Methodology for Generating Labor Force Data

The Labor Market Information Division (LMID) uses several methods to estimate statistics for civilian labor force, employment, unemployment, and unemployment rates. These methods discussed in this article were developed in cooperation with the U.S. Department of Labor, Bureau of Labor Statistics (BLS). Additional details and background are available from the BLS website.

Definition of Terms
CA and LA-LB-Glendale MD - Time Series Models

Definition of Terms:

Civilian Labor Force is the sum of civilian employment and civilian unemployment. Civilians, as defined, are age 16 years or older, not members of the Armed Services, and are not in institutions such as prisons, mental hospitals, or nursing homes.
Civilian Employment includes all individuals who worked at least one hour for a wage or salary, or were self-employed, or were working at least 15 unpaid hours in a family business or on a family farm, during the week including the 12th of the month. Those who were on vacation, on other kinds of leave, or involved in a labor dispute, were also counted as employed.
Civilian Unemployment includes those individuals who were not working but were able, available, and actively looking for work during the week including the 12th of the month. Individuals who were waiting to be recalled from a layoff, and individuals waiting to report to a new job within 30 days were also considered to be unemployed.
Unemployment Rate is the number of unemployed as a percentage of the labor force.

CA and LA-LB-Glendale MD - Time Series Models

In January 1996, time series models replaced the Current Population Survey (CPS) as the basis for the estimates of labor force data (labor force, employment, unemployment, and the unemployment rate) for California. In January 2005, the LMID revised data back to 1976 using the new time series models. The models cover two areas of the State: the Los Angeles-Long Beach-Glendale Metropolitan Division (MD) and the "Balance of California" (i.e., the rest of California). The results are added together to derive state-level data.

The time series models consist of two models for each area (Los Angeles-Long Beach-Glendale MD and Balance of California):

one estimates the unemployment rate and
the other estimates the civilian employment-to-population ratio

With these data and estimates of population change, employment, unemployment, and labor force are calculated. The models estimate ratios (employment-to-population and the unemployment rate) rather than the employment and unemployment levels because these ratios are easier to estimate than specific levels.

Unemployment Rate Model

The unemployment rate model uses the relationship between the monthly Unemployment Insurance (UI) claims data and the CPS unemployment rate.

Flexible trend and seasonal components are included to account for movements in the CPS rate that are not reflected in the historical UI claims series.

The seasonal component reflects, for example, movement or changes in new entrant unemployment (typically teenagers with no work experience who can be unemployed but not usually eligible to file a UI claim).
The trend component adjusts for systematic differences, such as the change in the relationship between claims and the unemployment rate during different parts of the economic cycle.

Employment-to-Population Model

The employment-to-population model uses the relationship between the ratio of the monthly Current Employment Statistics Survey (CES) employment to the population and the ratio of CPS employment to the population.

The model also includes trend and seasonal components to account for movements in the CPS not captured in the CES series. The seasonal component accounts for the seasonality in the CPS not explained by the CES (for example, agricultural employment movement), while the trend component adjusts for long-run systematic differences between the two series (for example, during expansions, the CES grows faster than the CPS).

Under the time series models for the Los Angeles-Long Beach-Glendale MD and the Balance of California, the previous month's estimates are revised. State monthly model estimates are controlled using "real-time" benchmarking to the national monthly labor force estimates from the CPS. This reduces the regular annual revisions at the end of the calendar year to the state unemployment and unemployment series.

Substate Labor Force Data - LAUS Method

The time-series models, discussed earlier, produce state-level data as well as data for the Los Angeles-Long Beach-Glendale MD. Estimates for substate areas, except Los Angeles-Long Beach-Glendale MD, are produced using indirect estimation techniques described below.

In the Local Area Unemployment Statistics (LAUS) program, the LAUS Handbook Employment and Unemployment method is used for producing sub-state employment and unemployment estimates.

Employment
Total Nonagricultural wage and salary employment from the CES (adjusted for residency using the 2010 Census)

Labor disputants
Total all other employment, including self-employed, unpaid family workers and domestics (2010 census data adjusted by monthly factors)
Total agricultural employment (agricultural wage and salary employment adjusted for multiple job holding)

Unemployment
Total Unemployment Insurance (UI), Unemployment Compensation for Federal Employees (UCFE) and Railroad Retirement Board (RRB) claims less earnings

UI exhaustees (Unemployed individual who have received all of their unemployment compensation benefits and are no longer eligible for any further benefits)
New and reentrant unemployed (new workers such as youth and people who previously worked in a full-time job but were out of the labor force prior to beginning to look for work)

Sub-County Areas - Census Share Method

The LMID derives monthly labor force data for sub-county areas by using the share of county-level employment and unemployment in the area at the time of the most current five-year American Community Survey (ACS) estimates, which are updated annually. The sub-area’s employment and unemployment estimates are then added to determine the total labor force and unemployment rate.

This method assumes that the rates of change in employment and unemployment are exactly the same in each sub-county area as at the county level (the same process is used for unemployment). If this assumption is not true for a specific sub-county area, then the estimates for that area may not be representative of the current economic conditions. Since this assumption is untested, caution should be employed when using these data.

Cautions When Using These Data

The "Employment" which is shown under "Labor Force" is not directly comparable to the "Total, All Industries" employment. A complete description of the Methodology for Generating Industry Employment Data is also available.
County labor force data are not adjusted for seasonality. When doing a comparison with state and U.S. rates, it is important to use "Not Seasonally Adjusted" labor force data for the state and the nation.
The unemployment rate usually gets the most attention, as it is a rough gauge of the area's labor market. It is best to consider the unemployment rate over a period of several months, or years. The employment and unemployment figures tend to vary from month to month for many reasons. Seasonal variation often may not reflect the economic conditions in all areas of the county. Seasonal factors may contribute to an area's high unemployment rate, but firms in some industries may have difficulty finding qualified employees. The labor market can vary greatly in different industries, in different occupations, and in different parts of the county.
The annual average figures, over time, tend to be a better gauge of the labor force trends within the area.
Month-to-month labor force data are a useful indicator to show the seasonal changes in an area including outdoor activities (such as construction), holiday hiring, school schedules, and agricultural activities.