Unemployment

Data Source

Bureau of Labor Statistics

The Local Area Unemployment Statistics (LAUS) program of the Bureau of Labor Statistics produces monthly and annual employment, unemployment, and labor force data for Census regions and divisions, states, counties, metropolitan areas, and many cities by place of residence. The LAUS estimates are consistent with the national labor force and unemployment measures from the Current Population Survey. A number of different methods are used to produce these estimates, including: (1) a signal-plus-noise time-series model for states, the District of Columbia, and some substate areas; (2) a building block approach referred to as the Handbook procedure for labor market areas; and (3) disaggregation procedures for many counties and virtually all cities.

Website to download data
For more detailed methodological information

Unemployment is a percentage

Unemployment is the percentage of the county’s civilian labor force, ages 16 and older, that is unemployed but seeking work.

Unemployment is created using statistical modeling

Unemployment estimates are created using modeled data from the Current Population Survey, Current Employment Statistics, and the Unemployment Insurance system. Modeling generates more stable estimates for places with small numbers of residents or survey responses. There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make statistical assumptions about relationships that may not be held in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods.

Caution should be used when comparing these estimates across states

The method and/or source of data used by the Bureau of Labor Statistics to produce county level estimates in each state may vary.  

Measure limitations

This measure does not count individuals who want work but who have given up seeking work (discouraged workers) as officially unemployed.⁵ Furthermore, there is no unemployment measure that reliably discerns the unemployed who cannot find work at their preferred wage level from those who cannot find work at any wage.³

Numerator

The numerator is the total number of people in the civilian labor force, ages 16 and older, who are unemployed but seeking work. Unemployed persons are defined as persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment some time during the 4-week period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed.

Denominator

The denominator is the total number of people in the civilian labor force, ages 16 and older. The civilian labor force includes all persons in the civilian noninstitutional population classified as either employed or unemployed. Employed persons are all persons who, during the reference week (the week including the 12th day of the month), (a) did any work as paid employees, worked in their own business or profession or on their own farm, or worked 15 hours or more as unpaid workers in an enterprise operated by a member of their family, or (b) were not working but who had jobs from which they were temporarily absent because of vacation, illness, bad weather, childcare problems, maternity or paternity leave, labor-management dispute, job training, or other family or personal reasons, whether or not they were paid for the time off or were seeking other jobs. Each employed person is counted only once, even if he or she holds more than one job.

Modeled estimates have specific drawbacks with their usefulness in tracking progress in communities. Modeled data may not capture the effects of local conditions, such as health promotion policies. In order to better understand and validate modeled estimates, it can be helpful to supplement with additional local data. 

Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:

• Gender
• Race
• Education
• Subcounty Area

You can find data about people who are unemployed by gender, race, and educational attainment, as well as by zip code, census tract, and census block, on Community Commons. 

¹ Egerter S, Braveman P, Sadegh-Nobari T, Grossman-Kahn R, Dekker M. Education matters for health. Princeton: Robert Wood Johnson Foundation (RWJF) Commission to Build a Healthier America. 2009: Issue Brief 6.

² Bartley M, Plewis I. Accumulated labour market disadvantage and limiting long-term illness: Data from the 1971-1991 Office for National Statistics' Longitudinal Study. International Journal of Epidemiology. 2002;31:336-341.

³ Strully KW. Job Loss and Health in the U.S. Labor Market. Demography. 2009;46(2):221-246.

⁴ Crabtree S. In U.S., depression rates higher for long-term unemployed. GALLUP News: Well-Being. 2014. 

⁵ Antonisse L, Garfield R. The relationship between work and wealth: Findings from a literature review. Kaiser Family Foundation; 2018.