Glossary (Talent)

Active Job Postings are postings that are currently visible online and advertising a job vacancy.

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.

To analyze actual earnings as reported via government sources, we recommend Emsi’s occupational earnings or industry earnings. 

If present, Emsi 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.

Emsi 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.

Source: For occupations, a combination of detailed Industry Demographics, staffing patterns, and American Community Survey. For industries, Emsi’s proprietary employment data, incorporating Census’ Quarterly Workforce Indicators and American Community Survey.

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.

How Emsi Incorporates ACS

Emsi’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. Emsi 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).

Emsi’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. Emsi 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.

Emsi 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.

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.

The use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and real-time data.

The BEA publishes data primarily used in Emsi’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 Emsi data inlcude

• 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)

Source: BEA

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 Emsi include

• Quarterly Census of Employment and Wages (QCEW)
• Occupational Employment Statistics (OES)
• National Industry-Occupation Employment Matrix (NIOEM)

Source: BLS 

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.

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.

Emsi uses the Census of Governments’ State and Local Government Finances dataset to help estimate earnings for government jobs.

Source: Census Bureau 

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.

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. Emsi uses CIU to disaggregate county-level LAUS unemployment counts into those categories.

For more on how Emsi uses CIU, see the article describing the methodology for Emsi’s unemployment data.

The CIP-to-SOC mapping connects educational programs with target occupations, showing potential higher ed talent pipelines into occupations. Emsi’s CIP-to-SOC mapping is based on the National Center for Education Statistics’ CIP-to-SOC mapping. Emsi 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.

Source: NCES.

Commercial Real Estate.

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.

Emsi uses CFS in the Input-Output model to help determine the potential for movement of goods and services between regions.

Source: Bureau of Transportation Statistics 

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.

Emsi uses CCD to help generate employment estimates for public education industries.

Source: NCES

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.

See Wages.

The Emsi compensation model provides occupational wage data with skill and certification premiums. It combines percentile wage data provided from Emsi’s LMI-based government data with observations culled from job postings.

Sources
The compensation model combines wage data from two distinct sources. The backbone for all Emsi occupational wage data is the Bureau of Labor Statistics’ Occupational Employment Statistics (OES) 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, Emsi 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 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. Emsi 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 Emsi 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.

Source: NCES, IPEDS.

See Location Quotient.

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. Emsi utilizes this data heavily in the Input-Output model to model consumption on industries by national demographic by income type.

Emsi uses CEX’s annually updated tables, which are generally released in September.

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.

Emsi’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.

Example:

• 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.

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 Emsi with Cost of Living data as well as information on industry and occupation diversity (diversity of a region’s industry or occupation mix).

Source: Council for Community and Economic Research 

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. Emsi uses CBP to help unsuppress QCEW data and to help inform our employment data for employment not covered by QCEW.

Emsi 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, Emsi 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.

Emsi’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.

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. Emsi uses CES to inform employment estimates for some of the industries not covered by QCEW.

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 QCEW and OES, Emsi’s major sources for industry and occupation data, respectively. QCEW defines Emsi’s current year for industry and occupation job counts and industry earnings, and OES defines Emsi’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, Emsi’s current year for job counts and industry earnings catches up to the calendar year in the last quarter of the calendar year. OES releases in the spring of the year following; for instance, 2017 OES was released in May 2018. 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.

See also: Sales, How Do Demand and Sales Differ?

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.

Source: Department of Labor

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.

See also Initial, Indirect, and Induced Effects.

Source: Emsi’s model, incorporating data from the Bureau of Economic Analysis (BEA).

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.

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.

Source: Emsi’s model, incorporating data from the Bureau of Economic Analysis (BEA).

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 Organizations are organizations, whether public or private, dedicated to developing the economy of a region or nation.

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

In Emsi data, employed refers to any person who is currently paid as an employee or is self-employed. It is important to note that Emsi employment counts count jobs, not people.

The Bureau of Labor Statistics’ Employment Projections program produces many datasets covering various facets of the labor force. the EP tables are updated annually. Emsi uses several datasets from EP:

1. Occupational Separations and Openings is used to provide replacement rates for use in Emsi’s Openings calculation.
2. Educational attainment for workers 25 years and older by occupation shows, by occupation, the percent educational breakout of the workforce for the occupation. Emsi uses this data as-is in its Educational Attainment breakouts for individual occupations:
   
3. 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.
4. The National Industry-Occupation Employment Matrix (NIOEM) is used to adjust Emsi’s industry and staffing projections, and therefore informs Emsi’s occupation projections as well. For more information on how Emsi creates occupation data using industry employment and staffing patterns, see this article.

A statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.

Emsi’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.

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.

Source: NIST

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)

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.

Emsi 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 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.

Source: https://www.bea.gov/data/income-saving/gross-domestic-income

The BEA produces Gross Domestic Product estimates. GDP is the value of the goods and services produced in an economy. Emsi uses BEA’s GDP by State (GSP) data for benchmarking parts of the Input-Output model. GDP is updated annually.

Source: BEA

The BEA produces Gross Domestic Product estimates. GDP is the value of the goods and services produced in an economy. Emsi uses BEA’s GDP by State (GSP)   data for benchmarking parts of the Input-Output model. GSP is updated annually.

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.

Emsi 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.

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 Emsi calculates hires for occupations, see the methodology article.

Source: Quarterly Workforce Indicators (QWI) from the Census Bureau and Emsi’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.

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.

Source: usa.gov

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.

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.

Emsi earnings data is presented by place of work. Emsi 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.

Emsi projects employment data 10 years into the future. Industry projections are built from Emsi’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 Emsi’s industry projections methodology.

Sources: QCEW

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.

Emsi 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, Emsi 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.

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 Emsi’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 money 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.

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.

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:

• Origin Destination
• Residence Area Characteristics
• Work Area Characteristics

These three pieces together provide information on commuting patterns by 2-digit industry between census tracts.

Emsi uses them together to form the basis of Occupation by Residence data. Emsi also uses Workplace Area Characteristics (WAC) to help build the commuting data used in Emsi’s Input-Output model.

Source: Census Bureau 

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.

Source: O*NET Online

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.

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.

Emsi uses LAUS data to produce unemployment counts and labor force participation data. Each Emsi datarun includes the latest month of LAUS data that was available when the datarun was processed.

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:

• Origin Destination
• Residence Area Characteristics
• Work Area Characteristics

These three pieces together provide information on commuting patterns by 2-digit industry between census tracts.

Emsi uses them together to form the basis of Occupation by Residence data. Emsi also uses Workplace Area Characteristics (WAC) to help build the commuting data used in Emsi’s Input-Output model.

Source:  Census Bureau 

See SPI/LPI

Emsi’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. Emsi 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 Emsi data run would include 2011-2015 ACS data).

Source: The Census’s Median Household Income

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.

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.

Source: Census Bureau

A credential that reflects the mastery of knowledge and skills that is typically more narrow than traditional degrees, certificates, and certifications.

Emsi’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.

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. Emsi’s multipliers are developed in-house through our proprietary Input-Output model, which uses Emsi’s final unsuppressed industry data, gravitational flows, commuting patterns, and the BEA’s “make and use” tables, among other sources.

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.

Source: NCES 

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.

Emsi uses NIPA data in the creation of the Input-Output Model. Specifically, it provides initial estimate values for the national model.

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.

Emsi uses NIOEM to adjust its industry projections.

Click here to read more about the BLS’s NIOEM dataset.

The use of computers to understand human language.

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 Emsi 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.

For more information on how place of residence job counts data is derived, see this article. For more information on how place of work job counts data is derived, see this article.

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 Emsi’s self-employed (Class 3) and Extended Proprietor (Class 4) data.

Source: Census Bureau 

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:

• Base rate
• Commissions
• Cost of living allowances
• Deadheading pay
• Guaranteed pay
• Hazard pay
• Incentive pay
• Longevity pay
• Over-the-road pay
• Piece rates
• Portal-to-portal rates
• Production bonuses
• Tips

Occupation earnings do not include the following:

• Attendance bonuses
• Back pay
• Clothing allowances
• Discount
• Draw
• Holiday bonus
• Holiday premium pay
• Jury duty pay
• Meal and lodging payments
• Merchandise discounts
• Non-production bonuses
• On-call pay
• Overtime pay
• Perquisites
• Profit-sharing payments
• Relocation allowances
• Severance pay
• Shift differentials
• Stock bonuses
• Tool/equipment allowances
• Tuition repayment
• Uniform allowance
• Weekend premium pay
• Year-end bonuses

OES provides definitions for all the categories listed above.

A Word about Percentile Earnings

Various reports within Emsi’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 Emsi’s occupation percentile earnings aggregation can be found here.

Source: Emsi’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.

The Occupational Employment Statistics (OES) 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. OES 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 Emsi Incorporates OES

OES is our primary source of occupation data, but we compensate for OES’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, OES-based staffing patterns to the industry data to show the distribution of jobs by occupation.

Emsi gathers occupation earnings data from OES. We use unsuppression techniques to fill in missing values as appropriate, and also build a time series of OES data in order to present historical occupation earnings.

For a more detailed explanation of how Emsi incorporates OES data into occupational processes, see this article.

Strengths of OES

• OES has estimates for specific industries, including national industry-specific occupational employment and wage estimates.
• OES has estimates for individual states, including cross-industry occupational employment and wage estimates for individual states.
• OES has estimates for metropolitan and nonmetropolitan areas, which together cover the entire United States.

Weaknesses of OES

• OES 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, OES’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 OES 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 OES 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. Emsi’s occupation methodology (see article referenced above) is designed to counteract this weakness in OES data.

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. Emsi 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; Emsi monitors O*NET for updates and downloads new data as it becomes available.

Source: O*NET

Also called “Other non-industries”, this consists primarily of two non-industries that nonetheless capture genuine income-generating activity within the economy.

1. 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.
2. 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: Emsi’s Proprietary data, primarily from the Bureau of Economic Analysis (BEA)

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.

Source: Census Bureau

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. Emsi 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.

Emsi 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.

Emsi 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, 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.

Property income is one of the four components of Gross Regional Product (GRP). The other elements are earnings (or labor income), taxes on production & imports, and subsidies.

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.

Source: Census Bureau

The Purdue industry clusters were created through a joint project between Emsi 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 Emsi 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) is a dataset published by the Bureau of Labor Statistics (BLS). QCEW is the backbone of Emsi’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]).

Emsi produces a slightly modified form of the BLS QCEW dataset.

• Emsi provides estimates for suppressed data (roughly 60% of QCEW data points are suppressed). For more on the importance of unsuppression, see this article.
• Emsi alters the NAICS classification of public-sector employment to make it more compatible with other data sources. For more information, see this article.
• Emsi 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) 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 Emsi Incorporates QWI

Industry Demographics Data

Emsi 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.

Hires

Emsi 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, Emsi’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.

Because the BLS’s Quarterly Census of Employment and Wages (QCEW) dataset does not cover the railroad industry, Emsi 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.

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. Emsi’s Input-Output model goes above and beyond the usual I-O model in that Emsi 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 Emsi’s proprietary data and data from the Bureau of Economic Analysis (BEA).

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 Emsi’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 Emsi’s methodology for Openings.

Unlike the majority of Emsi 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, Emsi 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 Emsi 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 Emsi 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, Emsi 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 Emsi 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.

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. Emsi’s job postings are scraped from company websites and job boards and aggregators. For more about how Emsi scrapes job postings, see this article.

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 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 Emsi’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:

• Lightcast skills
• Skills Required (ebook) – How higher ed can meet the needs of learners and employers in a skill-based economy.
• Skillabi – A new way to evaluate and improve curriculum alignment with the job market, using the common language of skills.

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.

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).

Source: un.org

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.

The Standard Occupational Classification (SOC) 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 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 Emsi’s use of SOC codes (including departures from the standard classification), see this article.

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.

Emsi uses State and Local Government Finances data in the Input-Output model to aid in breaking out state and local data reported in the BEA’s Make and Use Tables.

The BEA’s State Personal Income (SPI) and Local Area Personal Income (LPI) datasets contain primarily earnings data, but also include employment estimates.

Emsi 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.

In Emsi data, Talent indicates the workforce currently available to employers. Data for Talent supply comes from Traditional LMI, job postings, and professional profiles.

Emsi’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.

The businesses in the selected industry with the most local employment according to DatabaseUSA, Emsi’s provider of business-level data. DatabaseUSA’s sources and methodology differ significantly from Emsi’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.

Source: DatabaseUSA

The total industry earnings for a region. Includes wages, salaries, supplements (additional employee benefits), and proprietor income.

Total Earnings is one of the four components of Gross Regional Product (GRP). The other elements are profits/property income, taxes on production & imports, and subsidies.

Source: Emsi’s model, incorporating data from the Bureau of Economic Analysis (BEA).

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.

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.

Source: BLS’ Education and training assignments by detailed occupation

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.

The Unclassified occupation (99-9999) is a special SOC code Emsi 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:

• Over-skilled
• Under-payed
• Low hour

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. 

Example

• 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.

Sources:

• Population educational attainment level by county (ACS data from the Census)
• Typical entry level education by occupation (BLS)

An estimate of total unemployed persons by industry or occupation in a region. Emsi 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 Emsi’s proprietary employment data.

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.

Source: Department of Labor

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.

Source: Department of Labor

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.

Emsi’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.

See Labor Force.

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.

Source: data.gov

ZIP Code Tabulation Areas are generalized areal representations of United States Postal Service (USPS) ZIP Code service areas. The USPS ZIP Codes identify the individual post office or metropolitan area delivery station associated with mailing addresses. USPS ZIP Codes are not areal features but a collection of mail delivery routes. The term ZCTA was created to differentiate between this entity and true USPS ZIP Codes. ZCTA is a trademark of the U.S. Census Bureau; ZIP Code is a trademark of the U.S. Postal Service.

Source: Census Bureau