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.

1) Data Introduction • The Salary data aims to determine whether individuals earn less than or more than $50,000 annually based on their employment, education, and demographic information. It is used widely in analyses that seek to understand income disparities and economic factors influencing earnings.

2) Data Utilization (1) Salary data has characteristics that: • The dataset includes factors such as age, education, job type, hours worked per week, and other socio-economic variables that contribute to predicting salary categories. (2) Salary data can be used to: • Workforce Analysis: Useful for employers and policymakers to understand wage structures and adjust compensation plans accordingly. • Economic Research: Helps researchers analyze economic mobility and the impact of education and employment on income levels.

This data comes from the 2018 salary survey data. Please notethe following:

1. Where able, the data has been altered to reflect the annual pay of the average employee in this city and in this position based on the city's salary range.
2. Not all salaries and wages of less than 1 FTE could be confirmed. As a result, some salaries for positions with less than 1 FTE are either the annual equivelant or the actual pay of the employee throughout a year (ex. if an employee would make $50,000 as a full time employee but only works half-time, some cities marked this as $50,000 or $25,000.
3. Educational attainment often was provided with a minimum education and experience as well as a preferred level. The minimum of both categories are provided.

During a June 2021 survey, around 59 percent of adults with an annual income of more than 100,000 U.S. dollars said that their wages remained the same during the COVID-19 pandemic. However, approximately 26 percent of respondents with an income of under 50,000 a year said that they experienced a decrease in wages during the pandemic.

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

Context

The United States government periodically collects demographic information by conducting a census.

In this problem, we are going to use census information about an individual to predict how much a person earns -- in particular, whether the person earns more than $50,000 per year. This data comes from the UCI Machine Learning Repository.

The file census.csv contains 1994 census data for 31,978 individuals in the United States.### Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

The dataset includes the following 13 variables:

age = the age of the individual in years workclass = the classification of the individual's working status (does the person work for the federal government, work for the local government, work without pay, and so on) education = the level of education of the individual (e.g., 5th-6th grade, high school graduate, PhD, so on) maritalstatus = the marital status of the individual occupation = the type of work the individual does (e.g., administrative/clerical work, farming/fishing, sales and so on) relationship = relationship of individual to his/her household race = the individual's race sex = the individual's sex capitalgain = the capital gains of the individual in 1994 (from selling an asset such as a stock or bond for more than the original purchase price) capitalloss = the capital losses of the individual in 1994 (from selling an asset such as a stock or bond for less than the original purchase price) hoursperweek = the number of hours the individual works per week nativecountry = the native country of the individual over50k = whether or not the individual earned more than $50,000 in 1994

Acknowledgements

MITx ANALYTIX

The remuneration and job title of employees whose salary is at least £50000.This information is published annually.Note 1 - Employee is contracted for less than full time hours in this post.Note 2 - Though the regulations governing this information apply only to officers of the Council and maintained schools, the roles of the City Mayor & Deputy City Mayor are included in the interests of transparencyNote 3 - The Pay Band for these officers includes the cost of compensation for loss of office, primarily redundancy.

In 2022, about 14.88 million households in the United States had an income of 200,000 U.S. dollars or more a year. Another 20.77 million households however, had an income of less than 25,000 U.S. dollars in the same year, The total number of households in the U.S. since 1960 can be found here.

Abstract

The millions of farmers of India have made significant contributions in providing food and nutrition to the entire nation and provided livelihood to millions of people of the country. During the five decades of planned economic development, India has moved from food-shortage and imports to self-sufficiency and exports. Food security and well being of the farmer appears to be major areas of concern of the planners of Indian agriculture. In order to have a snapshot picture of the farming community at the commencement of the third millennium and to analyze the impact of the transformation induced by public policy, investments and technological change on the farmers' access to resources and income as well as well-being of the farmer households at the end of five decades of planned economic development, Ministry of Agriculture have decided to collect information on Indian farmers through “Situation Assessment Survey” (SAS) on Indian farmers and entrusted the job of conducting the survey to National Sample Survey Organisation (NSSO).

The Situation Assessment Survey of Farmers is the first of its kind to be conducted by NSSO. Though information on a majority of items to be collected through SAS have been collected in some round or other of NSS, an integrated schedule, viz., Schedule 33, covering some basic characteristics of farmer households and their access to basic and modern farming resources will be canvassed for the first time in SAS. Moreover, information on consumption of various goods and services in an abridged form are also to be collected to have an idea about the pattern of consumption expenditure of the farmer households.

Schedule 33 is designed for collection of information on aspects relating to farming and other socio-economic characteristics of farmer households. The information will be collected in two visits to the same set of sample households. The first visit will be made during January to August 2003 and the second, during September to December 2003. The survey will be conducted in rural areas only. It will be canvassed in the Central Sample except for the States of Maharashtra and Meghalaya where it will be canvassed in both State and Central samples.

Geographic coverage

The survey covered rural sector of Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir, (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Household (farmer)

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

Outline of sample design: A stratified multi-stage design has been adopted for the 59th round survey. The first stage unit (FSU) is the census village in the rural sector and UFS block in the urban sector. The ultimate stage units (USUs) will be households in both the sectors. Hamlet-group / sub-block will constitute the intermediate stage if these are formed in the selected area.

Sampling Frame for First Stage Units: For rural areas, the list of villages (panchayat wards for Kerala) as per Population Census 1991 and for urban areas the latest UFS frame, will be used as sampling frame. For stratification of towns by size class, provisional population of towns as per Census 2001 will be used.

Stratification

Rural sector: Two special strata will be formed at the State/ UT level, viz.

• Stratum 1: all FSUs with population between 0 to 50 and
• Stratum 2: FSUs with population more than 15,000.

Special stratum 1 will be formed if at least 50 such FSU's are found in a State/UT. Similarly, special stratum 2 will be formed if at least 4 such FSUs are found in a State/UT. Otherwise, such FSUs will be merged with the general strata.

From FSUs other than those covered under special strata 1 & 2, general strata will be formed and its numbering will start from 3. Each district of a State/UT will be normally treated as a separate stratum. However, if the census rural population of the district is greater than or equal to 2 million as per population census 1991 or 2.5 million as per population census 2001, the district will be split into two or more strata, by grouping contiguous tehsils to form strata. However, in Gujarat, some districts are not wholly included in an NSS region. In such cases, the part of the district falling in an NSS region will constitute a separate stratum.

Urban sector: In the urban sector, strata will be formed within each NSS region on the basis of size class of towns as per Population Census 2001. The stratum numbers and their composition (within each region) are given below. - stratum 1: all towns with population less than 50,000 - stratum 2: all towns with population 50,000 or more but less than 2 lakhs - stratum 3: all towns with population 2 lakhs or more but less than 10 lakhs - stratum 4, 5, 6, ...: each city with population 10 lakhs or more The stratum numbers will remain as above even if, in some regions, some of the strata are not formed.

Total sample size (FSUs): 10736 FSUs have been allocated at all-India level on the basis of investigator strength in different States/UTs for central sample and 11624 for state sample.

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to provisional population as per Census 2001 subject to the availability of investigators ensuring more or less uniform work-load.

Allocation of State/UT level sample to rural and urban sectors: State/UT level sample is allocated between two sectors in proportion to provisional population as per Census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. Earlier practice of giving double weightage to urban sector has been modified considering the fact that two main topics (sch. 18.1 and sch 33) are rural based and there has been considerable growth in urban population. More samples have been allocated to rural sector of Meghalaya state sample at the request of the DES, Meghalaya. The sample sizes by sector and State/UT are given in Table 1 at the end of this Chapter.

Allocation to strata: Within each sector of a State/UT, the respective sample size will be allocated to the different strata in proportion to the stratum population as per census 2001. Allocations at stratum level will be adjusted to a multiple of 2 with a minimum sample size of 2. However, attempt will be made to allocate a multiple of 4 FSUs to a stratum as far as possible. Selection of FSUs: FSUs will be selected with Probability Proportional to Size with replacement (PPSWR), size being the population as per population census 1991 in all the strata for rural sector except for stratum 1. In stratum 1 of rural sector and in all the strata of urban sector, selection will be done using Simple Random Sampling without replacement (SRSWOR). Samples will be drawn in the form of two independent sub-samples.

Note: Detail sampling procedure is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

Schedule 33 (Situation Assessment Survey) has been split into several blocks to obtain detailed information on various aspects of farmer households.

Block 0- Descriptive identification of sample household: This block is meant for recording descriptive identification particulars of a sample household.

Block 1- Identification of sample household: items 1 to 12: The identification particulars for items 1, 6 - 11 will be copied from the corresponding items of block 1 of listing schedule (Sch.0.0). The particulars to be recorded in items 2, 3, 4 and 5 have already been printed in the schedule.

Block 2- Particulars of field operation: The identity of the Investigator, Assistant Superintendent and Superintendent associated, date of survey/inspection/scrutiny of schedules, despatch, etc., will be recorded in this block against the appropriate items in the relevant columns.

Block 3- Household characteristics: Characteristics which are mainly intended to be used to classify the households for tabulation will be recorded in this block.

Block 4- Demographic and other particulars of household members: All members of the sample household will be listed in this block. Demographic particulars (viz., relation to head, sex, age, marital status and general education), nature of work, current weekly status, wage and salary earnings etc. will be recorded for each member using one line for one member.

Block 5- Perception of household regarding sufficiency of food: This block will record information about perception of households regarding sufficiency of food.

Block 6- Perceptions regarding some general aspects of farming: In this block some information regarding perception of the farmer household about some general aspects of farming are to be recorded.

Block 7- Particulars of land possessed during Kharif/Rabi: This block is designed to record information regarding the land on which farming activities are carried out by the farmer household during Kharif/Rabi.

Block 8- Area under irrigation during Kharif/Rabi: In this block information regarding the area under irrigation during last 365 days for different crops will be recorded according to the source of irrigation.

Block 9- Some particulars of farming resources used for cultivation during Kharif / Rabi: Information regarding farming resources used for cultivation during the last 365 days will be ascertained from the farmer households and will be recorded in this block.

Block 10- Use of energy during last 365 days: This block will be

The average Dutch employee reached their highest annual salary between the age of 50 and 54 years old, at just over 50,000 thousand euros. Up to that point, the average annual salary generally increased, from about 1,500 euros per year for those under 15, to 51,900 for those 45-49 years old. After that age, average annual salaries decreased again, to about 17,300 euros for those 75 years and older. The average annual salary in the Netherlands in 2022 was just under 39,900 thousand euros, including bonuses.

 Highest salaries in the mining and quarrying industry  

Those looking to make a substantial amount of money were best off in the mining and quarrying industry, where average annual wages reached nearly 83.5 thousand euros in 2021. Employees in the electricity and gas supply sector earned roughly 62.7 thousand euros, and those working in finance made nearly as much, at 62,200 euros. On the lower end of the scale, employees in the accommodation and food serving industry earned an average annual salary of only 15.1 thousand euros.

Gender differences  

 In general, annual salaries for men were considerably higher than salaries for women. Whereas men earned an average annual salary of over 46.3 thousand in 2021, women in the Netherlands on average made about 29.7thousand euros annually. This was not just the result of men having a higher average hourly salary, to some extent this was also a consequence of women working fewer hours than men. Whereas men in the Netherlands on average worked 33.5 hours per week in 2021, women worked only 25.3 hours.

According to exit polling in the 2020 Presidential Election in the United States, ** percent of surveyed voters making less than 50,000 U.S. dollars reported voting for former Vice President Joe Biden. In the race to become the next president of the United States, ** percent of voters with an income of 100,000 U.S. dollars or more reported voting for incumbent President Donald Trump.

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.

The average net salary of lecturers in the UK and in Germany in 2020 amounted to around 50,000 euros yearly. On the contrary, Italian junior professors earned 28,600 euros, 43 percent less than their British and German colleagues. Associate professors in France and Italy were paid around 57,000 euros net per year, 13,000 euros less than in Germany and the UK. Moreover, French and Italian full professors received a lower salary than a German or British associate professor, and earned only 7,000 euros more than a lecturer.

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.

In total, about 59.9 percent of U.S. households paid income tax in 2022. The remaining 40.1 percent of households paid no individual income tax. In that same year, about 47.1 percent of U.S. households with an income between 40,000 and 50,000 U.S. dollars paid no individual income taxes.

In 2023, the real median household income for householders aged 15 to 24 was at 54,930 U.S. dollars. The highest median household income was found amongst those aged between 45 and 54. Household median income for the United States since 1990 can be accessed here.

In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between ******* and ******* Indian rupees a year. On the other hand, about ***** percent of households that same year, accounted for the rich, earning over * million rupees annually. The middle class more than doubled that year compared to ** percent in financial year 2005. Middle-class income group and the COVID-19 pandemic During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant. Economic inequality While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last ***** decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

According to exit polling in ten key states of the 2024 presidential election in the United States, ** percent of voters with a 2023 household income of ****** U.S. dollars or less reported voting for Donald Trump. In comparison, ** percent of voters with a total family income of 100,000 to ******* U.S. dollars reported voting for Kamala Harris.