Use and interpret Civilian Labor Force data

Labor Force data provide key indicators for determining the health and status of an economy.

Esri's Data Development team produces demographic data (known as Updated Demographics) for the United States using a variety of sources to update small areas, beginning with the latest U.S. Census base along with a mixture of other private sources to capture demographic change. Alongside Updated Demographics, Esri provides U.S. Census Bureau and American Community Survey (ACS) demographics as a point of reference for understanding growth in an area and to provide additional community details. Data tutorials educate both the novice and the expert analyst to learn more about a topic to properly incorporate Esri Demographics that are accessible within various products. In this tutorial, you will learn about the following:

  • The five types of Civilian Labor Force data Esri makes available
  • The value of Esri Updated Demographics through the use of reports, and smart mapping techniques
  • How to interpret Civilian Labor Force measures that add insight to your analysis
  • Important data considerations
  • Additional resources

First, you'll learn what Civilian Labor Force data is and why these data measures are used to uncover characteristics of the labor market.


Labor Force data

Labor Force (also referred to as the “workforce”) data helps us to understand what segments of the population are employed or currently looking for employment --- key indicators for determining the health and status of an economy. In addition it provides pertinent information regarding employment status and employment dynamics data among age groups and specific industry and occupation categories. 

The Civilian Labor Force consists of all persons 16 years of age and older who are either employed or unemployed. It is important to mention the modifier “civilian” is included because this definition does not include persons on active duty in the Armed Forces. 

There are many layers and attributes that contribute to analyzing an area’s labor force. Let’s explore five types of labor force data Esri provides:

 

EMPLOYED AND UNEMPLOYED CIVILIAN POPULATION 16 YEARS AND OLDER


An area’s labor force is comprised of two components: the employed and unemployed populations. A person is classified as employed if they work as paid employees, work in their own business or profession, work on their own farm, or work 15 hours or more as unpaid workers on a family farm of in a family business; or those who did not work but have jobs or businesses from which they are temporarily absent due to illness, bad weather, industrial dispute, or other personal reasons.

In contrast, a person is classified as unemployed if they were neither “at work” nor “with a job but not at work”, actively looking for work and were available to start a job. Also included as unemployed are those not working but waiting to be called back to a job from which they had been laid off and were available for work except for temporary illness.

EMPLOYED AND UNEMPLOYED CIVILIAN POPULATION 16 YEARS AND OLDER BY AGE


The labor force can also be analyzed using employment and unemployment estimates by age. This breakdown provides the ability to analyze an area’s unique labor force profile across an age distribution. Esri produces estimates by four age groups: 

  • 16 to 24 years;
  • 25 to 54 years;
  • 55 to 64 years; and
  • 65 years and older

EMPLOYED AND UNEMPLOYED CIVILIAN POPULATION 16 YEARS AND OLDER BY SEX


The labor force can be further reviewed by using employment and unemployment estimates by sex. This breakdown provides the ability to analyze area differences between males and females in the labor force.

 

EMPLOYED AND UNEMPLOYED CIVILIAN POPULATION 16 YEARS AND OLDER BY RACE


Another view of the labor force is available by using employment and unemployment estimates by race. This breakdown provides the ability to analyze an area’s unique labor force profile across a race distribution. Esri produces estimates by seven race groups:

  • White
  • Black/African American
  • American Indian/Alaska Native
  • Asian
  • Pacific Islander
  • Other Race
  • Multiple Races

EMPLOYED CIVILIAN POPULATION 16 YEARS AND OLDER BY OCCUPATION AND INDUSTRY


The employed population 16 years and older is also disaggregated by industry and occupation. There are 20 industry sectors based on the North American Industry Classification System (NAICS). This classification system was an updated replacement to the Standard Industrial Classification (SIC) system that was commonly utilized prior to 1997.

Industry sectors

Employment is also available by 22 major occupation groups based on the Standard Occupational Classification (SOC) system. Furthermore, this occupational distribution can be arranged by White Collar, Blue Collar, and Services. These categories can complement your occupational employment analysis when viewed by this taxonomy. This is detailed further in the  Frequently asked questions (FAQ ). 

Occupation groups


Why use Civilian Labor Force data

Leveraging Esri’s updated characteristics of the U.S. workforce provides important insights to help data users craft more informed and effective business investment or policymaking decisions. Using Civilian Labor Force data allows data users to better report, plan, manage, and allocate resources effectively. For instance, a large unemployment rate within a specific age group or continued annual employment decline in an industry may be causes for concern. Unlike many other socio-demographic characteristics, the economic situation in an area can change quickly. So, having current year economic statistics is a critical component towards a complete area profile. 

The power behind this information lies in its relative simplicity and flexibility. There are a variety of measures that can be used to explain the economic situation in an area, and the decision of which to use depends on which are best suited to your specific use case. 


Data measures 

Many rates and ratios can be calculated with the labor force data provided by Esri. This section will focus on the most commonly used. Each one can provide a different view of an area’s labor force profile, so it is important to understand the strengths and weaknesses of each when determining which one(s) will complement your analysis.

UNEMPLOYMENT RATE

Unemployment Rate

The Unemployment Rate (UR) represents the total number of unemployed persons as a percentage of the Civilian Labor Force. As one of the most popular and widely referenced economic indicators, this rate provides a direct measure of joblessness in an area. 

Unemployment Rate Formula

Employed or unemployed population represents civilian labor force age 16 years and older.

Unemployment Rate

Since this rate measures the unemployed population relative to the labor force (as opposed to the total population) it is sensitive to changes in the size and age composition of the labor force.

Factors such as discouraged workers dropping out of the labor force are not accounted for in this narrowly defined measure.

Unemployment Rate

Other measures of labor under utilization may include less or more detail.

Learn more about the full range of unemployment rate measures defined by the Bureau of Labor Statistics (BLS)  here .

LABOR FORCE PARTICIPATION RATE

Labor Force Participation Rate

The Labor Force Participation Rate (LFPR) is used to compare the size and composition of the labor force (those employed or unemployed but looking for work) as a proportion of all working aged persons.

Labor Force Participation Rate Formula

This rate measures the size of an area’s supply of labor relative to the working age population.

Labor Force Participation Rate

Areas with this map image show where Labor Force Participation rates are above average in Los Angeles County.

Labor Force Participation Rate

Areas with this map image show where Labor Force Participation rates are below average.

Labor Force Participation Rate

One advantage of the LFPR measure is that the population base is typically more stable than the civilian labor force, resulting in relatively gradual shifts from year-to-year when analyzing a labor market of sufficient size.

The LFPR measure also allows for comparison to the broader working aged population rather than a measure that focuses just on the population within the civilian labor force, like the unemployment rate.

Labor Force Participation Rate

However, as the arithmetic expression shows, this rate does not discern between which proportion of those individuals in the labor market are employed or unemployed. While a participation rate can be high, an area’s unemployment can be high as well. As demonstrated with this map image that shows the relationship between the LFPR and the unemployment rate.

Labor Force Participation Rate

It is important to keep in mind that changes in the LFPR can be driven by economic or non-economic factors. Economic expansions or downturn can cause the rate to rise or decline, but so too can non-economic change due to significant demographic shifts (growth of women entering the workforce, retiring Baby Boomers, etc.).

EMPLOYMENT-POPULATION RATIO

Employment-Population Ratio

The Employment-Population (E-P) Ratio compares the number of persons employed in an area relative to the working age population.

Employment-Population Ratio

The primary drawback to this measure is that you are unable to discern whether those individuals who are not employed are unemployed and searching for work or simply not in the labor force.

Employment-Population Ratio Formula

This measure allows for a quick view of the employed population that is not impacted by the total size of the labor force.

Employment-Population Ratio

Measures like the UR, LFPR, and the E-P ratio are useful to include in your analysis because they allow for direct comparison of two or more labor market areas of different population sizes. With that said, the E-P ratio is also less affected by short term fluctuations in the UR.

Employment-Population Ratio

Moreover, changes in the E-P ratio can be the result of the growth or decline in the workforce and/or more or fewer persons entering into the labor market as the size of working aged population changes.

Employment-Population Ratio

Like the LFPR, this ratio is also sensitive to economic and non-economic shifts due to the rise or fall in the share of younger-aged persons deciding to stay in school or growth of the retiree population.

Hence, the overall E-P ratio reflects an area’s unique age profile, so keep that in mind when conducting your labor market analysis. Gleaning this ratio by age can help users understand how this metric can change across specific cohorts.

LOCATION QUOTIENT

Location Quotient

The use of Location Quotients (LQ) in a demographic analysis allows data users to uncover how a local economy compares to a broader geographic area of interest, such as the nation.

Location Quotient Formula

This formula is used for determining both types of LQ's (occupation or industry), where the base area represents a larger geographic area such as a state, region, or the U.S.)

Location Quotient

For example, LQs can help you discover the strength and size of an area’s industrial specialization or ascertain the balance of skilled and unskilled occupations within a local area.

Location Quotient

An LQ represents your local area’s share of total employment in an industry or occupation relative the nation’s share of total employment in that same industry or occupation.

Location Quotient

Given this relationship, an LQ of 1.0 identifies the condition where the local area and nation have the same proportional employment shares within that specific industry or occupation.

LQs greater than 1.0 will highlight higher concentrations of employment specialization while LQs below 1.0 will reveal which industries or occupations are below the national average. 

Overall, the Unemployment Rate, Labor Force Participation Rate, and Employment-Population Ratio can all be calculated by age. This can be important to understand how the labor market profiles compare among the age groups within your study area that may contain a distinct age structure.

So, which economic yardstick is the best? All of these measures tell a unique story but are best consumed as a whole with the other labor market statistics (rates by age, counts of the civilian labor force and working aged population) to gain a more complete understanding of supply and demand for labor given an area’s unique demographic composition that underlies these measures.


Data access

You can access Esri Demographics using Esri software and through apps like ArcGIS  Business Analyst ,   ArcGIS for Excel , or ready to use maps from   ArcGIS Living Atlas of the World . For use outside of the Esri platform data files are available in CSV, dBase, Excel, shapefile, or file geodatabase formats.

Contact an Esri data sales specialist with data questions at 800-447-9778 or send an email with your request to: datasales@esri.com.


How to use and interpret Civilian Labor Force data

Now that we have explained what Civilian Labor Force data offerings are and when you may want to use them, let's look at a specific use case that helps answer questions ranging from which industries or occupations are most prevalent within your trade area to understanding how labor force participation and unemployment can differ by age.

A policy decision maker is interested in the economic outlook of Los Angeles County, California. They require a full understanding of the labor force including the top occupations in the county. They are also interested in how unemployment varies across age groups so that they can best allocate resources. 

To demonstrate, a Civilian Labor Force Profile report was generated using the ArcGIS Business Analyst Web App. The Civilian Labor Force Profile is a two page report that shows key information about the area's employed and unemployed populations, including the measures discussed above and employment by occupation and industry sectors.

Let's look at some key figures on the profile to gain a better understanding of Los Angeles County's workforce.

Data shown on report is for illustrative purposes only.

What we learn

On page one of the report we learn that out of the 8.1 million people who reside in Los Angeles County, a total of 5.3 million, or 65%, comprise the Civilian Labor Force. Los Angeles County's Employment-Population Ratio of 61.5% means that there are approximately 62 employed individuals for every 100 individuals who are of working age (age 16 years and older). At 3.3 million, the population age 25 to 54 contains the highest number of workers in the county. The Employment-Population Ratio for this age group is 78.6%. In terms of total employment, the largest industry sectors in this area are Healthcare/Social Assistance (13.0%), Retail Trade (9.4%), and Manufacturing (9.2%).

On page two of the report we can gain a better understanding of the types of employment (White Collar, Blue Collar, or Services) and specific occupations that make up the LA County workforce. For example, nearly 60% of the workforce are employed in white collar professions, with 13.7% employed in Office/Administrative Support occupations. The occupation distribution in this county is similar to the U.S. in most cases, but some occupations show notable differences. For instance, the county's employment in the Arts/Design/Entertainment occupation group is 2.4 percentage points greater than the U.S. This leads to the highest occupation location quotient value of 2.30, or more than double the rate of the U.S.


This next step of the analysis helps to understand what types of occupations are dominant in the downtown Los Angeles area. Are there other types of employment opportunities that could benefit the area?

With the Civilian Labor Force Profile report above we briefly discussed employment by occupations. The report provides a quick breakdown of employment by number and percent for each occupation and industry category and provides a comparison to the U.S. statistics.

Below, a census tract-level  predominance map  image of Los Angeles County was produced using ArcGIS Online to help provide visual context to where predominant occupation types are clustered.

What we learn

From the profile report, it was determined that Office/Admin Support, Sales, and Management are the top three occupations in the county. With a detailed map analysis, you can see where top occupations are concentrated for each tract/neighborhood.

For example, in this map image of Los Angeles (and the surrounding areas), a heavy pattern of Office/Admin Support is revealed both east and west of downtown LA, while Sales occupations are prevalent to the south.

Traveling north of downtown Los Angeles (towards Hollywood and Universal City, for example) reveals a heavy pattern of occupations in the Arts/Design/Entertainment group.


This next step of the analysis helps us to understand where unemployment rates are high in comparison to those between the age of 16 to 24. For example, community leaders can research what factors may be contributing to higher rates of unemployment for this particular age group.

In this example, another map was created using two labor force attributes: Total Unemployment Rate and Unemployment Rate for Population Age 16 to 24. To accomplish this we used ArcGIS Online and applied a high to low  Relationship style mapping  technique that helps us explore how these attributes are related in the area.

Areas shaded in dark brown have a high total Unemployment Rate and a high Unemployment Rate for population age 16 to 24.

What we learn

By comparing total Unemployment Rates and Unemployment Rates for the Population Age 16 to 24 you can see how these measures are correlated with another. For example, the decision maker can focus on tracts that have low total URs but high URs among those aged 16 to 24 (the tracts colored orange). These are areas that could potentially benefit from increased employed opportunities for young adults. High estimates for both UR measures are concentrated in the downtown Los Angeles area. As you move further away from the downtown area, both UR attributes generally become lower.

Creating Relationship style maps helps to establish geographic patterns for an area between any variables of interest, making them highly adaptable to the data user's goals and interests. For example, if the user is more interested in where those of retirement age are looking for work but are unable to find jobs, the UR for those aged 16 to 24 in the above map could be replaced with the UR for those aged 65 years and older.


Data review and considerations

Civilian Labor Force estimates can differ from unemployment data published by the CPS program of the BLS for several reasons. 

Small Area Data Source: Civilian Labor Force updates use ACS data for its forecast base because it is the only source available by block group for this socioeconomic information. While ACS uses the same labor force definitions, the surveys have different methods. More detail can be found in this  Census Bureau working paper .

Data vintage: Civilian Labor Force data are current as of July 1 of the release year. Data from the BLS (for example, CPS and Local Area Unemployment Statistics (LAUS) programs) are more current monthly time series.

Seasonal Adjustment: Civilian Labor Force data are not seasonally adjusted. CPS produces two sets of national unemployment estimates that are seasonally adjusted and not seasonally adjusted. The unemployment rate often quoted in the headlines is typically a seasonally adjusted estimate.


Next steps

In this tutorial, you learned about the basics of Civilian Labor Force data, how to interpret the data, and the significant impact it has on area businesses and communities. Additional data tutorials in two series are available. Click the links below for continued data exploration, learning, and ways to access the data.


Learn more

Data methodologies

Civilian Labor Force estimates are developed using the American Community Survey (ACS) and Current Population Survey (CPS) as well as the Local Area Unemployment Statistics (LAUS), Occupational Employment Statistics (OES), and Current Employment Statistics (CES) programs. Represented as point-in-time estimates as of July 1, the data is available for Esri’s standard geographic areas and for any user-defined polygon such as a ring or drive time.  Read the Esri Updated Demographics Methodology Statement for more information 

Frequently Asked Questions

Use our  data reference page   to help answer additional questions about Esri Demographics.

Helpful links


Connect with us

If you have a topic you would like covered in a data tutorial to help you better understand U.S. data, send us an e-mail with your topic idea.

About this story

This story was created by Donna Fancher in collaboration with the Esri Data Development team. To start working with the U.S. data collection, visit the   Esri Location Data Resources   page.

Led by chief demographer Kyle Cassal and economist Douglas Skuta, Esri's Data Development team uses sophisticated quantitative methods to produce small area demographic and socioeconomic data to support informed decision-making. The team builds on a rich history of market intelligence to produce trusted independent estimates and forecasts for the United States based on innovative methodologies that use public and private data sources with the power of ArcGIS. Esri's Data Development team provides more than 7,000 proprietary data items to better understand the characteristics of people and places across multiple statistical and administrative boundaries and custom trade areas.

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Areas shaded in dark brown have a high total Unemployment Rate and a high Unemployment Rate for population age 16 to 24.