Introduction

The U.S. job market, with its dynamic trends and fluctuating unemployment rates, serves as an important barometer for the nation's economic health. All rates provided in this dataset are seasonally adjusted. Delving into the intricacies of unemployment rates by age and gender helps researchers, policymakers, and analysts uncover underlying patterns and address potential disparities.

Usage Examples

• Economic Research: Study the historical unemployment trends to gauge economic cycles.
• Policy Making: Inform labor market policies and interventions based on age or gender disparities.
• Business Strategy: Companies can analyze job market conditions when considering expansions or contractions.
• Academic Projects: Students and educators can use the dataset for case studies, dissertations, or classroom projects.

Image Source Photo by Ron Lach : https://www.pexels.com/photo/woman-looking-for-jobs-in-newspaper-9832700/

Dataset Contents

This dataset, sourced from the FRED API, provides: - df_sex_unemployment_rates.csv: A breakdown of U.S. unemployment rates based on gender. - df_unemployment_rates.csv: Unemployment rates categorized by various age groups, ranging from young entrants (ages 16-17) to seasoned professionals (55 and above).

Together, these data files offer a comprehensive insight into the nuances of unemployment in the U.S., highlighting potential disparities in the job market across different age groups and between men and women.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

This page shows total unemployment rates in the U.S., transportation and warehousing sector, transportation and material moving occupations, and men and women.

This dataset provides the unemployment rates and Proportion of Unemployed (PU) based on both usual status (ps+ss) and current weekly status (CWS). The data is sourced from the annual report of the Periodic Labour Force Survey (PLFS) conducted by the Ministry of Statistics and Programme Implementation. The data helps assess both long-term and short-term unemployment trends within the population. The data is available by region- urban and rural, and gender- male and female. The years covered in the survey are from July to June. For instance, 2023-24 refers to the period July 2023 to June 2024 and likewise for other years.

Contains the monthly unemployment rate from 1948-2021. None of the data is seasonally adjusted. This file also contains information on subsets of the population, including based on age ranges from 16-55 and over, and unemployment rates for men and women. This data is collected by the US Bureau of Labor Statistics.

Explore labor force indicators dataset including information on employed persons, unemployment rate, population groups excluded from the labor force, and more. Access data on male and female populations, participation rates, and employment statistics in Saudi Arabia.

Population not in the Labour Force, Employed persons, Unemployment Rate, Male, Unemployment, %, Total, Population Groups Excluded from the Labor Force, Female, Number, Labor Force, Population, Unemployment, Participation Rate, Employment, Labor, Labor Force data

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Sources:Total Employed Persons - Saudi Employed Persons - Non-Saudi Employed Persons : GOSI , MCS, MLSDSaudi Job Seekers: HRDF, MCS, NICother indicators: Estimated data from the GaStat Labor Force Survey (LFS)Data do not include employees in the security and military sectors and non-registered in the records of GOSI, MCSFor data after 2016 go to : Main Labor Market Indicators

This dataset presents the annual unemployment rate for youth aged 15–24 years in Qatar, disaggregated by nationality (Qatari and non-Qatari) and gender (male and female). It includes breakdowns by population group and overall totals, enabling insight into youth labor force participation and unemployment patterns. The dataset is useful for youth employment policy and economic planning.

This dataset presents the annual unemployment rate in Qatar for individuals aged 15 years and above, disaggregated by nationality (Qatari and non-Qatari) and gender (male and female). It includes specific breakdowns by group and overall totals by gender and combined population. The dataset helps monitor labor market conditions and supports workforce policy decisions.

College Majors and their Graduates

Job Opportunities, Salaries and Gender Disparities

By FiveThirtyEight [source]

About this dataset

This repository contains a comprehensive selection of lavish data and processing scripts behind the articles, graphics, and interactive experiences generated by FiveThirtyEight. With this dataset, you'll have the power to explore college programs and their graduates like never before and create stories of your own! Whether you use it to check our work or craft your own powerful visuals, we would absolutely love to know if you found it useful. Under the Creative Commons Attribution 4.0 International License and MIT License respectively, our data is available for anyone who chooses to use it. Let us know how our resources turned out at

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Create an interactive comparison tool for researching college majors and their earning potential, so that prospective students can make informed decisions about what to study.
• Analyze the proportions of male and female graduates across different majors to uncover gender disparities in higher education.
• Explore the correlations between major categories, average salaries earned by graduates from specific major categories, unemployment rates for those with specific majors and more – to identify trends in job opportunities for certain specialties of study

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: majors-list.csv | Column name | Description | |:-------------------|:----------------------------------------------------| | FOD1P | First-level division of the field of study (String) | | Major | The specific major of the field of study (String) | | Major_Category | The broader category of the field of study (String) |

File: recent-grads.csv | Column name | Description | |:-------------------------|:-------------------------------------------------------------------------------| | Major | The specific major of the field of study (String) | | Rank | The rank of the major in terms of popularity (Integer) | | Major_code | The code associated with the major (Integer) | | Major_category | The category of the major (String) | | Total | The total number of students in the major (Integer) | | Sample_size | The sample size of the major (Integer) | | Men | The number of male students in the major (Integer) | | Women | The number of female students in the major (Integer) | | ShareWomen | The percentage of female students in the major (Float) | | Employed | The number of employed graduates from the major (Integer) | | Full_time | The number of full-time employed graduates from the major (Integer) | | Part_time | The number of part-time employed graduates from the major (Integer) | | Full_time_year_round | The number of full-time year-round employed graduates from the major (Integer) | | Unemployed | The number of unemployed graduates from the major (Integer) | | Unemployment_rate | The unemployment rate of graduates from the major (Float) | | Median | The median salary of graduates from the major (Integer) | | P25th | The 25th percentile salary of graduates from the major (Integer) | | P75th | The 75th percentile salary of graduates from the major (Integer) | | College_jobs | The number of college jobs held by graduates from the major...

Unemployment Rate in Spain increased to 10.45 percent in the third quarter of 2025 from 10.29 percent in the second quarter of 2025. This dataset provides the latest reported value for - Spain Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides information on the unemployment rates for different demographic groups in the United States.

The data is sourced from the Economic Policy Institute’s State of Working America Data Library and economic research conducted by the Federal Reserve Bank of St. Louis.

The dataset contains unemployment rates for various age groups, education levels, genders, races, and more.

Interesting Task Ideas:

1. See how unemployment rates have changed for different groups of people over time.
2. Look into how education levels can affect unemployment rates.
3. Compare unemployment rates between different races / genders.
4. Check out how unemployment rates can vary across different age groups and genders.
5. Find out if there's a connection between education levels and unemployment rates within specific racial or gender groups.
6. Explore how economic downturns can impact unemployment rates for specific groups of people.
7. Use the data to create visuals that show how unemployment rates differ across all sorts of factors.

Don't forget to upvote this dataset if you find it useful! 😊💝

Checkout my other datasets

Pension Coverage in the USA

Non-High School Wage Penalty

Health Insurance Coverage in the USA

USA Hispanic-White Wage Gap Dataset

Black-White Wage Gap in the USA Dataset

Column Descriptions

Series Name: Unemployment rate by sex and disability (percent)Series Code: SL_TLF_UEMDISRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilitiesTarget 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal valueGoal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Data on the activity rate, employment rate and unemployment rate by quarter since 1996; in total, men and women; and by age (16-24, 26-54 and 55 and over).

Context

The datasets gives the change in labor participation and unemployment rate over the years based on gender- Men & women and age- 15 to 64.

Content

This data is acquired from https://data.oecd.org/ and it provides data of more than 15 countries from 1990 to 2015 with 5 years gaps between them and afterwards for all the years: 2016,2017,2017,2019. The values are given in percentages. To get other datasets involving employment please visit https://data.oecd.org/searchresults/?q=labor

Acknowledgements

A huge shoutout to https://data.oecd.org/ for keeping all datafiles public.

Inspiration

Countries with highest unemployment rate and labor participation How unemployment and labor participation has changed based on gender OPEN FOR EXPLORATATION..........

Employment, unemployment and economic inactivity for men aged from 16 to 64 and women aged from 16 to 59 (seasonally adjusted). The employment and inactivity rates shown in this table were the headline employment and inactivity rates until August 2010, when ONS replaced these headline rates with rates for those aged from 16 to 64 for both men and women. These new headline rates for those aged from 16 to 64 are shown in Table A02 SA. These estimates are sourced from the Labour Force Survey, a survey of households.

Estimates of labour market status (employment, unemployment and inactivity) for all people, men and women aged 16 to 24 by ethnicity for Quarter 4 each year from 2006 to 2011. The estimates for men and women are subject to much higher sampling variability than those for all people and should be used with caution. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Labour market status for young people by ethnicity

Introduction

As a part of the Google Data Analytics Professional Certificate Program, this case study serves as a data analytics adventure and a way to dive into something personal. While many face the difficulty of finding employment out of college, it became especially tedious to do so due to the COVID-19 pandemic. As such, this case study revolves around unemployment trends from 2021 using data sourced from the United States Bureau of Labor Statistics. I used datasets surrounding unemployment and employment trends in 2021 to answer the following:

Questions

1. What methods for job searching were the most prevalent across age ranges? Across gender/race/Hispanic-Latino ethnicity?
2. What trends exist between and within the most prevalent venues for job searching among the unemployed?
3. What job sector(s) does the majority of the population comprise? What trends exist within and between the most popular job sector and the least popular job sector? What relationship do these factors have with race/gender/Hispanic-Latino ethnicity?
4. How does information about prevalent job searching influence the job market and the applicants in the job search phase?

Insights (see the data section below for charts, graphs, and the .Rmd file I utilized)

• In 2021, the unemployed, with ages ranging from 16-65, preferred resumes and applications as their method for seeking out jobs. This method was especially prevalent in the age range 16-34, where, the highest bracket of job seekers were 24-35 years old. A close second was contacting an employer directly, primarily used by 45-64-year-olds. When considering gender/ethnicity/race, however, compared to their male counterparts, white women and women of color were the highest users of the resumes and applications method. However, white males and men of color were the highest users of the contacting employers directly method.
• Among the unemployed resumes were overall the most prevalent method of applying for jobs in 2021, where, people aged 16--34 and women regardless of ethnicity/race were the most likely to utilize this method to search for jobs.
• The majority of the population resides in the "Management, Professional, and related occupations" job sector, with the least popular form of occupations being in the "Farming, Fishing, and Forestry" sector. This sentiment can be found almost across all genders/races/ethnicities, though, some other job sectors like "Production, transportation, and material moving occupations" and "Natural resources, construction, and maintenance occupations" were more prevalent concerning the Black/African American men, Hispanic/Latino women, and Hispanic/Latino men respectively.
• This information is highly useful for job industries, specifically, those in the "Management, Professional, and related occupations" sector. With this, industries in this job sector can project what their incoming job applicant pool may look like and how to prepare for making the application process more accessible. This information can also serve to reinforce fairness and inclusivity in the job application process and in the work environment.

** Overall**

Using this information a company can project in 2022-2023 the majority of applicants will either apply to jobs using resumes/applications, the majority of these applicants may be 16-34 years old, and women regardless of ethnicity and race. They can also look out for applicants who are older, 45-64 years old, and applicants who are men regardless of ethnicity and race, being more likely to contact them as an employer directly. If an employer prefers to be directly contacted, they should make sure to consider the difficulties that people of different race/ethnic/and gender identities may have done so, and, either should either make the job positing more welcoming and inclusive to do so or, be sure to include a process of hiring via resumes/applications in order to better represent the unemployed population seeking jobs.

Employment by industry and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

The Labour Force Survey (LFS) is a household survey carried out monthly by Statistics Canada. Since its inception in 1945, the objectives of the LFS have been to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these categories. Data from the survey provide information on major labour market trends such as shifts in employment across industrial sectors, hours worked, labour force participation and unemployment rates, employment including the self-employed, full and part-time employment, and unemployment. It publishes monthly standard labour market indicators such as the unemployment rate, the employment rate and the participation rate. The LFS is a major source of information on the personal characteristics of the working-age population, including age, sex, marital status, educational attainment, and family characteristics. Employment estimates include detailed breakdowns by demographic characteristics, industry and occupation, job tenure, and usual and actual hours worked. This dataset is designed to provide the user with historical information from the Labour Force Survey. The tables included are monthly and annual, with some dating back to 1976. Most tables are available by province as well as nationally. Demographic, industry, occupation and other indicators are presented in tables derived from the LFS data. The information generated by the survey has expanded considerably over the years with a major redesign of the survey content in 1976 and again in 1997, and provides a rich and detailed picture of the Canadian labour market. Some changes to the Labour Force Survey (LFS) were introduced which affect data back to 1987. There are three reasons for this revision: The revision enables the use of improved population benchmarks in the LFS estimation process. These improved benchmarks provide better information on the number of non-permanent residents. There are changes to the data for the public and private sectors from 1987 to 1999. In the past, the data on the public and private sectors for this period were based on an old definition of the public sector. The revised data better reflects the current public sector definition, and therefore result in a longer time series for analysis. The geographic coding of several small Census Agglomerations (CA) has been updated historically from 1996 urban centre boundaries to 2001 CA boundaries. This affects data from January 1987 to December 2004. It is important to note that the changes to almost all estimates are very minor, with the exception of the public sector series and some associated industries from 1987 to 1999. Rates of unemployment, employment and participation are essentially unchanged, as are all key labour market trends. The article titled Improvements in 2006 to the LFS (also under the LFS Documentation button) provides an overview of the effect of these changes on the estimates. The seasonally-adjusted tables have been revised back three years (beginning with January 2004) based on the latest seasonal output.

Data available on the Webstat tool of the Banque de France. Name of the dataset: Unemployment rate & Periodicity: Monthly = & Statistical correction: CVS = & Concept — STS context: Unemployment rate = & Classification — STS context: Rates, Total (all ages), Total (men and women) = & Institution of origin: Eurostat = & Variation — STS context: Not specified