LAUS: BLS Local Area Unemployment Statistics Explained

The Local Area Unemployment Statistics (LAUS) program is a key component of the U.S. Bureau of Labor Statistics’ effort to provide detailed labor market information at the state, metropolitan, and local levels. By collecting data from a representative sample of households and businesses, LAUS produces estimates of employment, unemployment, and labor‑force participation that help policymakers, researchers, and the public understand economic conditions in specific communities.

What the LAUS acronym stands for

LAUS stands for Local Area Unemployment Statistics. It is a nationwide survey conducted by the U.S. Bureau of Labor Statistics (BLS) that estimates unemployment and labor‑force participation for states, metropolitan statistical areas (MSAs), and local areas such as counties, cities, and other defined regions. The program was established in 1975 to provide more granular data than the national unemployment rate, allowing users to see how economic conditions vary across the country. LAUS collects data through a monthly household survey and a quarterly business survey. The household survey asks respondents whether they are employed, unemployed, or not in the labor force, while the business survey gathers information on the number of workers and vacancies. By combining these two data sources, LAUS generates estimates of the unemployment rate, employment level, and labor‑force participation rate for each local area.

How LAUS relates to the national unemployment measures

LAUS estimates are derived from the same underlying data collection methods used for the national unemployment rate, but they are aggregated at smaller geographic levels. The national unemployment rate is calculated by the BLS’s Current Population Survey (CPS), which samples households across the entire United States. LAUS takes the CPS data and applies statistical techniques to produce estimates for each state, MSA, and local area. Because the sample size for each local area is smaller than the national sample, LAUS estimates have larger margins of error, but they still provide valuable insight into regional labor market trends. The relationship between LAUS and national measures is that LAUS can be viewed as a decomposition of the national rate: the national unemployment rate is essentially a weighted average of all local area rates, weighted by each area’s labor‑force size. Consequently, changes in LAUS estimates can help explain shifts in the national unemployment rate, especially when certain regions experience significant economic events.

Which state and local geographies LAUS covers

The Local Area Unemployment Statistics (LAUS) program, administered by the U.S. Bureau of Labor Statistics (BLS), provides unemployment and labor‑force data for a wide range of geographic units. At the state level, every state and the District of Columbia receive official estimates, ensuring that state‑wide labor market conditions are consistently reported. Beyond the state, LAUS extends to metropolitan areas, defined by the Office of Management and Budget as Metropolitan Statistical Areas (MSAs) and Micropolitan Statistical Areas (μSAs). These areas capture the economic activity of core urban centers and their surrounding counties. Within each metropolitan or micropolitan area, LAUS further disaggregates data into local labor market areas (LLMAs). An LLMA is a group of counties that share a common labor market, often identified by commuting patterns and economic linkages. The program also produces estimates for individual counties, allowing for granular analysis of local employment trends. In addition, LAUS offers data for specific localities such as cities, towns, and census-designated places that meet certain population thresholds. This hierarchical structure—from state to metro to county to city—provides policymakers, researchers, and businesses with a detailed view of labor market dynamics across the United States.

How modeled estimates differ from direct surveys

LAUS employs a combination of direct survey data and statistical modeling to produce its estimates. The primary source of data is the Current Population Survey (CPS), a monthly household survey that samples approximately 60,000 households nationwide. For larger geographies like states and metropolitan areas, the CPS sample size is sufficient to calculate unemployment rates with acceptable precision, and these figures are reported as direct estimates. However, for smaller geographies—such as individual counties or cities—the CPS sample often contains too few respondents to generate reliable statistics. In these cases, LAUS turns to a model called the Local Area Unemployment Statistics (LAUS) model, which blends CPS data with ancillary information such as payroll records, administrative data, and demographic variables. The model applies a statistical algorithm that adjusts for sampling error and imputes missing values, producing a “modeled” estimate. The key difference lies in the source of uncertainty: direct estimates have a known sampling error derived from the CPS, while modeled estimates incorporate additional uncertainty from the modeling process. Consequently, the confidence intervals for modeled estimates are typically wider, and the estimates may be more sensitive to changes in the underlying assumptions of the model. Users should be aware of these distinctions when interpreting LAUS data, especially when comparing small localities to larger regions.

Seasonal adjustment and revisions in LAUS releases

The Local Area Unemployment Statistics (LAUS) program publishes monthly estimates of unemployment and labor force participation for states, metropolitan areas, and local communities. Because employment data are influenced by predictable seasonal patterns—such as increased retail hiring in the holiday season or reduced construction activity in winter—LAUS applies a seasonal adjustment to raw counts. The adjustment removes the regular, calendar‑driven fluctuations so that analysts can compare figures across months and years without seasonal bias. Seasonal adjustment is performed using the Census Bureau’s X‑12‑ARIMA methodology, which models and removes seasonal components based on historical data. The adjusted series is what appears in the official LAUS tables and is the basis for trend analysis. Revisions are another key feature of LAUS releases. Initial estimates are based on incomplete data, such as incomplete coverage of the Current Population Survey (CPS) and early returns from local administrative sources. As more complete information becomes available, the Bureau issues revised estimates. Revisions can be either upward or downward and typically occur within the first few weeks of the following month. The revision process is transparent: each release includes a revision history that shows the original estimate, the revised figure, and the difference. This allows researchers to track how estimates stabilize over time and to assess the reliability of the most recent numbers.

Finding local labor force and unemployment data

Accessing LAUS data for a specific locality begins with the BLS’s online data portal. Users can select the desired geographic level—state, metropolitan statistical area (MSA), or county—and then choose the specific series, such as the unemployment rate or the number of unemployed persons. The portal provides both the raw (unadjusted) and seasonally adjusted values, along with the labor force size and employment figures. For local communities, the BLS offers a “Local Area Unemployment Statistics” tool that allows users to search by county, city, or ZIP code. The tool displays a table of the most recent monthly estimates and a chart of the series over time. Once the data are retrieved, analysts often need to interpret the figures in context. The unemployment rate is calculated as the number of unemployed persons divided by the labor force, expressed as a percentage. The labor force itself is the sum of employed and unemployed persons who are actively seeking work. By comparing the local unemployment rate to the state and national rates, stakeholders can gauge whether a community is experiencing a localized labor market downturn or if the trend mirrors broader economic conditions. Additionally, the BLS provides documentation on the methodology, data sources, and definitions, ensuring that users understand the assumptions behind each estimate.