Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2024 about personal income, personal, median, income, real, and USA.

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 2024, the median household income in the United States was 83,730 U.S. dollars. This reflected an increase from the previous year. Household income The 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 varied from state to state. In 2024, Massachusetts recorded the highest median household income in the country, at 113,900 U.S. dollars. On the other hand, Mississippi, recorded the lowest, at 55,980 U.S. dollars.Household income is also used to determine the poverty rate in the United States. In 2024, 10.6 percent of the U.S. population was living below the national poverty line. This was the lowest level since 2019. Similarly, the child poverty rate, which represents people under the age of 18 living in poverty, reached a three-decade low of 14.3 percent of the children. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.52 in 2024. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality, while a score of one indicates complete inequality.

Graph and download economic data for Mean Personal Income in the United States (MAPAINUSA646N) from 1974 to 2024 about average, personal income, personal, income, and USA.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

In 2025, just over 45 percent of American households had an annual income that was less than 75,000 U.S. dollars. On the other hand, some 16 percent had an annual income of 200,000 U.S. dollars or more. The median household income in the country reached almost 84,000 U.S. dollars in 2024. 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. Massachusetts, New Hampshire, and Maryland were among the states with the highest median household income in 2024. In terms of income by race and ethnicity, the average income of Asian households was highest, at over 120,000 U.S. dollars, while the median income among Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates the poverty threshold based on the income of various household types. As of 2023, the threshold for a single-person household was 15,480 U.S. dollars. For a family of four, the poverty line increased to 31,200 U.S. dollars. There were an estimated 38.9 million people living in poverty across the United States in 2024, which reflects a poverty rate of 10.6 percent.

Context

This dataset contains information about individuals' demographic and employment attributes to predict whether their income exceeds $50,000 per year. It originates from the 1994 U.S. Census database and has been widely used in classification problems, making it an excellent resource for machine learning, data analysis, and statistical modeling.

Content

The dataset includes various features related to personal and work-related attributes. The target variable is whether an individual's income exceeds $50,000 annually.

Key features include:

Age: Age of the individual.

Workclass: Employment type (e.g., private, government, self-employed).

Education: Highest level of education achieved.

Education-Num: Number corresponding to the level of education.

Marital Status: Marital status of the individual.

Occupation: Profession or job role.

Relationship: Family role (e.g., husband, wife, not in family).

Race: Race of the individual.

Sex: Gender of the individual.

Capital Gain: Income from investment sources other than salary.

Capital Loss: Losses from investment sources.

Hours Per Week: Average number of hours worked per week.

Native Country: Country of origin of the individual

Variables

Age: Continuous variable representing the age of the individual.

Workclass: Categorical variable indicating the type of employment (e.g., Private, Self-Employed, Government).

Education: Categorical variable showing the highest level of education achieved (e.g., Bachelors, Masters).

Education-Num: Numerical representation of the education level.

Marital Status: Categorical variable representing marital status (e.g., Married, Never-Married).

Occupation: Categorical variable indicating the job role or occupation

Relationship: Categorical variable describing the family relationship (e.g., Husband, Wife).

Race: Categorical variable showing the race of the individual.

Sex: Categorical variable indicating the gender of the individual.

Capital Gain: Continuous variable representing income from capital gains.

Capital Loss: Continuous variable representing losses from investments.

Hours Per Week: Continuous variable showing the average working hours per week.

Native Country: Categorical variable indicating the country of origin.

Income: Target variable (binary), indicating whether the individual earns more than $50,000 (>50K) or not (<=50K).

Acknowledgements

This dataset was derived from the 1994 U.S. Census database and has been made publicly available for research and educational purposes. It is not affiliated with any specific organization. Users are encouraged to comply with ethical data usage guidelines while working with this dataset.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over (LEU0254525800A) from 2000 to 2024 about human resources, management, second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Health Outcomes and Socioeconomic Factors

A Study of US County Data

By Data Exercises [source]

About this dataset

This dataset contains a wealth of health-related information and socio-economic data aggregated from multiple sources such as the American Community Survey, clinicaltrials.gov, and cancer.gov, covering a variety of US counties. Your task is to use this collection of data to build an Ordinary Least Squares (OLS) regression model that predicts the target death rate in each county. The model should incorporate variables related to population size, health insurance coverage, educational attainment levels, median incomes and poverty rates. Additionally you will need to assess linearity between your model parameters; measure serial independence among errors; test for heteroskedasticity; evaluate normality in the residual distribution; identify any outliers or missing values and determine how categories variables are handled; compare models through implementation with k=10 cross validation within linear regressions as well as assessing multicollinearity among model parameters. Examine your results by utilizing statistical agreements such as R-squared values and Root Mean Square Error (RMSE) while also interpreting implications uncovered by your analysis based on health outcomes compared to correlates among demographics surrounding those effected most closely by land structure along geographic boundaries throughout the United States

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How to use the dataset

This dataset provides data on health outcomes, demographics, and socio-economic factors for various US counties from 2010-2016. It can be used to uncover trends in health outcomes and socioeconomic factors across different counties in the US over a six year period.

The dataset contains a variety of information including statefips (a two digit code that identifies the state), countyfips (a three digit code that identifies the county), avg household size, avg annual count of cancer cases, average deaths per year, target death rate, median household income, population estimate for 2015, poverty percent study per capita binned income as well as demographic information such as median age of male and female population percent married households adults with no high school diploma adults with high school diploma percentage with some college education bachelor's degree holders among adults over 25 years old employed persons 16 and over unemployed persons 16 and over private coverage available private coverage available alone temporary private coverage available public coverage available public coverage available alone percentages of white black Asian other race married households and birth rate.

Using this dataset you can build a multivariate ordinary least squares regression model to predict “target_deathrate”. You will also need to implement k-fold (k=10) cross validation to best select your model parameters. Model diagnostics should be performed in order to assess linearity serial independence heteroskedasticity normality multicollinearity etc., while outliers missing values or categorical variables will also have an effect your model selection process. Finally it is important to interpret the resulting models within their context based upon all given factors associated with it such as outliers missing values demographic changes etc., before arriving at a meaningful conclusion which may explain trends in health outcomes and socioeconomic factors found within this dataset

Research Ideas

• Analysis of factors influencing target deathrates in different US counties.
• Prediction of the effects of varying poverty levels on health outcomes in different US counties.
• In-depth analysis of how various socio-economic factors (e.g., median income, educational attainment, etc.) contribute to overall public health outcomes in US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. -...

We analyze how median real incomes in the United States have changed since 1980 under a definition of the middle class that adjusts for changes in demographics. We find that failing to adjust for demographic shifts in the population relating to age, race, and education can indicate a more positive outlook than is truly the case. We also find that the real median incomes of today’s middle class are somewhat higher than they used to be, particularly for households headed by two adults. We find, as in prior research, that prices for housing, healthcare, and education have risen more than middle-class incomes, while prices for transportation, food, and recreation have risen less than middle-class incomes.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over: Men was 1520.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over: Men reached a record high of 1570.00000 in January of 2022 and a record low of 1053.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over: Men - last updated from the United States Federal Reserve on December of 2025.

The Los Angeles County Climate Vulnerability Assessment identified and incorporated 29 social vulnerability indicators. These indicators are listed below alongside their description and data source. Full report: https://ceo.lacounty.gov/cva-report/Note: All indicators are at the census tract level. Census tracts with no population (data) are omitted from this layer. Indicator Description Source Countywide Average

Asian Percent identifying as non-Hispanic Asian US Census Bureau, American Community Survey 2018 5-Year Estimates 14.4%

Asthma Age-adjusted rate of emergency department visits for asthma California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 52.2

Black Percent identifying as non-Hispanic black or African American US Census Bureau, American Community Survey 2018 5-Year Estimates 7.9%

Cardiovascular Age-adjusted rate of emergency department visits for heart attacks per 10,000 California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 8.4

Children Percent of people 18 and under US Census Bureau, American Community Survey 2018 5-Year Estimates 24.9%

Disability Percent of persons with either mental or physical disability US Census Bureau, American Community Survey 2018 5-Year Estimates 9.9%

Female Percent female US Census Bureau, American Community Survey 2018 5-Year Estimates 50.7%

Female householder Percent of households that have a female householder with no spouse present US Census Bureau, American Community Survey 2018 5-Year Estimates 16.2%

Foreign born Percent of the total population who was not born in the United States or Puerto Rico US Census Bureau, American Community Survey 2018 5-Year Estimates 35.2%

Hispanic Latinx Percent identifying as Hispanic or Latino US Census Bureau, American Community Survey 2018 5-Year Estimates 48.5%

Households without vehicle access Percent of households without access to a personal vehicle US Census Bureau, American Community Survey 2018 5-Year Estimates 8.8%

Library access Each tract's average block distance to nearest library LA County Internal Services Department 1.14 miles

Limited English Percent limited English speaking households US Census Bureau, American Community Survey 2018 5-Year Estimates 13.6%

Living in group quarters Percent of persons living in (either institutionalized or uninstitiutionalized) group quarters US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

Median income Median household income of census tract US Census Bureau, American Community Survey 2018 5-Year Estimates $69,623

Mobile homes Percent of occupied housing units that are mobile homes US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

No health insurance Percent of persons without health insurance US Census Bureau, American Community Survey 2018 5-Year Estimates 0.2%

No high school diploma Percent of persons 25 and older without a high school diploma US Census Bureau, American Community Survey 2018 5-Year Estimates 10.8%

No internet subscription Percent of the population without an internet subscription US Census Bureau, American Community Survey 2018 5-Year Estimates 22.6%

Older adults Percent of people 65 and older US Census Bureau, American Community Survey 2018 5-Year Estimates 18.4%

Older adults living alone Percent of households in which the householder is 65 and over who and living alone US Census Bureau, American Community Survey 2018 5-Year Estimates 12.9%

Outdoor workers Percentage of outdoor workers - agriculture, fishing, mining, extractive, construction occupations US Census Bureau, American Community Survey 2018 5-Year Estimates 8.0%

Poverty Percent of the population living in a family earning below 100% of the federal poverty threshold US Census Bureau, American Community Survey 2018 5-Year Estimates 5.4%

Rent burden Percent of renters paying more than 30 percent of their monthly income on rent and utilities US Census Bureau, American Community Survey 2018 5-Year Estimates 16.1%

Renters Percentage of renters per census tract US Census Bureau, American Community Survey 2018 5-Year Estimates 54.3%

Transit access Percent of population residing within a ½ mile of a major transit stop Healthy Places Index, SCAG 52.8%

Tribal and Indigenous Percent identifying as non-Hispanic American Indian and Alaska native US Census Bureau, American Community Survey 2018 5-Year Estimates 54.9%

Unemployed Percent of the population over the age of 16 that is unemployed and eligible for the labor force US Census Bureau, American Community Survey 2018 5-Year Estimates 6.9%

Voter turnout rate Percentage of registered voters voting in the 2016 general election CA Statewide General Elections Database 2016 63.8%

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over was 1453.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over reached a record high of 1453.00000 in January of 2024 and a record low of 938.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over - last updated from the United States Federal Reserve on November of 2025.

United States Avg Weekly Earnings: sa: OS: Human Rights Organization data was reported at 1,008.640 USD in May 2018. This records a decrease from the previous number of 1,019.500 USD for Apr 2018. United States Avg Weekly Earnings: sa: OS: Human Rights Organization data is updated monthly, averaging 691.330 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 1,019.500 USD in Apr 2018 and a record low of 549.680 USD in Mar 2006. United States Avg Weekly Earnings: sa: OS: Human Rights Organization data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G033: Current Employment Statistics Survey: Average Weekly and Hourly Earnings: Seasonally Adjusted.

2023 Tract-level Indicators of Potential Disadvantage for the DVRPC RegionTitle VI of the Civil Rights Act states that "no person in the United States, shall, on the grounds of race, color, or national origin be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving federal financial assistance.”Under Title VI of the Civil Rights Act, Metropolitan Planning Organizations (MPOs) are directed to create a method for ensuring that Title VI compliance issues are investigated and evaluated in transportation decision-making. There is additional guidance from the FHWA’s Title VI and Additional Nondiscrimination requirements (2017), and FTA’s Title VI requirements and guidelines (2012). The Indicators of Potential Disadvantage (IPD) analysis is used throughout DVRPC to demonstrate compliance with Title VI of the Civil Rights Act.This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:YouthOlder AdultsFemaleRacial MinorityEthnic MinorityForeign-BornDisabledLimited English ProficiencyLow-IncomeCensus tables used to gather data from the 2019-2023 American Community Survey 5-Year EstimatesUsing U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group.Census tables used to gather data from the 2019-2023 American Community Survey 5-Year Estimates.For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipdSource of tract boundaries: 2020 US Census Bureau, TIGER/Line ShapefilesNote: Tracts with null values should be symbolized as "Insufficient or No Data".Data Dictionary for Attributes:(Source = DVRPC indicates a calculated field)FieldAliasDescriptionSourceyearIPD analysis yearDVRPCgeoid2011-digit tract GEOIDCensus tract identifierACS 5-yearstatefp2-digit state GEOIDFIPS Code for StateACS 5-yearcountyfp3-digit county GEOIDFIPS Code for CountyACS 5-yeartractceTract numberTract NumberACS 5-yearnameTract numberCensus tract identifier with decimal placesACS 5-yearnamelsadTract nameCensus tract name with decimal placesACS 5-yeard_classDisabled percentile classClassification of tract's disabled percentage as: well below average, below average, average, above average, or well above averagecalculatedd_estDisabled count estimateEstimated count of disabled populationACS 5-yeard_est_moeDisabled count margin of errorMargin of error for estimated count of disabled populationACS 5-yeard_pctDisabled percent estimateEstimated percentage of disabled populationACS 5-yeard_pct_moeDisabled percent margin of errorMargin of error for percentage of disabled populationACS 5-yeard_pctileDisabled percentileTract's regional percentile for percentage disabledcalculatedd_scoreDisabled percentile scoreCorresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4calculatedem_classEthnic minority percentile classClassification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above averagecalculatedem_estEthnic minority count estimateEstimated count of Hispanic/Latino populationACS 5-yearem_est_moeEthnic minority count margin of errorMargin of error for estimated count of Hispanic/Latino populationACS 5-yearem_pctEthnic minority percent estimateEstimated percentage of Hispanic/Latino populationcalculatedem_pct_moeEthnic minority percent margin of errorMargin of error for percentage of Hispanic/Latino populationcalculatedem_pctileEthnic minority percentileTract's regional percentile for percentage Hispanic/Latinocalculatedem_scoreEthnic minority percentile scoreCorresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4calculatedf_classFemale percentile classClassification of tract's female percentage as: well below average, below average, average, above average, or well above averagecalculatedf_estFemale count estimateEstimated count of female populationACS 5-yearf_est_moeFemale count margin of errorMargin of error for estimated count of female populationACS 5-yearf_pctFemale percent estimateEstimated percentage of female populationACS 5-yearf_pct_moeFemale percent margin of errorMargin of error for percentage of female populationACS 5-yearf_pctileFemale percentileTract's regional percentile for percentage femalecalculatedf_scoreFemale percentile scoreCorresponding numeric score for tract's female classification: 0, 1, 2, 3, 4calculatedfb_classForeign-born percentile classClassification of tract's foreign born percentage as: well below average, below average, average, above average, or well above averagecalculatedfb_estForeign-born count estimateEstimated count of foreign born populationACS 5-yearfb_est_moeForeign-born count margin of errorMargin of error for estimated count of foreign born populationACS 5-yearfb_pctForeign-born percent estimateEstimated percentage of foreign born populationcalculatedfb_pct_moeForeign-born percent margin of errorMargin of error for percentage of foreign born populationcalculatedfb_pctileForeign-born percentileTract's regional percentile for percentage foreign borncalculatedfb_scoreForeign-born percentile scoreCorresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4calculatedle_classLimited English proficiency percentile classClassification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above averagecalculatedle_estLimited English proficiency count estimateEstimated count of limited english proficiency populationACS 5-yearle_est_moeLimited English proficiency count margin of errorMargin of error for estimated count of limited english proficiency populationACS 5-yearle_pctLimited English proficiency percent estimateEstimated percentage of limited english proficiency populationACS 5-yearle_pct_moeLimited English proficiency percent margin of errorMargin of error for percentage of limited english proficiency populationACS 5-yearle_pctileLimited English proficiency percentileTract's regional percentile for percentage limited english proficiencycalculatedle_scoreLimited English proficiency percentile scoreCorresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4calculatedli_classLow-income percentile classClassification of tract's low income percentage as: well below average, below average, average, above average, or well above averagecalculatedli_estLow-income count estimateEstimated count of low income (below 200% of poverty level) populationACS 5-yearli_est_moeLow-income count margin of errorMargin of error for estimated count of low income populationACS 5-yearli_pctLow-income percent estimateEstimated percentage of low income (below 200% of poverty level) populationcalculatedli_pct_moeLow-income percent margin of errorMargin of error for percentage of low income populationcalculatedli_pctileLow-income percentileTract's regional percentile for percentage low incomecalculatedli_scoreLow-income percentile scoreCorresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4calculatedoa_classOlder adult percentile classClassification of tract's older adult percentage as: well below average, below average, average, above average, or well above averagecalculatedoa_estOlder adult count estimateEstimated count of older adult population (65 years or older)ACS 5-yearoa_est_moeOlder adult count margin of errorMargin of error for estimated count of older adult populationACS 5-yearoa_pctOlder adult percent estimateEstimated percentage of older adult population (65 years or older)ACS 5-yearoa_pct_moeOlder adult percent margin of errorMargin of error for percentage of older adult populationACS 5-yearoa_pctileOlder adult percentileTract's regional percentile for percentage older adultcalculatedoa_scoreOlder adult percentile scoreCorresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4calculatedrm_classRacial minority percentile classClassification of tract's non-white percentage as: well below average, below average, average, above average, or well above averagecalculatedrm_estRacial minority count estimateEstimated count of non-white populationACS 5-yearrm_est_moeRacial minority count margin of errorMargin of error for estimated count of non-white populationACS 5-yearrm_pctRacial minority percent estimateEstimated percentage of non-white populationcalculatedrm_pct_moeRacial minority percent margin of errorMargin of error for percentage of non-white populationcalculatedrm_pctileRacial minority percentileTract's regional percentile for percentage non-whitecalculatedrm_scoreRacial minority percentile scoreCorresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4calculatedtot_ppTotal population estimateEstimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born)ACS 5-yeartot_pp_moeTotal population margin of errorMargin of error for estimated total population of tractACS 5-yeary_classYouth percentile classClassification of tract's youth percentage as: well below average, below average, average, above

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over: Women was 1424.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over: Women reached a record high of 1424.00000 in January of 2024 and a record low of 912.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over: Women - last updated from the United States Federal Reserve on November of 2025.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources workers occupations: 16 years and over (LEU0257855900A) from 2011 to 2024 about human resources, second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Context

A striking graph from the Social Security Administration (https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.html) shows that median annual earnings for all men above the age of 20 have decreased since 1988: https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.svg" alt="">

I wanted to better understand how educational attainment has played a role in the above trend, and to come up with a model to forecast the future trend for earnings by educational attainment.

As I began looking at the data from the Bureau of Labor Statistics website, there was a striking trend: the median weekly earnings for all groups of people who did not have a bachelors degree or higher had decreased from 1979 levels, in constant 2020 dollars.

Content

I collated data from the US Bureau of Labor Statistics (https://www.bls.gov/webapps/legacy/cpsatab4.htm) and (https://www.bls.gov/cps/cpswktabs.htm) and the US Census Bureau (https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html) to create this dataset.

I have omitted details of gender and race, to solely look at the correlation between educational attainment and median weekly earnings over the years. All of the data is for ages 25 and higher unless otherwise stated in the column header.

An important note is that all the earnings data are in constant base 2020 dollars. This removes the effects of inflation and makes it possible to compare the numbers over the years.

The data starts at the year 1960, but unfortunately only overall labor force data, and population percentages of persons with a high school graduation (HSG) and persons with a Bachelors or Higher Degree are available. Median weekly earnings data categorized by educational attainment is available from 1979 onwards, while labor force data i.e., labor force level, labor force participation rate and the employment level by educational attainment is available only from 1992 onwards.

The only columns that have data from 1960 onwards are: (i) overall labor force level, (ii) civilian non-institutional population level, (iii) overall labor force participation rate, (iv) overall employment level, (v) overall percentage of high school graduates, and (vi) overall percentage of persons with a bachelors degree or higher.

Some of the columns can be calculated from other columns, for instance the civilian non-institutional population level can be calculated from the labor force participation rate.

Acknowledgements

All of this data is from the Bureau of Labor Statistics, and the Census Bureau: https://www.bls.gov/webapps/legacy/cpsatab4.htm , https://www.bls.gov/cps/cpswktabs.htm and https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html .

A big thank you to all those who worked so hard to collect and organize this data.

Inspiration

The main question is: what is the best way to generate forecasts for median weekly earnings for each educational attainment level?

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. table name indicator name Universe timeframe source race notes source url

below_fpl_perc below 100% federal poverty level percent (%) Population for whom poverty status is determined 2016-2020 American Community Survey - S1703 Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S1703

below_200fpl_perc below 200% federal poverty level percent (%) Total population 2021 Population and Poverty Estimates of Los Angeles County Tract-City Splits by Age, Sex and Race-Ethnicity for July 1, 2021, Los Angeles, CA, April 2022 All races are Non-Hispanic LA County eGIS-Demography

median_income Median income (household) Households 2016-2020 American Community Survey - S1903 All races are Non-Hispanic; Race is that of householder https://data.census.gov/cedsci/table?q=S1903&g=0500000US06037

percapita_income Mean Per Capita Income Total population 2016-2020 American Community Survey - S1902 Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S1902

college_degree_any College degree AA, BA, or Higher % Population 25 years and over 2021 American Community Survey - B15002B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?q=b15002b&g=0500000US06037

graduate_professional_degree Graduate or professional degree % Population 25 years and over 2021 American Community Survey - B15002B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?q=b15002b&g=0500000US06037

unemployment_rate Unemployment Rate Population 16 years and over 2016-2020 American Community Survey - S2301 Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?q=S2301%3A%20EMPLOYMENT%20STATUS&g=0500000US06037&tid=ACSST5Y2020.S2301

below_300fpl_food_insecure Percent of Households with Incomes <300% Federal Poverty Level That Are Food Insecure Percent of Households with Incomes <300% Federal Poverty Level 2018 Los Angeles County Health Survey

https://publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

below_185fpl_snap Percent of Adults (Ages 18 Years and Older) with Household Incomes <185% Federal Poverty Level Who Are Currently Receiving Supplemental Nutrition Assistance Program (SNAP), Also Known as Calfresh Adults (Ages 18 Years and Older) with Household Incomes <185% Federal Poverty Level Los Angeles County Health Survey 20182018 https://publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

B24010 Sex by Occupation for the Civilian Employed Population 16 Years and Over Civilian employed population 16 years and over