Glossary (Alumni Outcomes)

Emsi compares the SOC code of a profile’s most recent job to a custom CIP-SOC mapping to determine whether the job is in or out of the field of study indicated by the CIP code your institution provided.

Each profile has an Estimated Wage which shows the highest result of three calculated wages:

1. The median wage for each profile’s SOC code in the profile’s county, adjusted for the individual’s age and highest level of education completed at your institution using the Mincer function
2. The tenth percentile wage for that occupation in that county
3. The federal minimum wage

Emsi Burning Glass then averages the wages of all profiles in your selection. All wage data is from Emsi Burning Glass’s county-level labor market data, which is built from OES, adjusted by QCEW, ACS and other federal and state sources.

AO reports use “graduate” to refer to a degree awarded rather than an individual student. Students may graduate with multiple awards (e.g. “double majors”), which are linked via student ID fields.

When selected, the Highest Award checkbox will limit report results to one award per student. This enables school wide analysis by headcount and is selected by default. Deselect the checkbox to analyze all members of a group if it may include students who earned other awards at your institution. 

See this article for more.

When selected, the Job Started After Grad Year checkbox will limit report results to profiles whose most recent job started during or after the year of graduation. This enables analysis of employment outcomes for selected groupings and is selected by default. Deselect the checkbox to include profiles whose most recent job started  before the year of graduation or did not include a job start year.

To count as a matched record, a profile has to match an institution’s past student information on name and at least one of the following: contact information or award information (such as graduation year, program name, etc.). Using data obtained from public profiles, Emsi’s deliverables will show the most recent job listed for matched records.

Files include one row per award (major) per student. The record that contains the highest degree for any particular student will be marked “Highest,” and any other record(s) tied to that particular student will be marked “Other.”

Emsi classifies various profile field values such as company and school into a smaller number of fixed categories to enable meaningful aggregation and analysis. We call this process “normalization.” An example of normalization would be to normalize free-form variations of “St. Louis, Missouri” as found in different profiles to “St. Louis. MO”. One person might list their location as “Saint Louis Missouri”; another might list “ST Louis MO”; and a third might list “St. Louis Missouri”. Normalization corrects all variations of this city name to “St. Louis, MO”. Without the normalization step, aggregate analysis of profiles would be impossible–searching for profiles in “St. Louis Missouri” would automatically exclude profiles where the person wrote their location as “Saint Louis Missouri”.

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.

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

The emp_wageAvg column shows the average wage for each profile’s SOC code in the county that best describes the profile’s location. Occupation earnings data comes from the BLS’s OES dataset, adjusted to take into account QCEW and ACS.

Emsi Profile Analytics is built from individual profiles of over a hundred million workers in the United States. Typical fields available are city/state/nation of residence, job history, education history, and skills. Many profiles also contain names, phone numbers, and email addresses, but these are not made available in bulk to Emsi users.

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.

Emsi uses the list of counties or states that defines your service region (as specified by your institution) to determine if a matched profile currently resides in or out of your region. *Note: Filtering a report by geographies outside of this service region will always result in 0 alumni in region.

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”).

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.