Glossary contains all the words, acronyms, and terms that you need to understand Lightcast data, sources, and products.
This glossary covers data terms for all countries represented in Analyst.
Active Job Postings
Active Job Postings are postings that are currently visible online and advertising a job vacancy.
Advertised Salary (Job Posting Analytics)
Advertised Salary is found within the Job Posting Analytics and Job Posting Competition reports and is the salary information provided by the company or entity advertising the position. Job postings reports can be sliced by job title, company, region, and skills, meaning users can see advertised salary information for these specific slices of data.
If present, Lightcast extracts the salary information from the job posting as advertised. Only 15-20% of all postings have salary information available. The percentage of postings containing salary information varies depending on the occupation, industry, and job title.
Lightcast displays the count of postings that include salary data (as shown below). This number will likely be much smaller than the number of postings returned in the report.
The count of postings displaying salary information is included to help the user understand how well the postings displaying salaries represent all postings in the user’s report.
A demographic breakdown, by age, of individuals working in an occupation or industry. For occupations, available by county for all 5-digit SOCs. For industries, available by county for all 6-digit NAICS.
US: For occupations, a combination of detailed Industry Demographics, staffing patterns, and American Community Survey. For industries, Lightcast’s proprietary employment data, incorporating Census’ Quarterly Workforce Indicators and American Community Survey.
CA: CANSIM tables providing annual estimates by census division, fertility and mortality rates, and projected population estimates by province.
American Community Survey (ACS)
The American Community Survey (ACS) is a nationwide survey designed to provide communities a fresh look at how they are changing. It is a critical element in the Census Bureau’s decennial census program. The ACS collects information such as age, race, income, commute time to work, home value, veteran status, and other important data. As with the 2010 decennial census, information about individuals remains confidential.
ACS is published every year instead of every ten years. Collecting data every year provides more up-to-date information throughout the decade about the U.S. population at the local community level. About 3.5 million housing unit addresses are selected annually, across every county in the nation.
Note: US only
How Lightcast Incorporates ACS
Lightcast’s Self-Employed Class of Worker includes all people who consider self-employment a significant part of their income and/or taking a significant part of their time. Lightcast largely bases job counts, hourly earnings, and projections for these unincorporated self-employed jobs on responses to the American Community Survey (with additional input from other sources).
Lightcast’s Extended Proprietors Class of Worker represents jobs that generate miscellaneous labor income, such as very small self-employment income and partnerships with many partners having limited involvement. Lightcast derives job counts and hourly earnings for Extended Proprietors from differences between ACS and other proprietor counts, the latter of which are based on tax returns and other data compiled by the Bureau of Economic Analysis as well as local personal income reports.
Lightcast also uses ACS to construct industry and occupation diversity data, which provides demographic breakouts of the workers in a given industry or occupation. We also publish many of the social and economic indicators that the ACS gathers and distributes through their various APIs.
Strengths of ACS
ACS covers a wide variety of data, providing information on national demographic, social, economic, and housing characteristics.
Responding to the Survey is mandatory for the housing units that are selected, so participation is strong.
Weaknesses of ACS
ACS is a survey, meaning it is subject to various forms of measurement error such as sampling error, misclassification (industry/occupation) error, and even incomplete or misleading responses.
Because it is designed to ensure good geographic coverage and does not target individuals, the Census Bureau selects only a small, random sample of about 295,000 addresses (of more than 180 million people) to be included in ACS each month.
The full implementation of ACS began in 2005, so historical data is limited.
For more information visit the ACS website.
Annual Business Survey (ABS)
The Annual Business Survey (ABS) is an annual survey covering the production, construction, distribution and service industries, which represent approximately two-thirds of the UK economy. Previously known as Annual Business Inquiry (ABI).
Note: UK only
Annual Openings Estimate
A combination of both new jobs and replacement jobs constitutes total openings. The annual openings figure is derived by dividing total openings by the number of years in the user’s selected timeframe. For example, an occupation showing 130 openings between 2016 and 2026 would result in an annual openings figure of 13.
For more information on how Openings is calculated, see this article.
US: Lightcast’s proprietary employment data, combined with occupation-specific percentages from the U.S. Bureau of Labor Statistics Employment Projections program.
Annual Population Survey (APS)
The Annual Population Survey (APS) is a continuous survey of households in Great Britain.
Topics covered include –
Source agency:The Office of National Statistics
Annual Survey of Hours and Earnings (ASHE)
The Annual Survey of Hours and Earnings statistics (ASHE) provides a snapshot of earning information in the UK. It provides information about the levels, distribution and make-up of earnings. It also looks at gender demographics and hours worked on a full-time and part-time basis.
Source: Office for National Statistics
Application Programming Interface
An application programming interface (API) is a method for separate computer applications to talk to each other. APIs are useful because they allow one computer application (e.g. website) to surface information from another system without needing to store all the data or handle all the complexity of the other system.
The automation index captures an occupation’s risk of being affected by automation using four measures:
% of time spent on high-risk work
% of time spent on low-risk work
Number of high-risk jobs in compatible occupations
Overall industry automation risk
The automation index is presented as a scale with a base of 100. An automation index greater than 100 indicates a higher-than-average risk of automation; an automation index less than 100 indicates a lower-than-average risk of automation.
For information on the methodology for the automation index, see this article.
Average Earnings Per Job (Industry)
Also called “average earnings per worker”, average earnings is the result of total pre-tax industry earnings divided by same-year industry employment. Earnings are defined as labor-related personal income—that is, income from work. Income from stock dividends or interest, rents, Social Security and other non-work sources are not included.
Average earnings is the sum of wages and salaries, and supplements. For further explanation, see this article.
US: BLS’s QCEW dataset (wages & salaries), BEA (supplements).
Average Monthly Postings
The average number of postings during each month within your selected timeframe.
Because active postings may appear in more than one month, the sum of "Average Monthly Postings" will exceed the total within a timeframe for this filter option.
Big Data Analytics
The use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and real-time data.
Bureau of Economic Analysis (BEA)
The BEA publishes data primarily used in Lightcast’s Input-Output modeling. BEA data is also used to help calculate earnings in some cases, and to provide employment estimates in key areas that BLS sources don’t cover.
Major sources from the BEA that appear in Lightcast data include:
State Personal Income/Local Area Personal Income (SPI/LPI)
Make & Use Tables (MUTs)
National Income & Product Accounts (NIPA)
Gross State Product/GDP by State (GSP)
Bureau of Labor Statistics (BLS)
The Bureau of Labor Statistics is the principal federal agency responsible for measuring labor market activity, working conditions, and price changes in the economy. Its mission is to collect, analyze, and disseminate essential economic information to support public and private decision making.
The BLS is the major source of employment and earnings data in the United States. Major BLS datasets used by Lightcast include
Quarterly Census of Employment and Wages (QCEW)
Occupational Employment and Wage Statistics (OEWS)
National Industry-Occupation Employment Matrix (NIOEM)
A Business Location refers to a business’s physical building. These locations are classified by NAICS code and employee size band (i.e. how many employees the business has). Unlike industry data available elsewhere in Analyst, industry data in the Industries by Business Location Size report is available down to the 6-digit NAICS level.
Source: StatCan’s Canadian Business Patterns product.
Business Register and Employment Survey (BRES)
The Business Register and Employment Survey (BRES) is the official government source of employee and employment estimates by detailed geography and industry.
Source: Office for National Statistics
Canadian Business Patterns (CBP)
Canadian Business Patterns.
Canadian Occupational Projection System (COPS)
The Canadian Occupational Projection System (COPS) provides national labour market analysis and 10-year labour market forecasts that career practitioners may find useful when assisting clients explore their career opportunities.
Canadian Socio-Economic Information Management System (CANSIM)
CANSIM is a comprehensive database from Statistics Canada containing more than 52 million numeric time series covering a wide variety of social and economic indicators.
Career Areas are the broadest occupational category in the Lightcast Occupation Taxonomy. They are designed to be useful in bringing together many occupations, which are similar in terms of the broad disciplines commonly associated with the competent performance of work tasks.
Census Agglomeration (CA)
Census Agglomeration is an area consisting of one or more neighbouring municipalities situated around a core. A census agglomeration must have a core population of at least 10,000.
Census Division (CD)
Canada: Census Division.
Census Metropolitan Area (CMA)
An area consisting of one or more neighbouring municipalities situated around a core. A census metropolitan area must have a total population of at least 100,000 of which 50,000 or more live in the core.
Census of Governments (COG)
The Census of Governments identifies the scope and nature of the nation’s state and local government sector and provides authoritative benchmark figures of public finance and public employment.
Lightcast uses the Census of Governments’ State and Local Government Finances dataset to help estimate earnings for government jobs.
Census Tracts are geographical regions defined and maintained by the U.S. Census Bureau for the purpose of collecting data. Tracts are subdivisions of counties. Tract boundaries are updated prior to each decennial Census, and are based on the number of people living in the tract–the denser the population, the smaller the geographical footprint of the tract, and vice versa. Census Tracts have a population of anywhere from 1,200 to 8,000 people, with 4,000 being the optimum number. There are about 40,000 ZIP codes in the United States, and about 73,000 Census Tracts.
To read more about Census Tracts, see the Census Bureau’s glossary entry.
Certifications are recognizable qualification standards assigned by industry or education bodies.
The net increase/decrease in regional jobs in an industry or occupation or demographic over the selected timeframe.
Change by Demographic (I-O)
In the Input-Output model, the user’s input change modeled through demographics for males, females, and eight age cohorts. This table shows the effect of the user’s input change through these demographic groups.
Change by Industry (I-O)
In the Input-Output model, the user’s input change modeled through all two-digit industry sectors. This table shows the effect of the user’s input change through all affected industries. For further detail (including job changes in all NAICS code digit-levels), see below the table.
Change by Occupation (I-O)
In the Input-Output model, the user’s input change modeled through all two-digit occupation sectors. This table shows the effect of the user’s input change through all affected occupations. For further detail (including job changes in all SOC code digit-levels), see below the table.
Change in Earnings (I-O)
In the Input-Output model, the user’s input change modeled through earnings.
Change in Jobs (I-O)
In the Input-Output model, the user’s input change modeled through jobs.
Characteristics of the Insured Unemployed (CIU)
A dataset from the Department of Labor, Characteristics of the Insured Unemployed provides state-level breakouts of unemployment into industry, occupation, or various demographic categories. Lightcast uses CIU to disaggregate county-level LAUS unemployment counts into those categories.
For more on how Lightcast uses CIU, see the article describing the methodology for Lightcast’s unemployment data.
A GIS data layer style (visualization method) that fills regions with a darker or lighter color shade based on numeric data. It is generally ideal for ratios and percentages, but generally not recommended for absolute numeric values.
The CIP-to-NOC mapping connects educational programs with target occupations, showing potential higher ed talent pipelines into occupations. Lightcast’s CIP-to-NOC mapping is based on the United States’ National Center for Education Statistics’ CIP-to-SOC mapping. Lightcast has made modifications to the mapping to make it applicable to Canadian NOC codes.
If you’d like to view the complete CIP-to-NOC map, please speak to your Lightcast Client Services Representative.
Sources: NCES: IPEDS
The CIP-to-SOC mapping connects educational programs with target occupations, showing potential higher ed talent pipelines into occupations. Lightcast’s CIP-to-SOC mapping is based on the National Center for Education Statistics’ CIP-to-SOC mapping. Lightcast has made modifications to the mapping to make it more useful.
Click here for a tutorial on editing the existing CIP-to-SOC mapping in Analyst/Developer.
If you’d like to view the complete CIP-to-SOC map, follow the steps outlined in this article.
Class of Worker (CoW)
Class of worker categorizes jobs according to the type of employment of the worker. This variable identifies whether the respondent is a salaried employee of a business, or is self-employed (Canada)/proprietor (UK).
For various reasons, Lightcast further splits each of these categories in two, resulting in four classes of worker in Lightcast Data.
QCEW Employees: The Bureau of Labor Statistics’ Quarterly Census of Employment and Wages (QCEW) dataset is the best source for job counts data in the United States. This quarterly near-census of workers is a byproduct of unemployment insurance reporting, which businesses are required to file monthly. QCEW covers 95% of the positions held by employees in the United States.
Non-QCEW Employees: The remaining 5% of employment not covered by QCEW occurs marginally in specific industries and is accounted for in other government datasets. Lightcast collects employment data for these industries and puts it in the Non-QCEW Employees Class of Worker. In other words, these jobs are held by employees of businesses, but for various reasons they are not covered by unemployment insurance and therefore aren’t counted in QCEW.
Self-Employed: This Class of Worker includes job counts for work we typically think of as constituting self-employment. The data comes from the Census’ American Community Survey, and counts respondents who list self-employment as their primary source of income.
Extended Proprietors: This Class of Worker contains miscellaneous job counts recorded by the BEA that exceed counts reported in American Community Survey data. Many of these jobs are incidental self-employment that does not constitute a primary source of income (e.g. selling handmade goods on Etsy). It’s important to note that, although the goal of this Class of Worker is to account for miscellaneous income from labor, it inherently contains miscellaneous income from capital as well (since the BEA looks at profits rather than earnings).
You can use any combination of these categories, but we recommend several groupings particularly.
To match the BLS’s QCEW dataset most closely, use the QCEW Employees class of worker by itself.
For a complete picture of the employed workforce, use the QCEW and non-QCEW classes together.
To capture the entire employed workforce, plus self-employed persons, use the QCEW Employees, non-QCEW Employees, and Self-Employed classes in conjunction. This is the default Class of Worker setting in Analyst, and generally fits most use cases.
The gig economy can be approximated using the Extended Proprietors Class of Worker; however, it is critically important to keep in mind that some income and “jobs” from capital will likely be included, due to the nature of the BEA’s data. This will be an approximation of the gig economy only, likely with jobs and earnings higher than they are in actuality because of the inclusion of some “extra” jobs and income.
Classification of Instructional Programs (CIP)
A standard numerical code for a post-secondary course of study, developed and defined by the U.S. Department of Education’s National Center for Education Statistics. The classification of instructional programs provides a taxonomic scheme that supports the accurate tracking and reporting of fields of study and program completions activity.
A specific age group (which may also include gender or race/ethnicity) in demographic data, e.g., “male African Americans born between 1980 and 1984.” Over time, this cohort will move through various standard Census* age categories such as “25 to 29 year olds” and “30 to 34 year olds.”
*Census categories in the United States; CANSIM categories in Canada.
Commercial Real Estate (CRE)
Commercial Real Estate.
Commodity Flow Survey (CFS)
The Commodity Flow Survey (CFS), a component of the Economic Census, is conducted every five years by the U.S. Census Bureau in partnership with the U.S. Department of Transportation’s Bureau of Transportation Statistics. The CFS is a shipper survey of approximately 100,000 establishments from the industries of mining, manufacturing, wholesale trade, auxiliaries (i.e. warehouses and distribution centers), and select retail and service trade industries that ship commodities.
Lightcast uses CFS in the Input-Output model to help determine the potential for movement of goods and services between regions.
Common Aggregation Hierarchy (CAH)
The Common Aggregation Hierarchy (CAH) provides a standardised hierarchical aggregation (grouping) of subject codes and terms suitable for the majority of uses.
CAH codes are from Higher Education Statistics Agency (HESA)
Common Core of Data (CCD)
The Common Core of Data is the Department of Education’s primary database on public elementary and secondary education in the United States. CCD is a comprehensive, annual, national database of all public elementary and secondary schools and school districts.
Lightcast uses CCD to help generate employment estimates for public education industries.
Community Indicators data comes from the American Community Survey (ACS), which is published by the Census Bureau. The Community Indicators dataset allows users to examine a number of regional characteristics including social characteristics such as average family size, economic characteristics such as population living at or below poverty level, and housing characteristics such as the number of occupied housing units.
For more information on the community indicators dataset, see this article.
This number is intended to score the compatibility of two occupations in terms of the knowledge, skills, and abilities they require: a score of 100 means complete compatibility, while a score of 0 means no compatibility. The compatibility index is a synthetic number generated by a proprietary algorithm that uses O*NET’s data on the required Levels and Importance of competencies.
The Lightcast compensation model provides occupational wage data with skill and certification premiums. It combines percentile wage data provided from Lightcast’s LMI-based government data with observations culled from job postings.
The compensation model combines wage data from two distinct sources. The backbone for all Lightcast occupational wage data is the Bureau of Labor Statistics’ Occupational Employment and Wage Statistics (OEWS) dataset. This set is updated annually, and provides percentile earnings data for occupations at the metro level throughout the United States. In cases where percentile earnings data are suppressed, Lightcast first unsuppresses the data.
Job postings are used to supplement OES data by providing wage observations that can be tied to skills and certifications (such granularity does not exist in the OES dataset). Job postings are scraped from online sources. For more about the process see this documentation on job postings. For more information about what is included in compensation, check out Compensation Model Documentation or see this article.
A specific area of knowledge, skills, or abilities in the O*NET framework. For example, a competency may be academic (mathematical knowledge), practical (mathematical problem-solving skills), or physical/cognitive (number facility, mathematical reasoning).
Competitive effect indicates how much of the job change within a given region is the result of some unique competitive advantage of the region. This is because the competitive effect, by definition, measures the job change that occurs within a regional industry that cannot be explained by broader trends (i.e. the National Growth Effect and the Industrial Mix Effect).
To measure competitive effect, we subtract Expected Change from the actual regional job change in the industry of interest.
Actual Change – Expected Change = Competitive Effect
It’s important to note that this effect can be positive even if regional employment is declining. This would indicate that regional employment is declining less than national employment.
A student who receives a degree, diploma, certificate, or other formal award. In order to be considered a completer, the degree/award must actually be conferred. Lightcast sources completer data from IPEDS.
See also: IPEDS.
The number of degrees or certificates conferred for a specific course of study in a given year. Includes all award levels. May be greater than the actual number of students who graduated, as Lightcast includes both primary and secondary majors. Both primary and secondary majors are included because a graduate with a dual major in mathematics and electrical engineering should be considered part of the potential supply for occupations that map to both majors.
The reference period for a completion year is July 1 of the prior year through June 30 of the current year. For example, the 2017 Completions metric is a count of completions from 7/1/2016-6/30/2017.
Number of people who have completed a course in the selected course area. Completions data in the UK is provided by your organisation and would be subject to change based on the data you have selected.
There are four types of concentration indices based on employment, demographic, profiles, and postings. These are all Location Quotient calculations, each using different data sources, namely the government data, online profiles, and job postings.
Consumer Expenditure Survey (CEX)
The Consumer Expenditure Survey program provides data on expenditures, income, and demographic characteristics of consumers in the United States. The BLS Consumer Expenditure Survey (CEX) provides data on expenditures, income, and demographic characteristics of consumers in the United States. Lightcast utilizes this data heavily in the Input-Output model to model consumption on industries by national demographic by income type.
Lightcast uses CEX’s annually updated tables, which are generally released in September.
Cost of Living (CoL)
The cost of living is an indication of the amount of money needed to live in a given region, including the price of food, taxes, housing, etc., and is linked to the wage level in that region.
Cost of Living Index (COLI)
The Cost of Living Index (COLI) comes annually from C2ER and provides a baseline for understanding how regional costs of living compare to the nation and to each other. The index is comprised of six major categories: grocery items, housing, utilities, transportation, health care, and miscellaneous goods and services. For example, an index below 100 means the region has a lower cost of living, whereas above 100 means it is more expensive to live.
Lightcast’s industry or occupation earnings, adjusted by the C2ER Cost of Living Index.
The Cost of Living index is 100-based, with an index above 100 indicating that the cost of living is higher than average in the region of study. Likewise, an index below 100 indicates that the cost of living is lower than average in the region of study.
To create COL-adjusted earnings, we divide earnings by the index, then multiply the result by 100.
Total Earnings for Manufacturing in Michigan: $78,616
Cost of Living in Michigan: 98.5
Cost of Living Adjusted Earnings: $79,813.20*
For more information about how Cost of Living is calculated, click here.
Council for Community and Economic Research (C2ER)
The Council for Community and Economic Research (C2ER) is a membership organization that promotes excellence in community and economic research by working to improve data availability, enhance data quality, and foster learning about regional economic analytic methods.
C2ER provides Lightcast with Cost of Living data as well as information on industry and occupation diversity (diversity of a region’s industry or occupation mix).
County Business Patterns (CBP)
County Business Patterns (CBP) is an annual series that provides subnational economic data by industry. This series includes the number of establishments, employment during the week of March 12, first quarter payroll, and annual payroll. Lightcast uses CBP to help unsuppress QCEW data and to help inform our employment data for employment not covered by QCEW.
County Not Reported
Lightcast geographies include a “county” within each state entitled “[(State), county not reported]”. These regions are used in the Quarterly Census of Employment and Wages dataset from the Bureau of Labor Statistics, our primary source for job counts.
Jobs that appear in these “counties” represent headcount for employers who have multiple establishments within a state, but report all those establishments in aggregate, with no geographical distinctions. In these cases the BLS is unable to assign jobs to particular counties, and so reports them all under the county not reported category. To be able to display and include these jobs in state-level estimates, Lightcast also includes them using the “county not reported” geography for each state.
Each state also has a “ZIP not reported” and a non-reported tract, so that a “not reported” bucket exists for each geography for which it might be needed.
This arrangement affects about 2% of jobs in the United States.
Lightcast’s crime data comes from the FBI’s Uniform Crime Reporting Program and includes crime counts broken into two categories: violent and property crime.
Violent crime: murder and nonnegligent manslaughter, rape, robbery, and aggravated assault.
Property crime: offenses of burglary, larceny-theft, and motor vehicle theft.
Local law enforcement agencies collect detailed incident level data regarding individual offenses and arrests and submit them to the National Incident-Based Reporting System using prescribed data elements and data values.
This data is often updated during the Q4 data run for the previous calendar year. Crime data is available at the county level for the United States. It is important to note that the data is collected by the location of the reporting agency, not by the location of the crime. Therefore, in cases where the reporting agency is not located in the county in which the crime was committed, the crime will be counted in the county of the reporting agency’s location.
Current Employment Statistics (CES)
Each month the Current Employment Statistics (CES) program surveys approximately 142,000 businesses and government agencies, representing approximately 689,000 individual worksites, in order to provide detailed industry data on employment, hours, and earnings for workers on nonfarm payrolls. Lightcast uses CES to inform employment estimates for some of the industries not covered by QCEW.
Current Population Survey (CPS)
The BLS’s Current Population Survey (CPS) is a monthly survey of households, and collects data on a number of topics including employment, labor force participation, and earnings. CPS data is the basis for the demographic breakout of data in the Input-Output model.
"Current Year" is defined by the data currently available from government data sources.
“Current Year” is defined by the data currently available from QCEW and OES, Lightcast’s major sources for industry and occupation data, respectively. QCEW defines Lightcast’s current year for industry and occupation job counts and industry earnings, and OES defines Lightcast’s current year for occupational earnings. QCEW generally lags the current calendar year by nine months, releasing the first quarter of the calendar year’s data in early September. Therefore, Lightcast’s current year for job counts and industry earnings catches up to the calendar year in the last quarter of the calendar year. OEWS releases in the spring of the year following; for instance, 2022 OEWS was released in April 2023. Therefore, the current year for occupational earnings data is two years behind the calendar year in the first half of the calendar year, and one year behind the calendar year in the last half of the calendar year.
Demand is an estimate of the amount of goods and services required by a region. The value is calculated using industry purchases across the nation, measured in terms of sales. Industry wages, taxes, and other values added payments are indirectly part of the demand through the production of the supplying industry. It is not possible to know the proportions into which demand should be broken out into categories such as wages, taxes, etc., but it is assumed that demand includes those categories.
Department of Labor (DoL)
The mission of the Department of Labor is to foster, promote, and develop the welfare of the wage earners, job seekers, and retirees of the United States; improve working conditions; advance opportunities for profitable employment; and assure work-related benefits and rights.
Direct Effect (I-O)
In an impact scenario in the Input-Output model, the effect of the user’s input change. This is the first round of changes. Using the analogy of tossing a rock into a pond as the initial, user-input change, the direct effect is the first ripple. The industry impacted by the user in the scenario will in turn impact other industries, demanding more goods or services from the industries in its supply chain. This is the direct effect.
CA: Lightcast's model, incorporating data from Statistics Canada (StatCan)
US: Lightcast’s model, incorporating data from the Bureau of Economic Analysis (BEA)
Distance Offered Programs
Distance-offered programs (often referred to as online courses) are college courses which are available for students to attend online from a remote location, while sometimes available on campus as well. These programs allow for higher enrollment rates and therefore higher graduation rates for participating schools. See this article for more information.
Industry earnings are the total industry wages, salaries, supplements, and proprietor income in the region, divided by the number of jobs in the region.
Earnings Multiplier (I-O)
The total earnings created in a region as a result of a single dollar of new earnings. This number includes the yield and the initial dollar addition. In other words, an earnings multiplier of 1.5 is made up of the initial dollar added (1.0) and the further yield (0.50).
An earnings multiplier of 1.5 means that for every dollar of earnings generated by a new scenario, a total of $1.50 is paid out in wages, salaries, and other compensation throughout your economy. This is important for understanding how a given scenario will affect not the number of jobs in your region, but the quality of those jobs. A scenario whose ripple effect brought two dozen lawyers and accountants into your region would have a much higher earnings multiplier than if that scenario brought the same number of indirect jobs into the region, but mostly in Food Services and Hotels.
US: Lightcast’s model, incorporating data from the Bureau of Economic Analysis (BEA).
Earnings Per Job (EPJ)
Earnings Per Job (i.e. total earnings divided by total number of jobs).
Economic base refers to the industries that contribute a large percentage of jobs and earnings to a local region. Aside from producing in-region income, they often bring in outside revenue as well, which helps to grow the region’s economy.
Economic Development Organization (EDO)
Economic Development Organizations are organizations, whether public or private, dedicated to developing the economy of a region or nation.
Educational Attainment (Demographics)
Educational Attainment indicates the level of education achieved by segments of the population of the United States. The data are broken out by gender and by race/ethnicity for the population ages 25 and over.
Source: Census Bureau
Educational Attainment (O*NET)
O*NET Educational Attainment is a breakdown of the education levels generally required for employment in an occupation. These levels may differ from the actual education levels attained by the occupation’s workforce which are presented in the SOC Educational Attainment section of Analyst.
Source: O*NET Database
Educational Attainment (SOC)
SOC Educational Attainment is a breakdown of the education levels attained by the occupation’s workforce. The Educational Attainment breakout is only provided for the nation as a whole.
Source: The Bureau of Labor Statistics’ (BLS) Educational Attainment for workers 25 years and older by detailed occupation
In Lightcast data, employed refers to any person who is currently paid as an employee or is self-employed. It is important to note that Lightcast employment counts count jobs, not people.
Employment Projections (EP)
The Bureau of Labor Statistics’ Employment Projections program produces many datasets covering various facets of the labor force. the EP tables are updated annually. Lightcast uses several datasets from EP:
Educational attainment for workers 25 years and older by occupation shows, by occupation, the percent educational breakout of the workforce for the occupation. Lightcast uses this data as-is in its Educational Attainment breakouts for individual occupations:
The Education and training by occupation table shows the typical education level needed to enter an occupation, as well as the work experience and typical level of on-the-job-training required.
The National Industry-Occupation Employment Matrix (NIOEM) is used to adjust Lightcast’s industry and staffing projections, and therefore informs Lightcast’s occupation projections as well. For more information on how Lightcast creates occupation data using industry employment and staffing patterns, see this article.
Establishments (Payrolled Business Location)
Also referred to as a “Payrolled Business Location”, an establishment is a single physical location of some type of economic activity (a business), used for reporting purposes in government data sources. A single company may have multiple establishments.
As an example, a single company with its corporate office in New York, a paper manufacturing plant in Georgia, and fifteen warehouses in various cities would comprise a total of seventeen establishments, and each establishment would be classified according to its own type of activity. In this case, three different industries would be used:
Corporate, subsidiary, and regional managing offices
Paper (except newsprint) mills
General warehousing and storage
See Race and Ethnicity.
In shift share analysis, expected change is the amount of job growth or decline that we would expect to see for a particular regional industry based on the national growth effect and the industry (or occupation) mix effect. Job change beyond this level is “unexpected” and can therefore be attributed to the region’s unique competitive effect (see definition below).
To measure expected change, we simply add the two effects we previously calculated:
Industrial Mix Effect + National Growth Effect = Expected Change
For the purposes of this definition, “region” refers to the area defined by the user and passed into Analyst. Exports show the amount of money that is spent by industries located outside the region in exchange for goods or services produced by an industry located in the region.
Exports can be either foreign or domestic. An example of foreign exports would be a business in Toronto purchasing consulting services from a consulting firm in New York in exchange for dollars. An example of domestic exports would be a firm in Maryland selling a software product to a firm in Alabama—the Maryland firm has exported its product to Alabama in exchange for dollars. Both the consulting and software examples are considered exports, because a good or service is leaving the region, and dollars are entering the region in exchange.
The exports figure does not directly include wages of employees in the industry from which goods or services were purchased. Money entering the region in exchange for goods and services exported out of the region will likely be indirectly used to pay employees (regardless of where the employee lives), but the exports figure is agnostic of what the industry producing the good or service will do with the money.
Extended Proprietors (Class of Worker 4)
This class covers the same job types as the “Self-Employed” class of worker, but Extended Proprietor jobs represent miscellaneous labor income for persons who do not consider it a primary job. Extended Proprietor jobs include minor or underreported self-employment, investments trusts and partnerships, certain farms, and tax-exempt nonprofit cooperatives. This class is normally only used for Input-Output purposes, since investments and partnerships in particular will be overrepresented in certain sectors.
See also Class of Worker (CoW)
A statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.
FBI Uniform Crime Reporting (UCR)
Lightcast’s crime data comes from the FBI’s Uniform Crime Reporting (UCR) dataset. UCR data is updated annually, provides statistics for violent and property crimes, and is available at the county level.
Federal Information Processing Standards (FIPS)
FIPS are standards and guidelines for federal computer systems that are developed by the National Institute of Standards and Technology (NIST) in accordance with the Federal Information Security Management Act (FISMA) and approved by the Secretary of Commerce. These standards and guidelines are developed when there are no acceptable industry standards or solutions for a particular government requirement. Although FIPS are developed for use by the federal government, many in the private sector voluntarily use these standards.
Counties are uniquely identified using unique FIPS codes. For example, Latah County Idaho’s FIPS code is 16057.
In Lightcast products, tables have “filter” capabilities. A filter is a set of one or more criteria, used to display only specific rows of data in a table. For example, a criterion might be “Total 2007 Jobs greater than or equal to 350”, where the column is “Total 2007 Jobs”, the comparison method is “greater than or equal to”, and the value is “350”. When applied as a filter, this criterion will show a table with only those rows whose “Total 2007 Jobs” field is greater than or equal to 350. Various criteria in a filter can be combined with AND and OR operators.
Freshmen Home Residency
Freshmen Home Residency shows the home state of first-time, first-year students. Transfer and returning students are not included in the data.
Freshmen home residency data are reported to IPEDS by individual institutions. To ease the burden on individuals responsible for completing IPEDS reporting on behalf of their institutions, reporting of freshmen home residency data is only required every other year.
Source: Integrated Postsecondary Education System (IPEDS)
Further Education (FE)
In UK educational data, “Further Education” refers to post-secondary job-training and vocational educational programs, such as apprenticeships. This is separate from higher education, which in the UK refers specifically to degree-seeking programs.
Industry: A demographic breakdown, by gender, of individuals working in the selected industry. Available by county for all 6-digit NAICS.
Occupation: A demographic breakdown, by gender, of individuals working in the selected occupation. Available by county for all 5-digit SOCs.
Source: (Industry) Lightcast’s proprietary employment data, incorporating Census’ Quarterly Workforce Indicators and American Community Survey; (Occupation) A combination of detailed Industry Demographics, staffing patterns, and American Community Survey.
Government Office Region
Government office regions are 9 broad level English regions based on economic levels. Wales and Scotland do not have government office regions, and thus are considered their own regions at this level.
Gov. Office region is one of the region choices in Analyst UK
Lightcast uses the terms “completion” and “graduate” interchangeably, and it is important to understand what is meant. Both terms refer to the number of degrees awarded rather than the number of students who graduated. Although students may graduate with multiple awards (e.g. “double majors”), our source data do not link awards to students.
See also: Completions.
Gross Domestic Income (GDI)
Gross domestic income is a measure of U.S. economic activity based on incomes. In theory, GDI should equal gross domestic product (GDP), but the different source data yield different results. BEA considers GDP more reliable because it’s based on timelier, more expansive data.
Gross Domestic Product (GDP)
The BEA produces Gross Domestic Product estimates. GDP is the value of the goods and services produced in an economy. Lightcast uses BEA’s GDP by State (GSP) data for benchmarking parts of the Input-Output model. GDP is updated annually.
Gross Domestic Product by State (GSP)
The BEA produces Gross Domestic Product estimates. GDP is the value of the goods and services produced in an economy. Lightcast uses BEA’s GDP by State (GSP) data for benchmarking parts of the Input-Output model. GSP is updated annually.
Gross Regional Product (GRP)
Gross Regional Product (GRP) is simply GDP for the region of study. More commonly, GRP is GDP for any region smaller than the United States, such as a state or metro. GRP measures the final market value of all goods and services produced in the region of study.
The national growth effect shows the number of jobs an industry is expected to gain or lose according to the industry’s national job growth. So if the industry sees national net job growth, you can expect to see job growth in most regions within the country as well.
This is sometimes explained as “the rising tide that lifts all boats.” Imagine several boats floating near the shore. If the tide begins to rise, each boat will rise with the tide–just as each boat will lower when the water lowers. This rising and falling is the national growth effect. It’s important to remember, however, that sometimes one of these “boats” (which are industries, in this case) may be pulled down deeper in the water, or may be experiencing higher tides on its own. These phenomena can be explained by competitive effect. To measure the national growth effect, we simply multiply the growth rate of the overall economy to the number of jobs in your region that are part of the industry.
National Growth Rate x Number of Regional Industry Jobs= National Growth Effect
Higher Education (HE)
In the U.S., Higher Education refers to colleges and universities. In the U.K., however, not all post-secondary education is considered a part of higher education; vocational and job-training programs are generally not considered higher education, instead being included in “Further Education”. This includes many community colleges and certificate programs that would be considered higher education in the US.
Lightcast clients may use our Higher Ed data to understand how well their programs prepare students for the workforce and what other institutions might be offering in terms of competition.
The number of hires for the selected timeframe. When compared with Unique Postings, Hires shows how much actual hiring activity there is relative to the amount of posting activity.
A hire is reported by the Quarterly Workforce Indicators when an individual’s Social Security Number appears on a company’s payroll and was not there the quarter before. The QWI program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws, similar to the QCEW program. For more information from the Census Bureau on how hires data is collected, see this publication.
For more information on how Lightcast calculates hires for occupations, see the methodology article.
Source: Quarterly Workforce Indicators (QWI) from the Census Bureau and Lightcast’s proprietary employment data
For industries, historical average earnings per job. Historical industry earnings go back to 2001.
For occupations, average and percentile wages. Historical wages are provided as far back as 2005, which enables users to track trends in wage growth or decline over time.
Historical wages are nominal, meaning that they are not adjusted for inflation.
Housing and Urban Development (HUD)
The Department of Housing and Urban Development administers programs that provide housing and community development assistance. The Department also works to ensure fair and equal housing opportunity for all.
On a scale of 1-100, how important the required level of knowledge, skill, or ability is to an occupation. Typically Importance and Level are similar, but there are cases such as a Registered Nurse’s knowledge of medicine and dentistry. They require “65 out of 100” in terms of their Level of knowledge, but the Importance of having that knowledge is scored “91 out of 100.” They don’t need to know everything about the topic, but what they do need to know is very important to their job.
By comparing Importance and Level across all occupations, we can view similar occupations based on overlap between the two.
Source: O*NET Database.
For the purposes of this definition, “region” refers to the area defined by the user and passed into Analyst.
Imports show the amount of money that is spent by all industries located in the region in exchange for goods or services produced by an industry located outside the region. Money leaves the region, and a good or service is brought into the region and consumed.
Imports can be foreign or domestic. An example of foreign imports would be a firm in New York paying money for consulting services from a firm in Toronto. An example of domestic imports would be the same firm in New York purchasing consulting services from a firm in Alabama.
The imports figure does not directly include wages of employees in the industry from which goods or services were purchased. Money used to purchase imported goods and services will likely be indirectly used to pay employees of the industry from which the good or service was purchased (regardless of where the employee lives), but the imports figure is agnostic of what the industry producing the good or service will do with the money.
Income refers to the amount of money an employee receives for their work. Income may be salaried or non-salaried, and therefore does not imply consistency in the amount received during each pay period.
Indirect Effect (I-O)
The subsequent ripple effect in further supply chains resulting from the direct change. In more awkward terms, this shows the sales change in the supply chains of the supply chain, as a result of the direct change. This is the second round of impacts. This change is due to inter-industry effects.
Source: Lightcast’s model:
Induced Effect (I-O)
This change is due to the impact of the new earnings created by the Initial, Direct, and Indirect changes. These earnings enter the economy as employees spend their paychecks in the region on food, clothing, and other goods and services. In other words, this figure represents the income effects on inter-industry trade.
Source: Lightcast’s model:
A group of businesses that produce similar goods and services, and share similar production processes for creating the goods and services they sell.
In the US and Canada, industries are classified using NAICS codes. Note that in the NAICS system, what a business produces is given less importance than the process used to create it. See NAICS.
In the UK, Industries are classified using SIC Codes.
Industry Diversity Clusters
Lightcast’s diversity cluster definitions and ranking methodology come from C2ER (The Council for Community and Economic Research). These measures quantify how jobs are distributed across industry clusters in a select region compared to a typical one.
A region’s economic function or functions represent the collection of broad economic activities in which the region’s workforce and firms engage. Practically, functions can be identified by grouping industries together into categories that are broadly similar on factors such as inputs, outputs, and/or the technological or skill requirements necessary to perform the work customary to these industries. Grouping industries according to function, rather than simply accepting the NAICS industry categories, can help to
Broadly characterize the economic roles a county plays in its region
Provide insight into the economic relationships and similarities counties have with other regions
Identify factors that make regions comparatively better fits for certain economic activities
Speak to the broader economic and demographic forces that are likely to impact a county’s economic prospects
The entropy measure of diversity is used to calculate industry function and occupation knowledge-based measures of economic diversity across U.S. counties and a variety of other geographies. These metrics were calculated according to the following formula:
where there are i = 1 to k industries and pi is the share of economic activity (e.g. employment or earnings) in the in the industry. The products of industry shares of economic activity and the natural log of the inverse industry shares of economic activity are summed to arrive at the final entropy index measurement. The index has a minimum value of 0 when all economic activity is within one industry, and the value increases as the number of industries increases and the distribution of economic activity across these industries becomes more equal.
Separate diversity rankings have been created based on geography type: county, microMSA, MetroMSA, and state.
Creating Industry Function Classifications
In an examination of the rise of services as a proportion of employment, Noyelle (1983) advanced a functional classification system for services based on the type of outputs (intermediate or final outputs) and the institutional setting under which services are provided (private, public, or nonprofit sectors). Lawrence (1984) classified manufacturing industries on the basis of the primary end use of the product (e.g. intermediate goods; consumer durables; producer durables; consumer nondurables) and the necessary inputs to the industry (e.g. research and development expenditures; scientists and engineers; capital-, labor-, and resource-intensive).
This analysis draws primarily from the work of Lawrence (1984) and Noyelle (1983) to categorize industries according to functional types. In an effort to focus on the economic base of counties, non-function industries or industries that often serve local populations, such as retail, trade, personal services, doctor’s offices, local government, and construction, were excluded from the analysis of functions.
 Malizia, E. E., & Ke, S. (1993). The influence of economic diversity on unemployment and stability. Journal of Regional Science, 33(2), 221-235.
 Noyelle, T. J. (1983). The implications of industry restructuring for spatial organization in the United States. In Regional analysis and the new international division of labor (pp. 113-133). Springer, Dordrecht.
 Lawrence, R. (1984). Sectoral Shifts and the Size of the Middle Class. The Brookings Review, 3(1), 3-11.
Lightcast earnings data is presented by place of work. Lightcast displays industry earnings as two separate values: “Wages and Salaries” and “Supplements” (or the total, “Earnings”).
Wages and salaries are equivalent to QCEW reported earnings. The BLS defines wages and salaries as including “bonuses, stock options, severance pay, the cash value of meals and lodging, tips and other gratuities. In some states, wages also include employer contributions to certain deferred compensation plans, such as 401(k) plans. Covered employers’ contributions to old-age, survivors, and disability insurance; health insurance; unemployment insurance (UI); workers’ compensation; and private pension and welfare funds are not reported as wages. Employee contributions for the same purposes, however, as well as money withheld for income taxes, union dues, and so forth, are reported, even though they are deducted from the worker’s gross pay.”
Supplements come from the BEA’s State and Local Personal Income datasets. According to the BEA, supplements consists of “employer contributions for employee pension and insurance funds and employer contributions for government social insurance.”
Contributions for employee pension and insurance funds include employer contributions to private pensions, publicly administered government employee retirement plans, private group health and life insurance plans, etc.
Contributions for government social insurance include “employer contributions for government social insurance as well as payments by employees, the self-employed, and other individuals who participate in government social insurance programs.”
Source: BLS wage definition taken from BLS Handbook of Methods, QCEW chapter, pp. 3-4.
BEA supplements definitions drawn from BEA Regional Definitions tool.
Lightcast projects employment data 10 years into the future. Industry projections are built from Lightcast’s final industry data, which is based on the BLS’s Quarterly Census of Employment and Wages (QCEW) dataset.
See this article for a more thorough treatment of Lightcast’s industry projections methodology.
Industry Requirements (Supply Chain)
Derived from Lightcast’s Input-Output model, this figure describes the purchases a given industry makes from all other industries—an industry’s supply chain—and also estimates whether those purchases came from within or without the region of study. Also known as Gap Analysis, this report is an important part of import substitution strategies employed by economic development organizations.
See also: Supply Chain Analysis
Source: Lightcast’s model:
Initial Effect (I-O)
This number represents the initial change in earnings or jobs as input by the user, and therefore does not include ripple effects.
Input-Output Model (I-O)
An Input-Output model represents the flow of money in an economy, primarily through the connection between industries; it shows the extent to which different industries are buying from and selling to one another in a particular geographic region. An I-O model also accounts for things like government spending, household spending, investments, imports, and exports, all of which help us gain a full picture of what is happening in an economy.
I-O models have three important uses:
Change – An I-O model can be used to demonstrate the effect job loss or job creation will have on a regional economy–to what extent it will affect other jobs in the area, additional earnings and sales. See how to do this HERE.
Supply Chain – An I-O model has the potential to expose the supply chain of goods via industries in a region. In particular, to what extent each industry is able to satisfy its purchasing needs in-region or out-region. This can be very helpful to economic development organizations who are looking to strengthen a local supply chain and increase in-region purchasing. See how to view an industry’s supply chain HERE.
Industry Importance – An I-O model can be used to identify important industries in your region–not just those with a lot of jobs (like retail or healthcare) but also those which have an unusually large and positive economic impact, like advanced manufacturing, technology, etc.
Integrated Post-secondary Education Data System (IPEDS)
IPEDS is a system of interrelated surveys conducted annually by the U.S. Department’s National Center for Education Statistics (NCES). IPEDS gathers information from every college, university, and technical and vocational institution that participates in the federal student financial aid programs. The Higher Education Act of 1965, as amended, requires that institutions that participate in federal student aid programs report data on enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid. Many institutions that do not receive federal funding also participate and report their data to IPEDS voluntarily.
Lightcast uses IPEDS data to provide information about postsecondary institutions, especially in regard to college completions by program type and demographic (race and gender). Completions include degrees (associate’s, bachelor’s, master’s, doctoral), certificates, and any other formal award.
In addition, Lightcast uses the CIP system to create program-to-occupation crosswalks, which map programs of study to occupations and reveal one measure of education supply and demand.
Strengths of IPEDS
Because the Higher Education Act of 1965, as amended, requires that institutions that participate in federal student aid programs report data to IPEDS, the data is a very comprehensive source.
IPEDS data has several uses, including providing the market research information necessary for universities and colleges to evaluate new and existing programs.
IPEDS allows you to view information for an individual institution, compare institutions side-by-side, or view trends for certain variables.
Weaknesses of IPEDS
Institutions self-report their information to IPEDS, so the possibility of error exists, particularly in regard to the reported programs of study.
There is about a year lag between when IPEDS collects its data and when it is released.
IPEDS is not comprehensive of all education and training programs (e.g. MakerSquare and other non-traditional education and training programs are not included in this data).
Programs that are offered online are flagged as such, but their enrollments and completions cannot be split out to show number of brick-and-mortar completions vs. number of online completions.
The CIP taxonomy is not intuitively organized; its classifications do not necessarily match up with the exact names of majors, and similar programs may not be found within the same six-, four-, or even two-digit series.
Inverse Staffing Patterns
A table of percentages that shows, on average, how regional occupations are divided up among regional industries. For example, a (simplified) inverse staffing pattern for registered nurses may show that 70% of RNs are employed by hospitals, 10% by local government (i.e., public schools), 10% by nursing homes, and 10% by offices of physicians. Inverse staffing patterns identify the industries currently employing this occupation, including those which are likely to be hiring due to growth or displacing workers due to contraction. See also Staffing Pattern.
US: Primarily the national OES staffing pattern, combined with projections from the National Industry- Occupation Employment Matrix and Lightcast’s proprietary employment data.
CA: Primarily industry by occupation percentages from the Census at the provincial level.
IRS Migration Data
IRS migration data is the foundation for the migration figures shown in Lightcast’s products. The data is based on tax returns filed and generally lags by a couple of years. Note, however, that this IRS data is not used in estimating migration for our demographics data. Our demographics data uses estimated migration based on Census population estimates and CDC birth/death rates.
The IRS’s migration data can be found here.
A job is any position in which a worker provides labor in exchange for monetary compensation. This includes those who work as employees for businesses (a.k.a. “wage and salary” employees) and proprietors who work for themselves.
Lightcast reports employment as annual averages. The exception is the Extended Proprietors Class of Worker (Class 4), which counts proprietors that existed at any time during a given year, because those data are based on tax returns. Employment averages represent jobs, not workers, since one individual may hold multiple jobs.
Due to limitations of source data, both full- and part-time jobs are included and counted equally, i.e. job counts are not adjusted to full-time equivalents. Geographically, payroll jobs are always reported by the place of work rather than the worker’s place of residence. Conversely, self-employed and extended proprietors are always reported by their place of residence. Unpaid family workers and volunteers are excluded from all Lightcast data.
Job counts (e.g. 2018 Jobs) are based on the most recent four quarters of data available from QCEW. For example, in May 2019, 2018Q3 was the most current QCEW data available from the BLS. Our 2019.2 datarun was based on this data in combination with the prior three quarters, so the “Current Year Jobs” are the 2018 Jobs.
Our methodology for current year job counts at any given time averages the last four quarters of QCEW to produce an annual picture. In the 2019.2 datarun, 2018 job counts were based on the average of the latest 4 quarters available from QCEW: 2018Q3, 2018Q2, 2018Q1, and 2017Q4. in the 2019.3 datarun, 2018 job counts were based on QCEW 2018Q4, 2018Q3, 2018Q2, and 2018Q1. In the 2019.4 datarun, 2019Q1 QCEW became available, making 2019 the current jobs year in the 2019.4 datarun.
Job counts for future years are projected based on past trends.
See this article for more on Lightcast’s Projections.
A jobs multiplier indicates how important an industry is in regional job creation. A jobs multiplier of 3, for example, would mean that for every job created by that industry, 2 other jobs would be created in other industries (for a total of 3 jobs).
Jobs multipliers are easily misinterpreted–jobs multipliers of 17 or higher are sometimes seen–but a high jobs multiplier for a set of one or more industries in an added-jobs scenario does not necessarily mean that attracting businesses in those industries to the region is the best of most viable option for regional economic growth.
Jobs multipliers are primarily tied to the type of industries in the scenario–industries with a high sales/labor ratio typically have a high jobs multiplier, and vice versa. For example, a nuclear power plant might have only 20 workers, but “behind” each of those workers there are millions of dollars of equipment costs and millions of dollars of electricity being generated. Thus, if we bring 20 more nuclear power jobs in to the region, it would involve a huge amount of investment flooding into the region (to build another nuclear power plant or double the size of the current one) and millions of dollars in new sales and profits.
Some of that mon
ey would go to the employees’ high salaries, some would go to local construction companies, real estate, janitorial services, etc. The overall jobs multiplier would be impressive–each new job in nuclear power might support 14 other jobs scattered throughout the rest of the economy (i.e. a jobs multiplier of 15). However, the effort it takes to attract 20 jobs in nuclear power (with all the necessary infrastructure) is substantially more than to attract 20 jobs in an industry with a lower jobs multiplier.
Job Posting Analytics.
Labor force encompasses all employed individuals as well as individuals seeking jobs.
Labour Force Survey Canada (LFS)
The Labour Force Survey is a study of the employment circumstances of the Canadian population. It is the largest monthly household study in Canada and provides the official measures of employment and unemployment.
Labour Force Survey UK (LFS)
The Labour Force Survey (LFS) in the UKmeasures all aspects of people’s working life.
The topics covered include-
Education and training needed to equip them for work
Occupation specific information
Job search for those out of work
Income from work and benefits
Source agency: The Office of National Statistics
Labor Market Information (LMI)
Labor Market Information (LMI) is reported on two levels: traditional or government LMI, and real-time LMI. Traditional LMI is data about the labor market that is collected and published by public sources (such as the Bureau of Labor Statistics, the U.S. Census, and the Bureau of Economic Analysis) for standardized industries and occupations. Real-time LMI is data taken from online job postings and profiles and is not governed by any one entity.
Institutions of higher education, non-traditional training providers such as bootcamps, companies, and organizations that provide training for employers.
The Local Employment Dynamics (LED) Partnership is a voluntary federal-state partnership that was started in 1999. Its main purpose is to merge data from workers with data from employers to produce a collection of enhanced labor market statistics known collectively as Quarterly Workforce Indicators (QWI), subject to strict protection of the identity and confidentiality of the individual respondents.
LEHD Origin-Destination Employment Statistics (LODES)
The Census’s Longitudinal Employer-Household Dynamics (LEHD) program contains several datasets, one of which is the Origin-Destination Employment Statistics (LODES) dataset. This dataset further contains three parts:
Residence Area Characteristics
Work Area Characteristics
These three pieces together provide information on commuting patterns by 2-digit industry between census tracts.
Lightcast uses them together to form the basis of Occupation by Residence data. Lightcast also uses Workplace Area Characteristics (WAC) to help build the commuting data used in Lightcast’s Input-Output model.
Attempts to score, between 1-100, the required level of knowledge, skill, or ability that is required of an occupation. Typically Importance and Level are similar, but there are cases such as a Registered Nurse’s knowledge of medicine and dentistry. They usually require “65 out of 100” in terms of their level of knowledge, but the importance of having that knowledge is scored “91 out of 100.” They don’t need to know everything about the topic, but what they do need to know is very important to their job.
By comparing Level and Importance across all occupations, we can view similar occupations based on overlap between the two.
Lightcast Occupation Taxonomy
The Lightcast Occupation Taxonomy (LOT) is our in-house occupation taxonomy. The taxonomy identifies roles that are the same, across employers and geographies, regardless of job title. This is especially important in emerging fields, when job titles can evolve quickly. It is composed of four different levels (Career Area, Occupation Group, Lightcast Occupation and Specialized Occupation).
Lightcast Occupations are working roles within the Lightcast Occupation Taxonomy that require a distinct mix of knowledge, skills, and abilities, and are performed using a variety of activities and tasks. Lightcast Occupations are designed to align easily with national taxonomies, undergraduate degree programs, and the career and career aspirations associated with workers entering the workforce or entering a new industry for the first time.
Local Area Unemployment Statistics (LAUS)
The Local Area Unemployment Statistics (LAUS) program 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.
Lightcast uses LAUS data to produce unemployment counts and labor force participation data. Each Lightcast datarun includes the latest month of LAUS data that was available when the datarun was processed.
Local Authority (LAU)
A local authority is a UK government organisation that is responsible for all the public services and facilities in a particular area. As the country is broken down into local levels it is the most detailed region selection we can look at, and for this reason it is also the smallest region you can look at on Analyst
One one of the region choices on Analyst UK.
Location Quotient (LQ)
Location quotient (LQ) is a way of quantifying how concentrated a characteristic of a particular region is compared to the nation. These characteristics could be an industry's or occupation's share of employment, resident demographic, online profiles or job postings. The LQ is the calculation that reveals what makes that particular region “unique” in comparison to the national average.
For a longer explanation, see Lightcast’s article on Location Quotient.
Source: Lightcast’s proprietary employment data.
Longitudinal Employer-Household Dynamics (LEHD)
The Longitudinal Employer-Household Dynamics program is part of the Center for Economic Studies at the U.S. Census Bureau. The LEHD program produces new, cost effective, public-use information combining federal, state and Census Bureau data on employers and employees under the Local Employment Dynamics (LED) Partnership. Their mission is to provide new dynamic information on workers, employers, and jobs with state-of-the-art confidentiality protections and no additional data collection burden.
The Census’s Longitudinal Employer-Household Dynamics (LEHD) program contains several datasets, one of which is the Origin-Destination Employment Statistics (LODES) dataset. This dataset further contains three parts:
Residence Area Characteristics
Work Area Characteristics
These three pieces together provide information on commuting patterns by 2-digit industry between census tracts.
Lightcast uses them together to form the basis of Occupation by Residence data. Lightcast also uses Workplace Area Characteristics (WAC) to help build the commuting data used in Lightcast’s Input-Output model.
Make and Use Tables (MUTs)
The BEA produces Make and Use Tables (MUTs), which are the basis for Input-Output modeling in the United States. The make table is a matrix that describes the amount of each commodity made by each industry in a given year. The use table is a matrix that describes the amount of each commodity used by each industry in a given year. The tables are updated annually.
The MUTs are used in the Lightcast Input-Output Model to produce an industry-by-industry matrix describing all industry purchases from all industries.
Median Household Income (MHI)
Median household income (MHI) refers to the distribution of household income into two equal groups, one having incomes above the median, and other having incomes below the median.
A household is defined as persons classified as members of a married-couple family, other family type, or as an unrelated individual. Their monthly family income, therefore, represents the sum of all cash income received by the individual and/or other family members. It may represent income from employment, assets (such as CD’s, rental property, savings accounts), and other sources (such as Social Security, Aid to Families With Dependent Children, pensions , State unemployment compensation, and so on).
Lightcast’s Median Household Income comes from the five year ACS data and includes data for individual ZIP codes, Census Tracts, counties, MSAs, States, and the nation. Lightcast does not provide MHI when aggregating regions, since one cannot create a new median by averaging the medians of those individual regions. ACS five year data has a two-year lag between when the data is collected and when it is released (i.e. a late 2017 Lightcast data run would include 2011-2015 ACS data).
Source: The Census’s Median Household Income
Median Posting Duration
Median posting duration shows the number of days a job posting is live and accepting applicants in your selected region, occupation, company, etc. In this example, the median number of days is 36, meaning that half of postings stay up longer and half are removed earlier.
Metropolitan Statistical Area (MSA)
A metropolitan statistical area is an area containing a substantial population nucleus, together with adjacent communities having a high degree of economic and social integration with that core. According to the United States Census Bureau, each metropolitan statistical area must have at least one urbanized area of 50,000 or more inhabitants. Pending approval, this minimum population threshold will increase to 100,000 according to the recommendations of the Metropolitan and Micropolitan Statistical Area Standards Review Committee.
A credential that reflects the mastery of knowledge and skills that is typically more narrow than traditional degrees, certificates, and certifications.
Lightcast’s military employment counts include both active duty and Reserve military personnel. National Guard personnel are also included as they are a subset of the Reserves. Military employment is included in Non-QCEW data. Class of Workers details can be found here.
In shift share analysis, this reflects regional growth that can be attributed to positive trends in the specific industry or occupation at a national level. For example, nursing jobs might be growing in your region and that’s great. However, looking at the national trends for nursing jobs reveals that they’re growing most everywhere. In this case, your region isn’t necessarily “excelling” at providing nursing jobs; they’re doing well everywhere, and every region in the country will likely see some growth as a result.
The industrial mix effect is the number of jobs we would expect to see added (or lost) within an industry in your region, based on the industry’s national growth/decline. If the industry is growing or declining at the national level, it can dependably grow or decline in smaller regions.
Industrial mix effect is calculated by applying the job growth of the industry at the national level to the same industry at the regional level. We start by subtracting the national growth rate of the overall economy from the national growth rate of the specific industry. This gives us a national industry premium which is an indication of how much that industry outperformed or underperformed the economy as a whole nationwide.
Industry Growth Rate – National Economy Growth Rate = Industry Premium
This rate (a percentage) is then applied to the number of the industry’s regional jobs:
Industry Premium x Number of Regional Industry Jobs = Industrial Mix Effect
See this longer article on shift share and its component parts.
A multiplier is a way of measuring how important one industry is to other industries in the region. So if an industry has a multiplier of 2.5, for every positive or negative change on that industry, the total effect on the regional economy will be 2.5 times the original change. Lightcast’s multipliers are developed in-house through our proprietary Input-Output model, which uses Lightcast’s final unsuppressed industry data, gravitational flows, commuting patterns, and the BEA’s “make and use” tables, among other sources.
Sources: Lightcast’s proprietary Input-Output model, the Flegg Location Quotient,
National Center for Education Statistics (NCES)
The National Center of Education Statistics is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. NCES is located within the U.S Department of Education and the Institute of Education Sciences. NCES fulfills a Congressional mandate to collect, collate, analyze, and report complete statistics on the condition of American education; conduct and publish reports; and review and report on education activities internationally.
National Household Survey (NHS)
The National Household Survey (NHS) was a voluntary survey (not required by law) that replaced the annual census in 2011. The topics of the survey included Aboriginal Peoples, Immigration and Ethnocultural Diversity, Education and Labour, Mobility and Migration, Language of Work, and Income and Housing. As the survey was not mandatory, fewer responses were recorded than in previous years, and the annual census has since been reinstated as the primary survey. NHS data is used only for datasets in 2011.
National Income and Product Accounts (NIPA)
NIPA is a set of tables produced by the Bureau of Economic Analysis (BEA). The NIPA tables cover a wide variety of economic measures for the nation. NIPA is updated periodically throughout the year and can be between a month and several years old depending on the specific account.
Lightcast uses NIPA data in the creation of the Input-Output Model. Specifically, it provides initial estimate values for the national model.
National Industry-Occupation Employment Matrix (NIOEM)
The Bureau of Labor Statistics publishes an industry-by-occupation employment matrix every two years as part of its Employment Projections program. The BLS projects this matrix out 10 years into the future, essentially providing current and future staffing patterns.
Lightcast uses NIOEM to adjust its industry projections.
Click here to read more about the BLS’s NIOEM dataset.
Natural Language Processing (NLP)
The use of computers to understand human language.
National Occupation Classification (NOC)
The National Occupational Classification (NOC) system is used by Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of about 500 unit groups according to their occupational definition. To facilitate classification, unit groups are combined to form about 150 minor groups, about 40 major groups, and 11 broad occupational categories.
The NOC system uses codes to divide occupations into four levels: major groups, minor groups, broad occupations, and detailed occupations.
3: Health occupations (Broad occupational category)
32: Technical occupations in health (Major group)
322: Technical occupations in dental health care (Minor group)
3222: Dental hygienists and dental therapists (Unit group)
Lightcast currently uses the NOC 2016 Version 1.1 Classification.
Source: StatCan’s NOC 2016 Version 1.1.
Not Elsewhere Classified - used for a classification that is not in any other grouping.
Net Commuters is the difference between the occupational residents in a region and the occupational employment in a region. For a region in which more workers live than there are jobs in the region, net commuting is negative (i.e. the net result is that workers commute out of the region for work). For a region in which there are more jobs than there are resident workers, net commuting is positive (i.e. the net result is that workers commute into the region for work).
Commuting patterns are derived from the Census Bureau’s LED LODES dataset. These commuting patterns are applied to to final Lightcast industry job counts (from the Bureau of Labor Statistics’ Quarterly Census of Employment and Wages (QCEW) dataset) to create an industry-based commuting/industry-by-residence dataset. This industry-based set is transformed to occupations through staffing patterns, resulting in occupation-based commuting/occupation-by-residence data.
Nomenclature of Territorial Units for Statistics (NUTS)
The location classification system for Europe.
Non-Employer Statistics (NES)
Non-employer Statistics is an annual series that provides subnational economic data for businesses that have no paid employees and are subject to federal income tax. The data consist of the number of businesses and total receipts by industry. Most non-employers are self-employed individuals operating unincorporated businesses (known as sole proprietorships), which may or may not be the owner’s principal source of income.
NES data informs Lightcast’s self-employed (Class 3) and Extended Proprietor (Class 4) data.
Companies in job postings reports are categorized as "staffing" or "non-staffing". Non-staffing companies are direct employers, hiring individuals to work for themselves rather than for placement at a different company.
North American Industry Classification System (NAICS)
The North American Industry Classification System (NAICS) is the standard federal system for classifying business establishments. Each establishment is assigned a six-digit code and category title, organizing them primarily by similar production processes into five levels: sectors, subsectors, industry groups, industries, and national industries (national industries are specific to one or more of the United States, Canada, and Mexico). Codes are hierarchical: less detailed categories are derived by removing digits from the end of more detailed codes.
23: Construction (sector)
236: Construction of Buildings (subsector)
2362: Nonresidential Building Construction (Industry Group)
23622: Commercial and Institutional Building Construction (industry)
236220: Commercial and Institutional Building Construction (national industry which in this case is identical to its parent industry)
The NAICS classification is updated every five years to better reflect economic realities.
For information on Lightcast’s use of NAICS codes (including departures from the standard classification), see this article.
The term occupation refers to professions or careers in the workforce. The occupation describes the role – what the worker actually does. This is distinct from the job title, which is what the worker is called. Occupations are also differentiated from jobs, as jobs show the count of positions held within a certain occupation. See also: Lightcast Occupations, Lightcast Occupation Taxonomy.
Occupation earnings data comes from the BLS’s OES dataset. It is collected from the employer’s perspective, meaning earnings data is pre-tax (individual employees’ tax withholdings will vary, so earnings are reported pre-tax). Because it is collected from the employer’s perspective, earnings data is also counted by the place of the employee’s work, not the employee’s residence. Occupations have average hourly earnings as well as percentile earnings for five percentiles (10th, 25th, 50th (median), 75th, and 90th).
Average earnings are determined by dividing the total earnings for the occupation by the number of jobs in the occupation. Percentile earnings indicate what percent of the jobs in the occupation earn that amount or less. For example, 10th percentile earnings of $12/hr. indicate that 10% of the workers in that occupation make $12/hr. or less. Median earnings of $15/hr. would mean that half of workers in that occupation make more than $15/hr., and half make less than $15/hr. 10th percentile earnings are often used as a proxy for entry level wages, as they represent some of the lowest earnings in the occupation.
Earnings are reported in terms of hourly income rather than annual income for all but a handful of occupations. For occupations with earnings reported annually, we divide by 2080 (number of hours in a working year) to determine hourly earnings.
Occupation earnings include the following:
Cost of living allowances
Occupation earnings do not include the following:
Holiday premium pay
Jury duty pay
Meal and lodging payments
Weekend premium pay
OES provides definitions for all the categories listed above.
Canada source: Lightcast’s industry data, regional occupation data from the Labour Force Survey (LFS), and regional staffing patterns taken from the Census.
A Word about Percentile Earnings
Various reports within Lightcast’s Analyst and Developer tools allow users to combine occupation percentile earnings for various occupations or regions. These combinations are powered by a proprietary occupation aggregation methodology that represents the combined wage curves of various occupations better than a weighted average. For this reason, users should not expect to be able to combine percentile earnings by hand and match combined percentile figures as displayed in Analyst. More information on Lightcast’s occupation percentile earnings aggregation can be found here.
Source: Lightcast’s proprietary employment data, relying heavily on occupational earnings reported in OES.
Occupation Groups are clusters of occupations within the Lightcast Occupation Taxonomy that share very similar skill or role requirements. They describe the different “fields” or “disciplines” available in the market for early- or pre-career students.
Occupational Employment Statistics (OES)
OEWS was knows as simply "Occupational Employment Statistics" prior to Spring 2021.
Occupational Employment and Wage Statistics (OEWS)
The Occupational Employment and Wage Statistics (OEWS) program estimates employment and wages for most occupations by industry and sector at the national level, and by occupation at the state and metropolitan statistical area (MSA) and non-MSA levels in the 50 states and the District of Columbia. OEWS accounts for 1.1 million establishments and 57% of national employment, including railroad, but excluding military, agriculture, fishing, forestry, private households, self-employment, and others.
How Lightcast Incorporates OEWS
OEWS is our primary source of occupation data, but we compensate for OEWS’s general weaknesses and lack of valid historical data by utilizing stronger, more accurate industry employment counts from QCEW, County Business Patterns (CBP), and American Community Survey (ACS), among others. We then apply regionalized, OEWS-based staffing patterns to the industry data to show the distribution of jobs by occupation.
Lightcast gathers occupation earnings data from OES. We use unsuppression techniques to fill in missing values as appropriate, and also build a time series of OEWS data in order to present historical occupation earnings.
For a more detailed explanation of how Lightcast incorporates OEWS data into occupational processes, see this article.
Strengths of OEWS
OEWS has estimates for specific industries, including national industry-specific occupational employment and wage estimates.
OEWS has estimates for individual states, including cross-industry occupational employment and wage estimates for individual states.
OEWS has estimates for metropolitan and nonmetropolitan areas, which together cover the entire United States.
Weaknesses of OEWS
OEWS is merely a survey and is not based on administrative records like Quarterly Census of Employment and Wages (QCEW) from the BLS; because of this, OEWS’s figures aren’t as comprehensive as most industry data.
Not all metropolitan and nonmetropolitan areas have information for all occupations.
Only 57% of employment is covered in the OEWS survey (compared to the 95% of wage-and-salary jobs captured by QCEW), which excludes all industries under NAICS 11 (agriculture, forestry, fishing, and hunting) except for logging, support activities for crop production, and support activities for animal production.
The OEWS survey takes up to three years to complete, so the BLS states that it is less useful for measuring change in job counts or wages over time. An apparent increase in wages, for example, could just as likely be due to different businesses responding to the survey in one year, changes in the occupational, industrial, and geographical classification systems, changes to collection or estimation methods, or changes to other methodologies in the survey. Lightcast’s occupation methodology (see article referenced above) is designed to counteract this weakness in OEWS data.
Occupational Information Network (O*NET)
O*NET provides occupation data such as knowledge, skills, and abilities needed to perform the work, as well as education and training requirements and alternate job titles. Lightcast incorporates this data throughout its tools in various ways.
The O*NET Program is the nation’s primary source of occupational information. The data are essential to understanding the rapidly changing nature of work and how it impacts the workforce and U.S. economy. From this information, applications are developed to facilitate the development and maintenance of a skilled workforce.
Central to the project is the O*NET database, containing hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated from input by a broad range of workers in each occupation.
O*NET updates do not follow a schedule; Lightcast monitors O*NET for updates and downloads new data as it becomes available.
The programs in the region of study that may train for this occupation. Lightcast uses a default crosswalk to build these associations; the occupations linked to a particular program may be edited from the program’s Program Overview page.
Office of National Statistics (ONS)
The Office of National Statistics (ONS) is the UK’s largest independent producer of official statistics.
Source agency: The Office of National Statistics website
Other Vectors (I-O)
Also called “Other non-industries”, this consists primarily of two non-industries that nonetheless capture genuine income-generating activity within the economy.
Owner-Occupied Dwellings, which captures the activity of people who own and occupy their residences. For a further definition of Owner-Occupied Dwellings, see Appendix B of our Input-Output Model Documentation.
Second are Government Enterprises, defined by the BEA as “Government agencies that cover a substantial portion of their operating costs by selling goods and services to the public and that maintain their own separate accounts.” This is accounted for at the Federal, State, and Local government level.
Source: Lightcast’s Proprietary data, primarily from the Bureau of Economic Analysis (BEA)
Owner-Occupied Dwellings (OOD)
A housing unit is owner-occupied if the owner lives in the unit, even if it is mortgaged or not fully paid for. The owner or co-owner must live in the unit and usually is Person 1 on the questionnaire. The unit is “Owned by you or someone in this household with a mortgage or loan” if it is being purchased with a mortgage or some other debt arrangement such as a deed of trust, contract to purchase, land contract, or purchase agreement. The unit also is considered owned with a mortgage if it is built on leased land and there is a mortgage on the unit. Mobile homes occupied by owners with installment loan balances also are included in this category.
The process of analyzing a string of symbols, either in natural language, computer languages, or data structures, conforming to the rules of formal grammar, also known as syntax analysis or syntactic analysis.
The U.S. Patent and Trademark Office maintains counts of patents granted by year and county. Lightcast collects these counts. The data is currently only available via API. Patents may be used as a proxy for business creativity, entrepreneurship, and small business initiative.
The data can be viewed here.
The Census’s Population Estimates Program produces estimates of the population of the United States. The latest US Decennial Census population figures are used as a base, and population change is estimated by modeling estimated births, deaths, and migration. Population Estimates are released annually.
Lightcast uses Population Estimates nearly unchanged in the years for which they are available as the basis for demographics counts for the nation, states, and counties.
Posting Intensity is the ratio of total to unique (deduplicated) job postings. A higher than average posting intensity can mean that employers are putting more effort than normal into hiring that position. Posting intensity is available by occupation, by job title, by company, and by region.
Postsecondary Student Information System (PSIS)
Lightcast’s source for enrollments and completions data for higher education institutions in Canada. PSIS’ reference period begins the day after the end of the institution’s previous winter term (usually in April, May, or June), and goes through the last day of the winter term of the following year.
PSIS aims to collect information from “Canadian public postsecondary institutions (universities, community colleges and trade and vocational training centres).” PSIS is a mandatory survey. According to PSIS, the response rate was 94.4% in 2016/2017.
Lightcast data uses the term program in reference to select courses offered at accredited colleges or universities. Programs are oriented toward a specific occupation, and completion of these programs is often signified by a specific award level, such as Baccalaureate, Master’s, and Doctorate degrees.
Property Income/Profits (I-O)
Property Income, sometimes called “non-labor income” or “profits,” is generally what is left after businesses make payments for labor, taxes on production, and the purchase of produced inputs.
Public Use Microdata Sample (PUMS)
The American Community Survey (ACS) Public Use Microdata Sample files are a set of untabulated records about individual people or housing units. The Census Bureau produces the PUMS files so that data users can create custom tables that are not available through pretabulated (or summary) ACS data products.
Purdue Industry Clusters
The Purdue industry clusters were created through a joint project between Lightcast and the Center for Regional Development at Purdue University. These can be found by clicking Groups in the header bar and selecting Industry Groups, then Template Groups.
Clusters are groups of interconnected industries that typically purchase from one another or otherwise benefit from being nearby each other. Many different definitions exist for different clusters, and we encourage our clients to use their local knowledge when doing cluster analysis. The Purdue clusters can be used as is or you can create a new group based on a Purdue cluster and modify it to your local needs.
See this article on how to create a new group from a template group for more info.
In Lightcast data, the term qualifications refers to the certifications decided on by a third-party entity (school, government, industry, etc.) that acknowledges a body of skills and abilities (e.g. MBA, Certified Registered Nurse).
For example, a job posting for a Registered Nurse may state that the qualifications for the position include a Bachelor’s of Science in Nursing from an accredited university and a nursing license from the state nursing board.
Quarterly Census of Employment and Wages (QCEW)
Quarterly Census of Employment and Wages (QCEW) is a dataset published by the Bureau of Labor Statistics (BLS). QCEW is the backbone of Lightcast’s core LMI data, providing establishment counts, monthly employment, and quarterly wages, by NAICS industry, by county, and by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (nation, state, and Metropolitan Statistical Area [MSA]).
Lightcast produces a slightly modified form of the BLS QCEW dataset.
Lightcast provides estimates for suppressed data (roughly 60% of QCEW data points are suppressed). For more on the importance of unsuppression, see this article.
Lightcast alters the NAICS classification of public-sector employment to make it more compatible with other data sources. For more information, see this article.
Lightcast transforms the data to use consistent county and NAICS definitions from 2001 to the present; original QCEW data does not use consistent definitions year-to-year.
Strengths of QCEW
Because QCEW is based on official government documentation (via state and federal unemployment agencies), the data is highly reliable and is considered the “gold standard” of industry data and of employment counts in the United States.
QCEW is comprehensive, capturing 95% of US wage-and-salary jobs.
QCEW can be viewed at a variety of detail levels, both geographically (by county, MSA, state, or national levels) and by industry level (available at 2-, 3-, 4-, 5-, and 6-digit levels).
Weaknesses of QCEW
There is about a five- to six-month lag between when the initial data is collected and when it is released. The releases occur quarterly.
Much of QCEW’s private-sector county level data (approximately 60%) is suppressed to protect the confidentiality of certain local businesses.
QCEW does not report on self-employed, military, railroad, and certain farm, domestic, and non-profit workers, among others.
Quarterly Workforce Indicators (QWI)
Quarterly Workforce Indicators (QWI) provides local labor market statistics by industry, worker demographics, employer age, and size. Unlike statistics tabulated from firm or person-level data, QWI source data is unique job-level data that link workers to their employers. Because of this link, labor market data in QWI is available by worker age, sex, educational attainment, and race/ethnicity. This allows for analysis by demographics of a particular local labor market or industry—for instance, identifying industries with aging workforces. Links between workers and firms also allow QWI to identify worker flows—hires, separations, and turnover—as well as net employment growth. (Since most hiring activity is the consequence of worker turnover rather than employment growth, a focus on employment growth alone may misrepresent employment opportunity in the local labor market.
How Lightcast Incorporates QWI
Industry Demographics Data
Lightcast uses QWI to create our detailed industry data, augmenting our regular employment data with QWI’s age/gender, and race/ethnicity demographics. After downloading individual state files, we prep QWI data through several steps.
First, we unsuppress QWI at its native level of industry detail (approximately 4-digit NAICS). Since QWI is compatible with QCEW, we then use QWI age/gender and race/ethnicity percentages to disaggregate our class of worker QCEW values, which are at 6-digit NAICS detail. However, we rely less heavily on QWI for creating detailed industry data for the remaining classes of worker (Non-QCEW, Self-Employed, and Extended Proprietors), preferring American Community Survey in these instances.
Lightcast also uses industry hires data from QWI as the basis for occupational hires. Job growth for occupations in each industry are combined with Bureau of Labor Statistics (BLS) separations data to model the pattern of occupational hiring needs for each industry–the percent of openings in each industry that come from openings in each occupation. This percent breakout is then applied to the QWI industry hires figure, yielding occupation hires.
Strengths of QWI
QWI provides a unique link between individuals and employers
QWI helps distinguish actual employment opportunity from general employment growth in a given area
QWI allows for easy wage comparisons between new hires, continuing workers, and similar workers across various regions
QWI data is released quarterly and the numbers are recalculated with every release to improve accuracy
QWI is compatible with Quarterly Census of Employment and Wages, Lightcast’s most important industry dataset
Weaknesses of QWI
QWI data is published at the 4-digit NAICS level rather than the 6-digit
QWI is produced on a quarterly schedule with a time lag of nine months (three quarters)
Like many public datasets, QWI contains many nondisclosed values that must be replaced with educated estimates
QWI does not distinguish between voluntary and involuntary separations
The Census’s QWI Explorer tool can be used to dig deeper into QWI data.
Race and Ethnicity
Based on 2010 government census categories of nationality, ethnicity, and race.
There are some slight variation between the categories used between the England & Wales census, and the Scottish census. In order to characterise over time, we used a common denominator, which required a reduction of detail. We have condensed 18 government categories into 16 in Analyst.
The federal government tracks several racial categories (White, Black or African American, Asian, etc.) but only two ethnic categories, Hispanic and Non-Hispanic. There may be some overlap between race and ethnicity unless the two characteristics are clearly separated, e.g., “White non-Hispanic,” “White Hispanic,” and “Non-white Hispanic.”
Railroad Retirement Board (RRB)
Because the BLS’s Quarterly Census of Employment and Wages (QCEW) dataset does not cover the railroad industry, Lightcast uses supplemental data from the Railroad Retirement Board to supply job counts for the industry. This data is published annually, usually in July. Job counts data does not contain suppressions, even at the county level.
Regional Matrix (I-O)
The Regional Matrix (Z Matrix) is an 1800 by 1800 matrix that describes the spending from one sector of the regional economy to the other. It is part of the foundation for Input-Output Modeling. Lightcast’s Input-Output model goes above and beyond the usual I-O model in that Lightcast also models the changes to Occupations and Demographics. For further explanation, we recommend reading the Input-Output documentation available here.
Source: The Regional Matrix is primarily based on Lightcast’s proprietary data and data from the Bureau of Economic Analysis (BEA).
Regional Requirements (I-O)
A table quantifying the goods and services that your region requires from each industry, as well as the degree to which those requirements are met within the region. For instance, if the regional requirements of Seattle for Petroleum Refineries (324110) is $8.4B, that means that all of the industries in Seattle spend $8.4B on Petroleum Refineries. The table will also contain columns outlining how much of the $8.4B demand is satisfied in the region, and how much of the $8.4B must be purchased from outside the region to satisfy the remainder of demand. See also Industry Requirements.
Source: Lightcast’s model, incorporating data from the BEA.
Remote Job Postings
The titles and bodies of all job postings are scanned for language indicating that the position is remote, hybrid, or onsite. This includes looking for phrases such as “work from home”, “remote”, “position can be located anywhere”, “partially remote” and the like. Postings found to contain job location language are tagged as Remote, Hybrid, or Non-Remote. Postings that do not contain identifying language are tagged as Unknown. Remote postings also include positions that need to be located within a particular region but not in an office.
Most job boards and posting software require the poster to enter a physical location. Additionally, our remote tagging is separate to our location tagging, which extracts the main location from the posting. For instance, there are postings that are 100% remote, work from anywhere, but will also list a location on the posting so it will appear under location searches. There are also remote postings that are restrictive on what location the candidate can be in, either due to tax reasons, time zone reasons, or for required on-site visits.
A detailed description of Lightcast’s job postings methodology is available here.
Replacements are jobs that will need to be filled by new hires due to existing workers leaving the occupation. Replacements are part of the Openings calculation.
Openings = Replacements + Growth.
For more information on how Replacements are calculated, see Lightcast’s methodology for Openings.
Resident Workers/Occupation by Residence Data
Unlike the majority of Lightcast data, resident worker data is presented in terms of where workers live rather than where they work. For instance, though ZIP code 85042 might have 50 software developers working in the region, there might only be 25 software developers who live there. This data is helpful in demonstrating workforce availability and helping companies locate the talent they need.
Source: This data comes from the Census’ LODES program and is most commonly used in their On The Map tool. Within the LODES dataset, Lightcast makes particular use of the Origin and Destination (OD) data, Regional Area Characteristics (RAC), and Workforce Area Characteristics (WAC) data to produce occupation by residence data.
Methodology Overview: QCEW is the foundation of Lightcast employment data for both industries and occupations. This is because the US lacks a comprehensive census-based (administrative records as opposed to surveys) source for occupation data, so Lightcast produces occupation data by running industry data through a regional staffing pattern derived from the OES survey data. This essentially uses the strengths of all available data, the numerical accuracy of QCEW and the less reliable occupation detail of OES to create a synthetic dataset of detailed occupational estimates. To create occupation by residence data, Lightcast also includes LODES as an input to the model to first convert industry data from place of work to place of residence before applying the staffing pattern to generate occupation data. LODES can lag behind other sources by 2-3 years, so we create a commuting pattern specific to each year of Lightcast employment data to model the employment from place of work to place of residence. The commuting pattern is adjusted to the matching year of industry employment before being applied (e.g.: to produce 2020 occupation by place of residence data, the industry commuting pattern from the closest year of LODES data is adjusted to match the 2020 industry data and is then run through a 2020 staffing pattern).
Return on Investment.
Regional Purchasing Coefficient.
In input-output modeling, Sales is an industry’s total annual sales (gross receipts), both to other industries and to consumers as well. Sales is representative of all four Classes of Worker. For the Retail (44), Wholesale (42), and Transportation (48) sectors, sales are only inclusive of the respective margin.
US: Lightcast’s model, incorporating data from Bureau of Economic Analysis (BEA).
CA: Lightcast’s model, incorporating data from Statistics Canada (StatCan).
Sales multipliers show how “deeply-rooted” an industry is in your region—for example, a highly-developed cluster will have a high sales multiplier because every dollar fed into the cluster from the outside has a high ripple effect, propagating through the regional economy for some time before it leaks out. One dollar of sales coming into a highly-developed Automotive Manufacturing cluster, for example, might have a ripple effect of 2.8 (that dollar led to a total of $2.80 in regional sales). Industries and clusters with very low multipliers are usually owned outside of the region (so the profit is lost immediately) and also buy mostly from outside the region (a “shallow root system”).
Web scraping is the process of gathering data from the internet, usually using automated bots or web crawlers. Lightcast’s job postings are scraped from company websites and job boards and aggregators. For more about how Lightcast scrapes job postings, see this article.
Search Engine Optimization (SEO)
Search Engine Optimization is often about making small modifications to parts of your website. When viewed individually, these changes might seem like incremental improvements, but when combined with other optimizations, they could have a noticeable impact on your site’s user experience and performance in organic search results.
Source: Google Support
A self-employed individual directly offers their personal services to others in return for compensation, instead of earning an income from a business, corporation, or employer.
A separation is indicated when a job is present in one quarter, but is not present in the following quarter.
A separation is reported by the Quarterly Workforce Indicators when an individual’s Social Security Number that appeared on a company’s payroll in the previous quarter is no longer present. Separations data is published at the industry level and modeled to occupation via staffing patterns. The QWI program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws, similar to the QCEW program. For more information, see this publication.
Source: Census Bureau and Lightcast’s proprietary employment data
Used in both industry and occupation contexts, Shift Share is a standard method of regional economic analysis that helps identify whether job change in an industry/occupation in a region is due to national factors–the “rising tide lifts all boats” phenomenon–or whether it’s due to factors within the region of study itself.
An industry/occupation could be growing/declining in a region because of one or several of the following factors:
Growth Effect, the overall growth/decline of the entire national economy;
Industry/Occupation Mix Effect, the growth/decline of the industry/occupation in question at a national level;
Competitive Effect, growth/decline that cannot be explained completely by national trends and therefore highlights something unique about the region of study.
The most important of the three is Competitive Effect, which identifies region-specific factors as being responsible for the growth/decline of the industry/occupation in question.
Expected Change shows the expected growth/decline for the industry/occupation in region in question given the National Growth Effect and the Industry/Occupation Mix Effect. The Competitive Effect is the leftover effect (if any) that cannot be explained by the National Growth Effect and Industry/Occupation Mix Effects as shown in the Expected Change metric.
For a deeper dive into Shift Share, see this article.
Source: Lightcast’s proprietary employment data.
The unique skill demands associated with a given career field, region, or individual.
Skillify (verb): to translate curricular content (e.g. course descriptions or syllabi) into the skill-based language of the modern labor market.
In practical terms, “skillifying” your curriculum means identifying the work-relevant skills that you already teach, in the courses you already offer, and assessing how they align with the skills employers are asking for.
This insight supports curriculum development, employer engagement, enrollment marketing, and other mission-critical aspects of higher education.
See how to skillify your syllabi, with Skillabi.
Learn more about:
In Lightcast data, skills are competencies at specific tasks or familiarity with specific subjects and tools acquired through education or experience.
Specialized Skills: Skills that are primarily required within a subset of occupations or equip one to perform a specific task (e.g. “NumPy” or “Hotel Management”). Also known as technical skills or hard skills.
Common Skills: Skills that are prevalent across many different occupations and industries, including both personal attributes and learned skills. (e.g. “Communication” or “Microsoft Excel”). Also known as soft skills, human skills, and competencies.
Software Skills: Any software tool or programming component used to help with a job (e.g. Python, Workday, AutoCAD, Microsoft Excel, React.Js, Accounting Software, and 3D Modeling Software would all be considered “Software Skills”).
Certifications: Recognizable qualification standards assigned by industry or education bodies (e.g. “Cosmetology License” or “Certified Cytotechnologist”).
A set of correlated skills that relate to a particular theme of work.
Social Accounting Matrix (SAM)
A Social Accounting Matrix is a square matrix in which each account is represented by a row and a column. It provides a comprehensive picture of the economic transactions of an economy. Each cell shows the payment from the account of its column to the account of its row. Thus, the incomes of an account appear along its row and its expenditures along its column. For each account in the SAM, total revenue (row total) should be equal to total expenditure (column total).
Specialized Occupations are roles grouped at a level of granularity designed to match the expectations of business looking to hire, train and develop workers for the specialized roles in their workforce and for universities looking to track demand for specialized masters and certificate level programs. They are characterized by unique and value-added sets of skills, roles, and responsibilities, as well as additional education or credentials beyond the minimum requirements of the entry-level role they may extend from.
Companies in job postings reports are categorized as "staffing" or "non-staffing". Staffing companies are those that hire individuals for placement at a different company. This categorization is based on the business the company operates in, therefore companies that operate as 'staffing companies' will have all postings categorized accordingly.
Staffing patterns show the occupational makeup of an industry in percentages. For example, a (simplified) staffing pattern for the industry “Hospitals” might show that 10% of jobs in the hospitals industry are occupied by surgeons, 15% by general practitioners, 20% by nurses, 5% by information technology support staff, 5% by janitors, 1% by chief executives, and so on. See also Inverse Staffing Pattern.
US: Primarily the national OES staffing pattern, combined with projections from the National Industry-Occupation Employment Matrix and Lightcast’s proprietary employment data.
CA: Primarily the industry by occupation percentages from the Census at the provincial level and Lightcast’s proprietary employment data.
Standard Industrial Classification (SIC)
The Standard Industrial Classification (SIC) system is a United Kingdom government system to classify businesses by the type of economic activity in which they are engaged. These are split in 4 categories, with 1-digit being the most generalised and 4-digit being the most detailed.
Standard Occupation Classification - UK (UK SOC)
The Standard Occupational Classification (SOC) system is used to classify occupational categories for the purpose of collecting, calculating, or disseminating data. Occupations with similar job duties, and in some cases skills, education, and/or training, are grouped together.
Standard Occupation Classification (SOC) - US
The Standard Occupational Classification (SOC) system is used by US Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of about 800 detailed occupations according to their occupational definition. To facilitate classification, detailed occupations are combined to form about 450 broad occupations, about 95 minor groups, and 24 major groups. Detailed occupations in the SOC with similar job duties, and in some cases skills, education, and/or training, are grouped together.
The SOC system uses hyphenated codes to divide occupations into four levels: major groups, minor groups, broad occupations, and detailed occupations.
29-0000: Healthcare practitioners and technical occupations (major group)
29-1000: Health diagnosing and treating practitioners (minor group)
29-1020: Dentists (broad occupation)
29-1021: Dentists, general (detailed occupation)
The SOC classification system was updated in 2018, and Lightcast began using this 2018 SOC version in 2022 when OEWS, our source for occupational earnings data, started using this SOC version.
For more information on Lightcast’s use of SOC codes (including departures from the standard classification), see this article.
State and Local Government Finances
The Census’s State and Local Government Finances dataset (also called Census of Government, COG) contains comprehensive information on local government finances. The data is updated annually, usually in September.
State Personal Income/Local Area Personal Income (SPI/LPI)
Lightcast uses the SPI/LPI datasets primarily to provide earnings and employment estimates for various industries not covered in the QCEW Employees Class of Worker, most notably Military employment.
Statistics Canada (StatCan)
Statistics Canada is the national statistical office. The agency ensures Canadians have the key information on Canada’s economy, society and environment that they require to function effectively as citizens and decision makers.
Suppression refers to the practice of not disclosing (“suppressing”) data points that can be traced back to a particular person or business in a particular location. Government entities suppress data points in data sets whenever disclosing the data point in question would expose a business or individual.
Because Lightcast’s mission is to drive economic prosperity in our clients’ communities, we provide the most accurate labor market data possible. Accurate LMI allows our clients to make informed decisions for the good of their communities. For this reason, we apply sophisticated unsuppression techniques to government LMI data, providing educated and bounded estimates for suppressed data points.
To read more on government suppression and the extent to which suppression can impact data, see our blog post.
Survey of Enrollment, Payrolls and Hours (SEPH)
The Survey of Employment, Payrolls and Hours provides a monthly portrait of the amount of earnings, as well as the number of jobs (i.e., occupied positions) and hours worked by detailed industry at the national, provincial and territorial levels.
SEPH data provide the principal input to labour income estimates; they also serve as a proxy output measure for about 15% of real gross domestic product and ‘nominal’ gross domestic product.
In Lightcast data, Talent indicates the workforce currently available to employers. Data for Talent supply comes from Traditional LMI, job postings, and professional profiles.
Talent Growth Index (TGI)
Lightcast’s Talent Growth Index scores companies on a 0-100 scale where 100 signals top performing companies and 0 signals worst performing companies in the state within that 3-digit industry. There are three categories that comprise the Talent Growth Index: company postings, industry trends, and regional trends. The industry and regional trends compare change compared to the previous year. Company postings identify the most recent month of postings to the company’s 6 month rolling average.
This scoring was developed using a statistical model looking at 18 years of data to understand what metrics matter to company growth and how much each metric should be weighted. The model is both regionalized and unique to each industry. For instance an aerospace company in Washington will be scored differently than a aerospace company in Maryland. Similarly an aerospace company in Washington will be scored differently than a finance company in Washington.
Taxes on Production and Imports (TPI)
Taxes on production and imports (TPI) consist of state and local taxes—primarily non-personal property taxes, licenses, and sales and gross receipts taxes—and Federal excise taxes on goods and services. Special assessments are also included.
To see the tax implications of adding or removing 50 manufacturing jobs in Denver, TPI will measure the change in local, state, and federal tax revenue through the increased or decreased industry sales, specifically general sales and property taxes. It’s important to note that this change in tax revenue corresponds to the ripple effects and cannot be tied to a particular timeframe.
A timeframe is a period of study, defined by a start and an end year. In Lightcast reports, users select timeframes for which they want to study data. Lightcast provides data back to 2001 for most datasets, and projects data out 10 years from the current year for some datasets. Also see Start Year and End Year.
Top Regional Businesses
The businesses in the selected industry with the most local employment according to DatabaseUSA, Lightcast’s provider of business-level data. DatabaseUSA’s sources and methodology differ significantly from Lightcast’s, and some differences in NAICS classification can be expected. Analyst lists the first 5 businesses as a convenience for all customers; detailed tables are available for an additional fee.
Total Earnings (I-O)
The total industry earnings for a region. Includes wages, salaries, supplements (additional employee benefits), and proprietor income.
Total Job Postings
Total Job Postings is the total and unduplicated number of online vacancies scraped from over 45,000 websites.
Deduplication is the process of identifying duplicate job postings and only counting one of the duplicates. The total posting count is the count of postings before the deduplication process. The unique posting count is the count of postings after the deduplication process. For example, if a user runs a report that returns 12 total job postings and 2 unique postings, this means that the 12 postings contained 10 duplicates and only 2 unique job advertisements.
Turnover rate gives context for how often employees in a given occupation are moving to different employers.
Turnover rate is calculated by comparing total separations to total jobs (separations divided by jobs). A separation is recorded when an individual’s Social Security Number that appeared on a company’s payroll is no longer present. By comparing separations to the total number of jobs in an occupation, we can benchmark the level of movement taking place in that occupation.
Typical Entry Level Education
The education level most often needed to enter an occupation. Typical entry-level education is reported at the national level, so alternate paths to employment may exist in a region of study.
In the United States
The Unclassified industry (999999) is used by Quarterly Census of Employment and Wages to categorize businesses who did not report a NAICS code. These are mostly newer businesses who have not yet determined their proper NAICS code. The BLS sends a special form to these businesses to help them determine their proper NAICS so that future reporting is improved.
In an industry table, Lightcast displays a unique NAICS code X000 for “Unclassified Industry.” This is a bucket developed for businesses not reporting their NAICS to SEPH, placing them in the Unclassified Industry category.
In the United States:
The Unclassified occupation (99-9999) is a special SOC code Lightcast uses for the Extended Proprietors class of worker (Class 4). The Unclassified occupation is used as an occupational bucket for industries that don’t have self-employed staffing patterns.
Underemployment data helps communities identify the portions of their population who are underutilizing their skills or time. There are three types of underemployment:
The underemployment data in the Economy Overview compares the educational attainment of the working age population (25+) to the number of jobs (25+) by typical entry level education in the region.
15% of region A’s population has a high school diploma. 34% of jobs only require a typical entry level education of a high school diploma. This means that 19% of the region’s working age population would be over-skilled and have a higher degree than necessary for this jobs.
Population educational attainment level by county (ACS data from the Census)
Typical entry level education by occupation (BLS)
The Unclassified occupation (X000) is used as an occupational bucket for the Unclassified industry, which does not have a known staffing pattern. Without a staffing pattern, it’s not possible to translate industry employment to occupation employment. Therefore Lightcast uses an Unclassified SOC code to hold occupational information where an industry classification was not provided.
An estimate of total unemployed persons by industry or occupation in a region. Lightcast uses LAUS as the basis of its unemployment data, which uses a definition of unemployment roughly equivalent to U3, the most widely used measure. Available by county for all 2-digit NAICS and 2-digit SOCs.
Source: LAUS, combined with CIU and Lightcast’s proprietary employment data.
Unemployment Compensation for Federal Employees (UCFE)
The Unemployment Compensation for Federal Employees program provides benefits for eligible unemployed former civilian Federal employees. The program is administered by States as agents of the Federal government. This program is operated under the same terms and conditions that apply to regular State Unemployment Insurance. In general, the law of the State in which your last official duty station in Federal civilian service was located will be the State law that determines eligibility for unemployment insurance benefits.
Unemployment Insurance (UI)
The U.S. Department of Labor’s Unemployment Insurance programs provide unemployment benefits to eligible workers who become unemployed through no fault of their own, and meet certain other eligibility requirements. The following resources provide information about who is eligible for these benefits and how to file a claim.
Unique Job Postings
Unique Job Postings is the number of deduplicated job vacancy advertisements scraped from over 45,000 websites.
Deduplication is the process of identifying duplicate job postings and only counting one of the duplicates. The unique posting count is the count of postings after the deduplication process has taken place. The total posting count is the count of postings before deduplication. For example, if a user runs a report that returns 12 total job postings and 2 unique job postings, this means that the 12 postings contained 10 duplicates and only 2 unique job advertisements.
United States Postal Service.
Lightcast’s veteran data comes from the five year 2015 ACS data and includes veteran counts by county for the United States.
As defined by the ACS, veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty, not counting the 4-6 months for initial training or yearly summer camps.
ACS five year data has a two-year lag between when the data is collected and when it is released (i.e. late 2017 data run would include 2011-2015 ACS data). This data is typically updated during a late year data run.
Occupational wages, which are sometimes referred to as compensation, consist of percentile earnings and average earnings for the occupation.
Wages Multiplier (I-O)
The total wages created in a region as a result of a single dollar of new wages. This number includes the yield and the initial dollar addition. In other words, a wage multiplier of 1.82 is made up of the initial dollar added (1.0) and the further yield (0.82).
Source: Lightcast’s model, incorporating data from Statistics Canada (StatCan).
See Labor Force.
Workforce Investment Board (WIB)
Workforce Investment Boards direct federal, state, and local funding to workforce development programs. They also oversee the American Job Centers, where job seekers can get employment information, find out about career development training opportunities and connect to various programs in their area.