIP) measure of affordability using Malaysia as a case study. We calculate cigarette affordability in Malaysia between 2009–2019 using government data, and multiple RIP variants. The conventional RIP calculation relies on 2,000 sticks and GDP (henceforth standard RIP). We explore that and other variants that use annual cigarette consumption estimates and/or proportions of various financial measures of wealth in both rural and urban areas. Our findings indicate broadly consistent trends in cigarette affordability across all methods. From 2009 to 2012, there was a slight decrease in the percentage of wealth required to purchase cigarettes, followed by an increase in 2015 and 2016, and then another decline, suggesting a recent trend toward increased affordability. Using the standard RIP method, 0.9 percentage points(pp) more of per capita GDP was required between 2009 and 2016, but, by 2019 it was 0.1pp less than in 2016. However, Household Income Per Capita (HIPC) and Household Expenditure Per Capita (HEPC) provide a more nuanced perspective on cigarette affordability compared to GDP per capita, as they reveal larger shifts in affordability. The conventional 2,000 sticks method using HIPC from 2009 to 2016 indicated 0.3pp more of income was required to purchase cigarettes, but by 2019, it was 1.0pp less than in 2016. Using HIPC with actual consumption estimates, smokers required approximately 0.9pp more of average income to purchase cigarettes between 2014 and 2016, but 2.5pp less from 2016 to 2019. Actual consumption estimates offer insight into smokers’ ability to offset higher purchase costs by adjusting consumption patterns without quitting. We conclude that to address issues related to cigarette affordability, the Malaysian government should consider increasing tobacco tax vis-à-vis income growth.

The bottom 50 percent in Argentina earned on average 15,057 U.S. dollars at purchasing power parity (PPP) before income taxes as of 2022, while individuals in the top one percent earned pre-tax more than 686,433 dollars. Looking at the percentage distribution of wealth in Argentina, the poorest half held 5.7 percent of the total in 2021. Moreover, the top one percent in the South American country accounted for 25.7 percent of the overall national wealth.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

Abstract

The Household Income and Expenditure Survey (NHIES) 2009 was a survey collecting data on income, consumption and expenditure patterns of households, in accordance with methodological principles of statistical enquiries, which were linked to demographic and socio-economic characteristics of households. A Household Income and expenditure Survey was the sole source of information on expenditure, consumption and income patterns of households, which was used to calculate poverty and income distribution indicators. It also served as a statistical infrastructure for the compilation of the national basket of goods used to measure changes in price levels. It was also used for updating the national accounts.

The main objective of the NHIES 2009-2010 was 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 (MDG's). 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

Analysis unit

• Individuals
• 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 (PHC). 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 between survey 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 data capturing process was undertaken in 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.

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

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Surveys related to the family budget are considered one of the most important surveys types carried out by the Department Of Statistics, since it provides data on household expenditure and income and their relationship with different indicators. Therefore, most of the countries undertake periodic surveys on household income and expenditures. The Department Of Statistics, since established, conducted a series of Expenditure and Income Surveys during the years 1966, 1980, 1986/1987, 1992, 1997, 2002/2003, 2006/2007, 2008/2009, 2010/2011 and because of continuous changes in spending patterns, income levels and prices, as well as in the population internal and external migration, it was necessary to update data for household income and expenditure over time. Hence, the need to implement the Household Expenditure and Income Survey for the year 2013 arises.

The survey was then conducted to achieve the following objectives: 1. Provide data on income and expenditure to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. 2. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 3. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 4. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty. 5. Identify consumer spending patterns prevailing in the society, and the impact of demographic, social and economic variables on those patterns. 6. Calculate the average annual income of the household and the individual, and identify the relationship between income and different socio-economic factors, such as profession and educational level of the head of the household and other indicators. 7. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the Kingdom. Where the Kingdom is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Household Expenditure and Income survey sample, for the year 2013, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 10 households was selected from each cluster, in addition to another 5 households selected as a backup for the basic sample, using a systematic sampling technique. Those 5 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2010 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (8 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map. It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

To reach the survey objectives, 3 forms have been developed. Those forms were finalized after being tested and reviewed by specialists taking into account making the data entry, and validation, process on the computer as simple as possible.

(1) General Form/Questionnaire This form includes: - Housing characteristics such as geographic location variables, household area, building material predominant for external walls, type of tenure, monthly rent or lease, main source of water, lighting, heating and fuel cooking, sanitation type and water cycle, the number of rooms in the dwelling, in addition to providing ownership status of some home appliances and car. - Characteristics of household members: This form focused on the social characteristics of the family members such as relation to the head of the family, gender, age and educational status and marital status. It also included economic characteristics such as economic activity, and the main occupation, employment status, and the labor sector. To the additions of questions about individual continued to stay with the family, in order to update the information at the end of each of the four rounds of the survey. - Income section which included three parts · Family ownership of assets · Productive activities for the family · Current income sources

(2) Expenditure on food commodities form/Questionnaire This form indicates expenditure data on 17 consumption groups. Each group includes a number of food commodities, with the exception of the latter group, which was confined to some of the non-food goods and services because of their frequent spending pattern on daily basis like food commodities. For the purposes of the efficient use of results, expenditure data of the latter group was moved with the non-food commodities expenditure. The form also includes estimated amounts of own-produced food items and those received as gifts or in an in-kind form, as well as servants living with the family spending on themselves from their own wages to buy food.

(3) Expenditure on non-food commodities form/Questionnaire This form indicates expenditure data on 11 groups of non-food items, and 5 sets of spending on services, in addition to a group of consumption expenditure. It also includes an estimate of self-consumption, and non-food gifts or other items in an in-kind form received or sent by the household, as well as servants living with the family spending on themselves from their own wages to buy non-food items.

Cleaning operations

----> Raw Data

The data collection phase was then followed by the data processing stage accomplished through the following procedures: 1- Organizing forms/questionnaires A compatible archive system, with the nature of the subsequent operations, was used to classify the forms according to different round throughout the year. This is to effectively enable extracting the forms when required for processing. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms are back to the archive system. 2- Data office checking This phase is achieved concurrently with the data collection phase in the field, where questionnaires completed in the fieldwork are immediately sent to data office checking phase. 3- Data coding A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were use, while for the rest of the questions, all coding were predefined

Affordability calculation using actual consumption estimates (ConsumptionRIP).

Variations of growth, as well as short-term cyclical variations, can be measured by different indicators. The current research relies on two measuring concepts, i.e. multivariate diffusion indeces or the national product. For the period from 1851 to 1913, there are four time series available regarding the development of the German NNP, notably the calculation of formation, distribution and application by Hoffmann (1965), and the calculation of distribution by Hoffmann and Müller (1959). ´In this article, we contribute to the ongoing debate about the data quality by presenting improvements of all four available series. In particular, we improve the output series by calculating a new Index of industrial production and by basing the index on a revised value of industrial output in 1913. In addition, we significantly enhance the capital sock and net investment series, and we present new data about capital income. Furthermore, we present a new series of net foreign capital income. Finally, we add new information about "indirect taxes" to the income and output series, and we thus calculate four series representing an NNP in market prices. Despite our significant improvements, differences between the series remain, and we solve this problem in a second step by computing a compromise estimate of Germany´s NNP for the years 1851 to 1913 as a weighted average of the four corrected original series. The remaining parts of the article analyze long-term growth and business cycles of the German economy employing the new compromise estimate. The revised data show a higher level of economic activity for 1851 as well as for 1913, but with a larger difference for 1851. Thus, the average growth rate of the German economy was lower during the industrialization period. Moreover, the driving force of this growth was a growing total factor productivity, which in turn was driven by structural change from agriculture to industry. The business cycles of the German economy had an average length of about five years during the second half of the nineteenth century; the magnitude of cycles was weaker in the last quarter of the nineteenth and the early twentieth century than before. Of special interest is our business cycle dating for the 1870s: with the new data the well-known pattern of "Gründerzeit"-boom and "Gründerzeit"-depression can not be confirmed.´ (Burhop, C. / Wolff, G. B., 2005: A Compromise Estimate of German Net National Product, 1851-1913 and its Implications for Growth and Business Cycles, in: Journal of Economic History, Vol. 65 (3), S. 614f). Factual classification of tables in HISTAT: B. Data by C. Burhop and G. B. Wolf: B. Compromise estimate and adjusted estimates of the German net national product on the basis of the prices of 1913(1851-1913) B. Time series for the calculation of the compromise estimate of the German net national product on the basis of the prices from 1913 (1851-1913) D. Data by O. Donner: D. Level of share prices, dividends, stock index, dividend index and stock rate of return for Germany according to Donner (1870-1913) H. Data by W. G. Hoffmann (1965) and W. G. Hoffmann/J. H. Müller (1957): H. Capital stock and investments according to Hoffmann (1850-1913) H. Industrial production and trade according to Hoffmann (1846-1913) H. Comparison of the four estimates of the net national product in Germany according to Hoffmann by prices from 1913 (1851-1913) H. German capital invested abroad and factor foreign income according to Hoffmann (1873-1913) H. Public consumption according to Hoffmann on the basis of the prices of 1913 (1850-1913)

The average personal wealth of the bottom 50 percent in Mexico was valued at -200 euros. That is, on average, people from this group had more debts than assets. On the other hand, the richest one percent held an average wealth of 2.91 million euros in this Latin American country. Similarly, Chilean's average personal wealth of the one percent reached 2.67 million euros that same year.

Yearly GDP, HIPC and HEPC per capita (All in current MYR).

A dataset listing the richest zip codes in New York per the most current US Census data, including information on rank and average income.

In 2023 in France, the average net monthly full-time equivalent salary was ***** euros. That year, ** percent of the poorest French employees earned less than ***** euros per month. On the other hand, ** percent of the richest French employees received more than ***** euros. The French people who were part of the richest one percent of the working population earned a salary over ***** euros per month.

Nomenclature of the different versions of the RIP.

In Mexico, as of 2022, the bottom 50 percent, which represents the population whose income lied below the median, earned on average 2,076 euros at purchasing power parity (PPP) before income taxes. Meanwhile, the top ten percent had an average earning of 111,484 euros, 53 times over than the average earning of the bottom half. Further, the bottom 50 percent accounted for -0.3 percent of the overall national wealth in Mexico, that is, they have on average more debts than assets.