Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Replication package for "Business disruptions from social distancing"

Please cite as

Koren, Miklós and Rita Pető. 2020. "Replication package for «Business disruptions from social distancing»" [dataset] Zenodo. http://doi.org/10.5281/zenodo.4012191

License and copyright

All text (*.md, *.txt, *.tex, *.pdf) are CC-BY-4.0. All code (*.do, Makefile) are subject to the 3-clause BSD license. All derived data (data/derived/*) are subject to Open Database License. Please respect to copyright and license terms of original data vendors (data/raw/*).

Data Availability Statements

The mobility data used in this paper (SafeGraph 2020) is proprietary, but may be obtained free of charge for COVID-19-related research from the COVID-19 Consortium. The authors are not affiliated with this consortium. Researchers interested in access to the data can apply at https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com). After signing a Data Agreement, access is granted within a few days. The Consortium does not require coauthorship and does not review or approve research results before publication. Datafiles used: /monthly-patterns/patterns_backfill/2020/05/07/12/2020/02/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released May 7, 2020), /monthly-patterns/patterns/2020/06/05/06/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released June 5, 2020) and /core/2020/06/Core-USA-June2020-Release-CORE_POI-2020_05-2020-06-06.zip (Core Places for June 2020, released June 6, 2020). The COVID-19 Consortium will keep these datafiles accessible for researchers. The authors will assist with any reasonable replication attempts for two years following publication.

All other data used in the analysis, including raw data, are available for reuse with permissive licenses. Raw data are saved in the folder data/raw/. The Makefile in each folder shows the URLs used to download the data.

SafeGraph

Citation

SafeGraph. "Patterns [dataset]"; 2020. Downloaded 2020-06-20.

License

Proprietary, see https://shop.safegraph.com/ or https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com)

O*NET

Citation

U.S. Department of Labor/Employment and Training Administration, 2020. "O*NET Online." Downloaded 2020-03-12.

License

CC-BY-4.0 https://www.onetonline.org/help/license

Current Employment Statistics

Citation

U.S. Bureau of Labor Statistics. 2020. "Current Employment Statistics." https://www.bls.gov/ces/ Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

National Employment Matrix

Citation

U.S. Bureau of Labor Statistics. 2018. "National Employment Matrix." https://www.bls.gov/emp/data/occupational-data.htm Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

Crosswalk

Citation

U.S. Bureau of Labor Statistics. 2019. "O* NET-SOC to Occupational Outlook Handbook Crosswalk." https://www.bls.gov/emp/classifications-crosswalks/nem-onet-to-soc-crosswalk.xlsx Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

American Time Use Survey

Citation

U.S. Bureau of Labor Statistics. 2018. “American Time Use Survey.” https://www.bls.gov/tus/.

We are using the following files:

• Respondent File
• Activity File
• Who File
• Replicate Weights
• Leave Module 2017-18

License

Data is in public domain.

County Business Patterns

Citation

U.S. Bureau of the Census. 2017. "County Business Patterns." Available at https://www.census.gov/programs-surveys/cbp.html

License

https://www.census.gov/data/developers/about/terms-of-service.html

Dataset list

Raw data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/raw/bls/industry-employment/ces.txt | BLS Current Employment Statistics | Public domain | Yes |

| data/raw/bls/atus/*.dat | BLS Time Use Survey | Public domain | Yes |

| data/raw/bls/employment-matrix/matrix.xlsx | BLS National Employment Matrix | Public domain | Yes |

| data/raw/bls/crosswalk/matrix.xlsx | ONET-SOC to Occupational Outlook Handbook Crosswalk | Public domain | Yes |

| data/raw/onet/*.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/raw/census/cbp/*.txt | County Business Patterns | Public domain | Yes |

| not-included/safegraph/02/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

| not-included/safegraph/05/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

Clean data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/clean/industry-employment/industry-employment.dta | BLS Current Employment Statistics | Public domain | Yes |

| data/clean/time-use/atus.dta | BLS Time Use Survey | Public domain | Yes |

| data/clean/employment-matrix/matrix.dta | BLS National Employment Matrix | Public domain | Yes |

| data/clean/onet/risks.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/clean/cbp/zip_code_business_patterns.dta | County Business Patterns | Public domain | Yes |

Derived data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/derived/occupation/* | Various sources | Public domain | Yes |

| data/derived/time-use/atus_working_at_home_occupationlevel.dta | BLS Time Use Survey | Public domain | Yes |

| data/derived/crosswalk/* | Various sources | Public domain | Yes |

| not-included/safegraph/naics-zip-??.csv| SafeGraph | Available with Data Agreement with SafeGraph | Yes, with permission of SafeGraph |

| data/derived/visit/visit-change.dta| SafeGraph | Aggregated to 3-digit NAICS industries | Yes, with permission of SafeGraph |

Computational requirements

Software Requirements

• Stata (code was last run with version 16.1)
  • estout (from http://www.stata-journal.com/software/sj14-2/)
  • running make install from the root of the folder will install estout locally, and should be run once.

Portions of the code use bash scripting (make, wget, head, tail), which may require Linux or Mac OS X.

The entry point for analysis is analysis/Makefile, which can be run by GNU Make on any Unix-like system by

cd analysis
make

The dependence of outputs on code and input data is captured in the respective Makefiles.

We have used Mac OS X, but all the code should run on Linux and Windows platforms, too.

Hardware

The analysis takes a few minutes on a standard laptop.

Description of programs

1. Raw data are in data/raw/. This data is saved as it has been received from the data publisher, downloaded by the respective Makefiles. Each folder has a README.md with data citation and license terms.
2. Clean data are in data/clean/. Each folder has a Makefile that specifies the steps of data cleaning.
3. Analysis data are in data/derived/. Each folder has a Makefile that

Replication package for "Not all leisure is created equal: Income-induced constraints on the enjoyment of leisure." This package uses American Time Use Survey data made publicly available by the U.S. Bureau of Labor Statistics at https://www.bls.gov/tus/data.htm (see README file for specific files used). We use this data to show that income enhances enjoyment during leisure activities. Our results also examine the mechanisms underlying this positive relationship. The full paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4896455

United States WE: Age 16 & Over: No. of Workers: White: Male data was reported at 49,898.000 Person th in Mar 2020. This records a decrease from the previous number of 50,996.000 Person th for Dec 2019. United States WE: Age 16 & Over: No. of Workers: White: Male data is updated quarterly, averaging 48,281.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 51,479.000 Person th in Sep 2019 and a record low of 44,040.000 Person th in Mar 2010. United States WE: Age 16 & Over: No. of Workers: White: Male data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

United States WE: Age 16 & Over: No. of Workers: White data was reported at 89,310.000 Person th in Mar 2020. This records a decrease from the previous number of 90,753.000 Person th for Dec 2019. United States WE: Age 16 & Over: No. of Workers: White data is updated quarterly, averaging 83,722.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 90,753.000 Person th in Dec 2019 and a record low of 78,418.000 Person th in Mar 2010. United States WE: Age 16 & Over: No. of Workers: White data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

United States WE: Age 25 & Over: Female: BH: Num Workers: Bachelor's Deg Only (BO) data was reported at 13,960.000 Person th in Mar 2020. This records a decrease from the previous number of 14,100.000 Person th for Dec 2019. United States WE: Age 25 & Over: Female: BH: Num Workers: Bachelor's Deg Only (BO) data is updated quarterly, averaging 10,168.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 14,100.000 Person th in Dec 2019 and a record low of 8,007.000 Person th in Sep 2000. United States WE: Age 25 & Over: Female: BH: Num Workers: Bachelor's Deg Only (BO) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

Analysis of ‘US Minimum Wage by State from 1968 to 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

US Minimum Wage by State from 1968 to 2020

The Basics

• What is this? In the United States, states and the federal government set minimum hourly pay ("minimum wage") that workers can receive to ensure that citizens experience a minimum quality of life. This dataset provides the minimum wage data set by each state and the federal government from 1968 to 2020.

• Why did you put this together? While looking online for a clean dataset for minimum wage data by state, I was having trouble finding one. I decided to create one myself and provide it to the community.

• Who do we thank for this data? The United States Department of Labor compiles a table of this data on their website. I took the time to clean it up and provide it here for you. :) The GitHub repository (with R Code for the cleaning process) can be found here!

Content

This is a cleaned dataset of US state and federal minimum wages from 1968 to 2020 (including 2020 equivalency values). The data was scraped from the United States Department of Labor's table of minimum wage by state.

Description of Data

The values in the dataset are as follows: - Year: The year of the data. All minimum wage values are as of January 1 except 1968 and 1969, which are as of February 1. - State: The state or territory of the data. - State.Minimum.Wage: The actual State's minimum wage on January 1 of Year. - State.Minimum.Wage.2020.Dollars: The State.Minimum.Wage in 2020 dollars. - Federal.Minimum.Wage: The federal minimum wage on January 1 of Year. - Federal.Minimum.Wage.2020.Dollars: The Federal.Minimum.Wage in 2020 dollars. - Effective.Minimum.Wage: The minimum wage that is enforced in State on January 1 of Year. Because the federal minimum wage takes effect if the State's minimum wage is lower than the federal minimum wage, this is the higher of the two. - Effective.Minimum.Wage.2020.Dollars: The Effective.Minimum.Wage in 2020 dollars. - CPI.Average: The average value of the Consumer Price Index in Year. When I pulled the data from the Bureau of Labor Statistics, I selected the dataset with "all items in U.S. city average, all urban consumers, not seasonally adjusted". - Department.Of.Labor.Uncleaned.Data: The unclean, scraped value from the Department of Labor's website. - Department.Of.Labor.Cleaned.Low.Value: The State's lowest enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.High.Value are identical. (Some states enforce different minimum wage laws depending on the size of the business. In states where this is the case, generally, smaller businesses have slightly lower minimum wage requirements.) - Department.Of.Labor.Cleaned.Low.Value.2020.Dollars: The Department.Of.Labor.Cleaned.Low.Value in 2020 dollars. - Department.Of.Labor.Cleaned.High.Value: The State's higher enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.Low.Value are identical. - Department.Of.Labor.Cleaned.High.Value.2020.Dollars: The Department.Of.Labor.Cleaned.High.Value in 2020 dollars. - Footnote: The footnote provided on the Department of Labor's website. See more below.

Data Footnotes

As laws differ significantly from territory to territory, especially relating to whom is protected by minimum wage laws, the following footnotes are located throughout the data in Footnote to add more context to the minimum wage. The original footnotes can be found here.

--- Original source retains full ownership of the source dataset ---

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

BackgroundThe fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations.MethodWe first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes.ResultsThe results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years.ConclusionOur results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.

United States WE: Age 16 & Over: No. of Workers: Black or African American (BA) data was reported at 14,902.000 Person th in Mar 2020. This records a decrease from the previous number of 15,463.000 Person th for Dec 2019. United States WE: Age 16 & Over: No. of Workers: Black or African American (BA) data is updated quarterly, averaging 12,501.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 15,843.000 Person th in Sep 2019 and a record low of 11,375.000 Person th in Mar 2010. United States WE: Age 16 & Over: No. of Workers: Black or African American (BA) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

This dataset includes information on labor force activity for the week prior to the survey. Comprehensive data are provided on the employment status, occupation, and industry of persons 14 years old and over. Also included are personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin. The supplement includes data on pension and retirement plan coverage through employer- or union-sponsored retirement plans, as well as individual retirement plans (IRAs) and Keoghs. Questions were asked of all persons employed for pay in four rotation groups common to the March 1988 Current Population Survey (CPS). Income and work experience data from the March income supplement are also included for individuals in these four rotation groups. In addition, the May supplement file was matched to the June CPS file to pick up that month's earnings data, and it was matched to the March income supplement to pick up detailed income information. The May supplement can be viewed as having three distinct parts: CPS labor force data, employee benefits supplement data, and March income data. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09316.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

United States WE: Age 25 & Over: No. of Workers data was reported at 106,239.000 Person th in Mar 2020. This records a decrease from the previous number of 108,100.000 Person th for Dec 2019. United States WE: Age 25 & Over: No. of Workers data is updated quarterly, averaging 94,167.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 108,100.000 Person th in Dec 2019 and a record low of 88,575.000 Person th in Mar 2002. United States WE: Age 25 & Over: No. of Workers data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

Data on labor force activity for the week prior to the survey are supplied in this collection. Information is available on the employment status, occupation, and industry of persons 14 years old and over. Demographic variables such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin are included as well. In addition to providing these core data, the October CPS survey also contains a special supplement on school enrollment. This supplement, which furnishes data for both adults and children, offers information on continuing education, previous year's enrollment, degree anticipated, grade or year of school attended, and whether school attended is public or private. New items introduced in the 1984 supplement deal with direct or "hands-on" use of computers. These additional variables include presence of computers in the home, school, and workplace, purposes for which home computers are used, and average time spent weekly working with computers. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08537.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

United States WE: Full Time: PT: Female data was reported at 619.000 USD in Mar 2020. This records an increase from the previous number of 606.000 USD for Dec 2019. United States WE: Full Time: PT: Female data is updated quarterly, averaging 474.000 USD from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 619.000 USD in Mar 2020 and a record low of 361.000 USD in Mar 2000. United States WE: Full Time: PT: Female data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

United States WE: Age 25 & Over: No. of Workers: Female data was reported at 47,941.000 Person th in Mar 2020. This records a decrease from the previous number of 48,763.000 Person th for Dec 2019. United States WE: Age 25 & Over: No. of Workers: Female data is updated quarterly, averaging 41,553.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 48,763.000 Person th in Dec 2019 and a record low of 38,679.000 Person th in Sep 2000. United States WE: Age 25 & Over: No. of Workers: Female data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

United States WE: Age 16 & Over: No. of Workers: White: Female data was reported at 39,412.000 Person th in Mar 2020. This records a decrease from the previous number of 39,757.000 Person th for Dec 2019. United States WE: Age 16 & Over: No. of Workers: White: Female data is updated quarterly, averaging 35,670.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 39,757.000 Person th in Dec 2019 and a record low of 34,378.000 Person th in Mar 2010. United States WE: Age 16 & Over: No. of Workers: White: Female data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

United States WE: Age 25 & Over: No. of Workers: Male data was reported at 58,297.000 Person th in Mar 2020. This records a decrease from the previous number of 59,337.000 Person th for Dec 2019. United States WE: Age 25 & Over: No. of Workers: Male data is updated quarterly, averaging 52,733.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 59,337.000 Person th in Dec 2019 and a record low of 48,818.000 Person th in Mar 2010. United States WE: Age 25 & Over: No. of Workers: Male data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

United States WE: Age 16 & Over: No. of Workers: HL: Male data was reported at 12,470.000 Person th in Mar 2020. This records a decrease from the previous number of 12,584.000 Person th for Dec 2019. United States WE: Age 16 & Over: No. of Workers: HL: Male data is updated quarterly, averaging 9,821.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 12,783.000 Person th in Sep 2019 and a record low of 7,830.000 Person th in Mar 2000. United States WE: Age 16 & Over: No. of Workers: HL: Male data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

United States WE: Age 25 & Over: No. of Workers: Male: Some College or Degree data was reported at 14,404.000 Person th in Mar 2020. This records a decrease from the previous number of 14,881.000 Person th for Dec 2019. United States WE: Age 25 & Over: No. of Workers: Male: Some College or Degree data is updated quarterly, averaging 13,477.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 14,881.000 Person th in Dec 2019 and a record low of 12,376.000 Person th in Mar 2010. United States WE: Age 25 & Over: No. of Workers: Male: Some College or Degree data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.