In October 2024, the total nonfarm payroll employment increased by around 12,000 people in the United States. The data are seasonally adjusted. According to the BLS, the data is derived from the Current Employment Statistics (CES) program which surveys about 140,000 businesses and government agencies each month, representing approximately 440,000 individual worksites, in order to provide detailed industry data on employment.

This dataset from the Bureau of Labor Statistics provides monthly estimates regarding total employment and unemployment, which together comprise the labor force. The unemployment rate is the percentage of people in the labor force who are unemployed. Our data extract lists all data published for North Carolina’s counties from January 2019 to the present. This dataset is a comprehensive nationwide representation using estimates derived from the national Current Population Survey (CPS) and American Community Survey 5-year estimates. No disaggregations by demographic or worker characteristics are included in the unemployment rate estimate. Time series reports for each variable (employment, unemployment, and labor force) are available for each geography (county) using the BLS multi-screen data tool. Preliminary estimates are released within 30 days of each month and finalized within another 30 days, resulting in a 2-month data lag. The data is available for a variety of geographic areas, including states, MSAs, counties, cities and towns, and other census regions.

Non Farm Payrolls in the United States increased by 73 thousand in July of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

Graph and download economic data for All Employees, Manufacturing (MANEMP) from Jan 1939 to Jul 2025 about headline figure, establishment survey, manufacturing, employment, and USA.

The Work Stoppages program provides monthly and annual data and analysis of major work stoppages involving 1,000 or more workers lasting one full shift or longer. The monthly and annual data show the establishment and union(s) involved in the work stoppage along with the location, the number of workers and the days of idleness. The monthly data list all work stoppages involving 1,000 or more workers that occurred during the full calendar month for each month of the year. The annualized data provide statistics, analysis and details of each work stoppage of 1,000 or more workers that occurred during the year. The work stoppages data are gathered from public news sources, such as newspapers and the Internet. The BLS does not distinguish between strikes and lock-outs in the data; both are included in the term "work stoppages". For more information and data visit: https://www.bls.gov/wsp/

Abstract

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS)

Documentation

Update frequency: Monthly Dataset source: U.S. Bureau of Labor Statistics Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/bls-public-data/bureau-of-labor-statistics

VITAL SIGNS INDICATOR
Unemployment (EC3)

FULL MEASURE NAME
Unemployment rate by residential location

LAST UPDATED
December 2022

DESCRIPTION
Unemployment refers to the share of the labor force – by place of residence – that is not currently employed full-time or part-time. The unemployment rate reflects the strength of the overall employment market.

DATA SOURCE
California Employment Development Department: Historical Unemployment Rates
1990-2010
Spreadsheet provided by CAEDD

California Employment Development Department: Labor Force and Unemployment Rate for California Sub-County Areas - https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Labor-Force-and-Unemployment-Rate-for-California-S/8z4h-2ak6
2010-2022

California Employment Development Department: Local Area Unemployment Statistics (LAUS) - https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Local-Area-Unemployment-Statistics-LAUS-/e6gw-gvii
1990-2022

U.S. Bureau of Labor Statistics: Local Area Unemployment Statistics (LAUS) - https://download.bls.gov/pub/time.series/la
1990-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Unemployment rates produced by the CA Employment Development Department (EDD) for the region and county levels are not adjusted for seasonality (as they reflect annual data) and are final data (i.e., not preliminary). Unemployment rates produced by U.S. Bureau of Labor Statistics (BLS) for the metro regions are annual and not adjusted for seasonality; they reflect the primary metropolitan statistical area (MSA) for the named region, except for the San Francisco Bay Area which uses the nine-county region. The unemployment rate is calculated based on the number of unemployed persons divided by the total labor force. Note that the unemployment rate can decline or increase as a result of changes in either variable.

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XV. APPENDIX 4). The microdata are available online at http://www/bls.gov/cex/pumdhome.htm. These microdata files present detailed expenditure and income data for the Diary component of the CE for 2002. They include weekly expenditure (EXPD) and annual income (DTBD) files. The data in EXPD and DTBD files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLD and MEMD files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files. Estimates of average expenditures in 2002 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2002. A list of recent publications containing data from the CE appears at the end of this documentation. The microdata files are in the public domain and with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2002".

Analysis unit

Consumer Units

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian noninstitutional persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2002 sample is composed of 105 areas. The design classifies the PSUs into four categories: • 31 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 46 "B" PSUs, are medium-sized MSA's. • 10 "C" PSUs are nonmetropolitan areas that are included in the CPI. • 18 "D" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2002 survey is generated from the 1990 Population Census 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (ED's) from the Census that fail to meet the criterion for good addresses for new construction, and all ED's in nonpermit-issuing areas are grouped into the area segment frame. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year. During the last 6 weeks of the year, however, the Diary Survey sample is supplemented to twice its normal size to increase the reporting of types of expenditures unique to the holidays.

STATE IDENTIFIER Since the CE is not designed to produce state-level estimates, summing the consumer unit weights by state will not yield state population totals. A CU's basic weight reflects its probability of selection among a group of primary sampling units of similar characteristics. For example, sample units in an urban nonmetropolitan area in California may represent similar areas in Wyoming and Nevada. Among other adjustments, CUs are post-stratified nationally by sex-age-race. For example, the weights of consumer units containing a black male, age 16-24 in Alabama, Colorado, or New York, are all adjusted equivalently. Therefore, weighted population state totals will not match population totals calculated from other surveys that are designed to represent state data. To summarize, the CE sample was not designed to produce precise estimates for individual states. Although state-level estimates that are unbiased in a repeated sampling sense can be calculated for various statistical measures, such as means and aggregates, their estimates will generally be subject to large variances. Additionally, a particular state-population estimate from the CE sample may be far from the true state-population estimate.

INTERPRETING THE DATA Several factors should be considered when interpreting the expenditure data. The average expenditure for an item may be considerably lower than the expenditure by those CUs that purchased the item. The less frequently an item is purchased, the greater the difference between the average for all consumer units and the average of those purchasing. (See Section V.B. for ESTIMATION OF TOTAL AND MEAN EXPENDITURES). Also, an individual CU may spend more or less than the average, depending on its particular characteristics. Factors such as income, age of family members, geographic location, taste and personal preference also influence expenditures. Furthermore, even within groups with similar characteristics, the distribution of expenditures varies substantially. Expenditures reported are the direct out-of-pocket expenditures. Indirect expenditures, which may be significant, may be reflected elsewhere. For example, rental contracts often include utilities. Renters with such contracts would record no direct expense for utilities, and therefore, appear to have no utility expenses. Employers or insurance companies frequently pay other costs. CUs with members whose employers pay for all or part of their health insurance or life insurance would have lower direct expenses for these items than those who pay the entire amount themselves. These points should be considered when relating reported averages to individual circumstances.

Mode of data collection

Computer Assisted Personal Interview [capi]

Graph and download economic data for All Employees, Federal (CES9091000001) from Jan 1939 to Jul 2025 about establishment survey, federal, government, employment, and USA.

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

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XV. APPENDIX 4). The microdata are available online at http://www/bls.gov/cex/pumdhome.htm.

These microdata files present detailed expenditure and income data from the Interview component of the CE for 2003 and the first quarter of 2004. The Interview survey collects data on up to 95 percent of total household expenditures. In addition to the FMLI, MEMI, MTBI, and ITBI files, the microdata include files created directly from the expenditure sections of the Interview survey (EXPN files). The EXPN files contain expenditure data and ancillary descriptive information, often not available on the FMLI or MTBI files, in a format similar to the Interview questionnaire. In addition to the extra information available on the EXPN files, users can identify distinct spending categories easily and reduce processing time due to the organization of the files by type of expenditure.

Estimates of average expenditures in 2003 from the Interview Survey, integrated with data from the Diary Survey, will be published in the report Consumer Expenditures in 2003. A list of recent publications containing data from the CE appears at the end of this documentation.

The microdata files are in the public domain and, with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Interview Survey, 2003."

Analysis unit

Consumer Units

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian non-institutionalized persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2003 and 2004 samples is composed of 105 areas. The design classifies the PSUs into four categories: • 31 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 46 "B" PSUs, are medium-sized MSA's. • 10 "C" PSUs are nonmetropolitan areas that are included in the CPI. • 18 "D" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2003 and 2004 surveys is generated from the 1990 Census of Population 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (EDs) from the Census that fail to meet the criterion for good addresses for new construction, and all EDs in non-permit-issuing areas are grouped into the area segment frame. Interviewers are then assigned to list these areas before a sample is drawn. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. The Interview Survey is a panel rotation survey. Each panel is interviewed for five consecutive quarters and then dropped from the survey. As one panel leaves the survey, a new panel is introduced. Approximately 20 percent of the addresses are new to the survey each month.

WEIGHTING Each CU included in the CE represents a given number of CUs in the U.S. population, which is considered to be the universe. The translation of sample families into the universe of families is known as weighting. However, since the unit of analysis for the CE is a CU, the weighting is performed at the CU level. Several factors are involved in determining the weight for each CU for which an interview is obtained. There are four steps in the weighting procedure: 1) The basic weight is assigned to an address and is the inverse of the probability of selection of the housing unit. 2) A weight control factor is applied to each interview if subsampling is performed in the field. 3) A noninterview adjustment is made for units where data could not be collected from occupied housing units. The adjustment is performed as a function of region, housing tenure, family size and race. 4) A final adjustment is performed to adjust the sample estimates to national population controls derived from the Current Population Survey. The adjustments are made based on both the CU's Member composition and the CU as a whole. The weight for the CU is adjusted for individuals within the CU to meet the controls for 14 age/race categories, 4 regions, and 4 region/urban categories. The CU weight is also adjusted to meet the control for total number of CUs and total number of CUs who own their living quarters. The weighting procedure uses an iterative process to ensure that the sample estimates meet all the population controls.

NOTE: The weight for a consumer unit (CU) can be different for each quarter in which the CU participates in the survey, as the CU may represent a different number of CUs with similar characteristics.

Mode of data collection

Computer Assisted Personal Interview [capi]

Data Build Appendix for “The Response of Consumer Spending to Changes in Gasoline Prices" By Michael Gelman, Yuriy Gorodnichenko, Shachar Kariv, Dmitri Koustas, Matthew D. Shapiro, Dan Silverman, and Steven Tadelis Overview We provide replication code to generate 4 figures and 6 tables in the paper. The raw (unaggregated) transactions data from the App cannot be disclosed or shared, so are not included in this repository. The deposited data includes aggregated data for replication purposes (see below). Raw data from the CEX are not included for practical purposes. The replicator should expect the CEX build to run for about 2 hours. Statement about Rights • I certify that the author(s) of the manuscript have legitimate access to and permission to use the data used in this manuscript. Summary of Availability • Some data cannot be made publicly available. Details on each Data Source Anonymous App Data This research is carried out in cooperation with a financial aggregation and bill-paying computer and smartphone application (the “app”). The raw (unaggregated) transactions data from the App cannot be disclosed or shared, so are not included in this repository. Consumer Expenditure Survey Our paper uses both the CEX Interview Survey and the CEX Diary Survey. In the directory “replication_files/Zsupplementary_data,” we provide our final builds of the CEX data from which our readers can replicate results reported in the paper. Our final build of the diary survey data is named, “diarybuild.dta” and our final build of the interview survey data is named, “CGKS_Expenditures_updated.dta.” We do not provide the raw files due to their size, however interested users can replicate our final build after downloading the raw files. We obtained the raw files from the following sources: 1980-1981 Interview Survey *.txt files from NBER http://data.nber.org/ces/1980-1981/ 1980-1981 Diary Survey (ICPSR 8235): https://doi.org/10.3886/ICPSR08235.v2 1982-1989 *.txt files from ICPSR: 1982-1983 Diary Survey (ICPSR 8599): https://doi.org/10.3886/ICPSR08599.v1 1982-1983 Interview Survey (ICPSR 8598): https://doi.org/10.3886/ICPSR08598.v1 1984 Diary Survey (ICPSR 8628): https://doi.org/10.3886/ICPSR08628.v1 1984 Interview Survey (ICPSR 8671): https://doi.org/10.3886/ICPSR08671.v2 1985 Diary Survey (ICPSR 8905): https://doi.org/10.3886/ICPSR08905.v1 1985 Interview Survey (ICPSR 8904): https://doi.org/10.3886/ICPSR08904.v2 1986 Diary Survey (ICPSR 9114): https://doi.org/10.3886/ICPSR09114.v1 1986 Interview Survey (ICPSR 9113): https://doi.org/10.3886/ICPSR09113.v2 1987 Diary Survey (ICPSR 9333): https://doi.org/10.3886/ICPSR09333.v1 1987 Interview Survey (ICPSR 9332): https://doi.org/10.3886/ICPSR09332.v2 1988 Diary Survey (ICPSR 9570): https://doi.org/10.3886/ICPSR09570.v1 1988 Interview Survey (ICPSR 9451): https://doi.org/10.3886/ICPSR09451.v2 1989 Diary Survey (ICPSR 9714): https://doi.org/10.3886/ICPSR09714.v1 1989 Interview Survey (ICPSR 9712): https://doi.org/10.3886/ICPSR09712.v1 1990-1995 *.txt files from NBER http://data.nber.org/ces/ 1996-2014 *.dta files from NBER http://data.nber.org/ces/ 2015 *.dta files from BLS https://www.bls.gov/cex/pumd_data.htm#stata For the NBER files, we store the data locally with the exact file structure at http://data.nber.org/ces/. For the ICPSR files, we download all files and preserve the original file names. We organize files in folders with the following file structure: CEX_[YEAR]/Diary and CEX_[YEAR]/Interview, for Diary and Interview data, respectively. Our build of the raw CEX Interview Survey follows Coibion, Gorodnichenko, Kueng, and Silvia (CGKS) (2012). We update the CGKS build through 2015. The CGKS build performs the following steps: sums expenditures that occur in the same month as recommended by the BLS, drops 4th and higher observations per interview, drops household with zero food expenditures

VITAL SIGNS INDICATOR
Rent Payments (EC8)

FULL MEASURE NAME
Median rent payment

LAST UPDATED
January 2023

DESCRIPTION
Rent payments refer to the cost of leasing an apartment or home and serves as a measure of housing costs for individuals who do not own a home. The data reflect the median monthly rent paid by Bay Area households across apartments and homes of various sizes and various levels of quality. This differs from advertised rents for available apartments, which usually are higher. Note that rent can be presented using nominal or real (inflation-adjusted) dollar values; data are presented inflation-adjusted to reflect changes in household purchasing power over time.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 2 (1970)
Form STF1 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B25058 (2005-2021; median contract rent)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Rent data reflects median rent payments rather than list rents (refer to measure definition above). American Community Survey 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

1970 Census data for median rent payments has been imputed from quintiles using methodology from California Department of Finance as the source data only provided the mean, rather than the median, monthly rent. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Inflation-adjusted data are presented to illustrate how rent payments have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

The Work Stoppages program provides monthly and annual data and analysis of major work stoppages involving 1,000 or more workers lasting one full shift or longer. The monthly and annual data show the establishment and union(s) involved in the work stoppage along with the location, the number of workers and the days of idleness. The monthly data list all work stoppages involving 1,000 or more workers that occurred during the full calendar month for each month of the year. The annualized data provide statistics, analysis and details of each work stoppage of 1,000 or more workers that occurred during the year. The work stoppages data are gathered from public news sources, such as newspapers and the Internet. The BLS does not distinguish between strikes and lock-outs in the data; both are included in the term "work stoppages". For more information and data visit: https://www.bls.gov/wsp/

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...

In April 2025, manufacturing sector employment in the United States decreased by 1,000 compared to the previous month. The data are seasonally adjusted. According to the BLS, the data is derived from the Current Employment Statistics (CES) program which surveys each month about 140,000 businesses and government agencies, representing approximately 440,000 individual worksites, in order to provide detailed industry data on employment.

Graph and download economic data for All Employees, Government (USGOVT) from Jan 1939 to Jul 2025 about establishment survey, government, employment, and USA.

Graph and download economic data for Job Openings: Total Nonfarm (JTSJOL) from Dec 2000 to Jun 2025 about job openings, vacancy, nonfarm, and USA.

Graph and download economic data for Unemployment Rate - 20 Yrs. & over (LNS14000024) from Jan 1948 to Jul 2025 about 20 years +, household survey, unemployment, rate, and USA.