This statistic shows the median household income in the United States from 1970 to 2020, by income tier. In 2020, the median household income for the middle class stood at 90,131 U.S. dollars, which was approximately a 50 percent increase from 1970. However, the median income of upper income households in the U.S. increased by almost 70 percent compared to 1970.

Graph and download economic data for Median Personal Income in the United States (MEPAINUSA646N) from 1974 to 2023 about personal income, personal, median, income, and USA.

In 2023, just over 50 percent of Americans had an annual household income that was less than 75,000 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Maryland, New Jersey, and Massachusetts were among the states with the highest median household income in 2020. In terms of income by race and ethnicity, the average income of Asian households was 94,903 U.S. dollars in 2020, while the median income for Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates its list of poverty levels. Preliminary estimates show that the average poverty threshold for a family of four people was 26,500 U.S. dollars in 2021, which is around 100 U.S. dollars less than the previous year. There were an estimated 37.9 million people in poverty across the United States in 2021, which was around 11.6 percent of the population. Approximately 19.5 percent of those in poverty were Black, while 8.2 percent were white.

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.

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

Families of tax filers; Distribution of total income by census family type and age of older partner, parent or individual (final T1 Family File; T1FF).

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.

Key information about South Korea Household Income per Capita

• South Korea Annual Household Income per Capita reached 19,208.928 USD in Dec 2024, compared with the previous value of 19,230.954 USD in Dec 2023.
• South Korea Annual Household Income per Capita data is updated yearly, available from Dec 2003 to Dec 2024, with an averaged value of 14,290.376 USD.
• The data reached an all-time high of 19,230.954 USD in Dec 2023 and a record low of 7,685.194 USD in Dec 2003.
• In the latest reports, Retail Sales of South Korea dropped 0.473 % YoY in May 2023.

CEIC calculates Annual Household Income per Capita from quarterly Monthly Household Income multiplied by 3 and quarterly Average Household Size and converts it into USD. Statistics Korea provides Household Income in local currency and Average Household Size. Federal Reserve Board average market exchange rate is used for currency conversions. Household Income excludes single person and farm households. Household Income per Capita prior to 2019 is based on old methodology.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Texas: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 500,731(4.66%) households where the householder is under 25 years old, 4,035,035(37.54%) households with a householder aged between 25 and 44 years, 3,856,052(35.88%) households with a householder aged between 45 and 64 years, and 2,355,422(21.92%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the state of Texas, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Texas median household income by age. You can refer the same here

This dataset accompanies the tables ‘Household income and saving in the National Accounts: distributions by income quintile’ and ‘Household consumption in the National Accounts: distributions by income quintile’ and presents the number of households in each quintile broken down by household type.

   Households are grouped into income quintiles on the basis of their equivalised disposable income, ranked from lowest to highest, i.e., the first quintile represents the 20% households with the lowest equivalised disposable income and the fifth quintile the 20% households with the highest. Equivalisation means that results for each household have been recalculated on the basis of its consumption needs, in order to produce comparable results across households of different size and composition. <br><br>

   Households are grouped on the basis of their composition, taking into account the presence, number and age of the members of the household. Eight categories are shown: a) single person (adult) less than 65 years old, b) single adult aged 65 and older, c) single adult with children living at home, d) a couple (two adults) where both are less than 65 years old without children living at home, e) two adults where at least one is aged 65 or older without children living at home, f) two adults with fewer than 3 children living at home, g) two adults with at least 3 children living at home, and h) others. In this classification, an adult is defined as anyone who is 18 years old or older.<br><br>

   The default view of this table is for a single country (‘Reference area’ filter) and single year (‘Time period’ filter). In cases where countries appear to be greyed-out, data may be available for earlier years, and these can be selected by selecting a different start and end year in the ‘Time period’ filter. <br><br>

   For more information on the (compilation of) these results, please see the <a href="https://www.oecd.org/sdd/na/household-distributional-results-in-line-with-national-accounts-experimental-statistics.htm"> webpage on household distributional results </a>.

For the Companies of Persons the data of all the assets present in the supply are exposed: - Dominicale: Framework RA line 27 column 11; - Agrarians: Framework RA line 27 column 12; - Breeding: framework RN row 5 column 1; - Buildings: framework RN line 6 column 1; - Self-employment: framework RN line 3 column 1; - Business Income in Ordinary Accounting: box RN line 1 column 1 if positive; - Business Loss in Ordinary Accounting: box RN line 1 column 1 if negative; - Simplified Accounting Business Income: box RN line 2 column 1 if positive; - Simplified Accounting Business Loss: box RN line 2 column 1 if negative; - Participation: framework RN column 1, column 1a, column 1b of row 8; - Capital gains: Framework RN line 7 column 1; - Capital: framework RL line 3 column 2; - Miscellaneous: Framework RL line 12 column 1; - Reserves established before processing: framework RN row 10 column 1; - Total income: sum of previous income; - Separate taxation: Framework RN Row 11 Column 1. For each asset, both the frequency and the amount are reported: the frequency indicates the number of entities that have the corresponding non-zero amount.

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.

This table presents household income and saving from the national accounts broken down by income quintile. It includes primary income, disposable income, adjusted disposable income and saving. Primary income is the income that households receive as a result of their involvement in production. It includes gross operating surplus (in the case of households, imputed income from owning your own house), gross mixed income (profits of the self-employed) and compensation of employees (wages and salaries and employers’ social contributions) as well as net interest, distributed income of corporations (dividends) and rent. Disposable income is the income that households receive after taxes on income and wealth, social contributions and benefits, non-life insurance premiums and claims, and other current transfers like remittances. Adjusted disposable income is derived from disposable income, but also includes the value of social transfers in kind received by households. Saving represents that part of disposable income (adjusted for the change in pension entitlements) that is not spent on final consumption goods and services.

   In this table, households are grouped on the basis of their composition, taking into account the presence, number and age of the members of the household. Eight categories are shown: a) single person (adult) less than 65 years old, b) single adult aged 65 and older, c) single adult with children living at home, d) a couple (two adults) where both are less than 65 years old without children living at home, e) two adults where at least one is aged 65 or older without children living at home, f) two adults with fewer than 3 children living at home, g) two adults with at least 3 children living at home, and h) others. In this classification, an adult is defined as anyone who is 18 years old or older.<br><br>

   Results are presented in national currency and as averages per household and per consumption unit (you can choose these from the ‘Unit of measure’ filter). Results per consumption unit (equivalised income and saving) are obtained by dividing each household’s result by the number of consumption units, reflecting its consumption needs, for example by applying the standard OECD-modified equivalence scale, counting the first adult as 1, any additional people aged 14 and over as 0.5 and all children under 14 as 0.3.<br><br>

   The default view of this table is for a single country (‘Reference area’ filter) and single year (‘Time period’ filter). In cases where countries appear to be greyed-out, data may be available for earlier years, and these can be selected by selecting a different start and end year in the ‘Time period’ filter. Users are recommended to select one country at a time to obtain a comprehensive overview of the distributional results for that country for a given period of time. Alternatively, you may select a specific item from the ‘Transaction’ filter to make cross-country comparisons. <br><br>

   For more information on the (compilation of) these results, please see the <a href="https://www.oecd.org/sdd/na/household-distributional-results-in-line-with-national-accounts-experimental-statistics.htm"> webpage on household distributional results </a>.

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.

Families of tax filers; Single-earner and dual-earner census families by number of children (final T1 Family File; T1FF).

Distribution of employment income of individuals by sex and work activity, Canada, provinces and selected census metropolitan areas, annual.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Michigan. The dataset can be utilized to gain insights into gender-based income distribution within the Michigan population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Michigan, among individuals aged 15 years and older with income, there were 3.72 million men and 3.67 million women in the workforce. Among them, 1.94 million men were engaged in full-time, year-round employment, while 1.44 million women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.45% fell within the income range of under $24,999, while 11.49% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 24.99% of men in full-time roles earned incomes exceeding $100,000, while 13.02% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/michigan-income-distribution-by-gender-and-employment-type.jpeg" alt="Michigan gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Michigan median household income by gender. You can refer the same here

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

Abstract

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

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

Geographic coverage

National Coverage

Analysis unit

Individuals and Households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Sampling deviation

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

Cleaning operations

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

Data capturing

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

Response rate

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

Data appraisal

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