Glossary

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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
American Community Survey (ACS)
The American Community Survey (ACS) is a nationwide survey designed to provide communities a fresh look at how they are changing. It is a critical element in the Census Bureau's decennial census program. The ACS…

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.

The ACS collects and produces population and housing information 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 our demographic data for the Non-QCEW Employees, Self-Employed, and Extended Proprietors Classes of Worker.

In addition, Emsi uses ACS to build national staffing patterns for the Self-Employed and Extended Proprietor Classes, and for the Employee Classes for some industries that the BLS’s OES dataset does not cover.

Strengths of ACS

  • ACS has a relatively short lag time; data is collected monthly and released within 12 months of the survey date.
  • 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) 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.

Classification of Instructional Programs (CIP)
A standard numerical code for a post-secondary course of study, developed and defined by the U.S. Department of Education’s National Center for Education Statistics. The classification of instructional programs provides a taxonomic scheme that supports…

A standard numerical code for a post-secondary course of study, developed and defined by the U.S. Department of Education’s National Center for Education Statistics. The classification of instructional programs provides a taxonomic scheme that supports the accurate tracking and reporting of fields of study and program completions activity.

Source: NCES

Employed in Field
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…

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.

Estimated Annual Wage
The emp_wageAgeAdjMax column shows the median wage for each profile's SOC code in the county that best describes the profile's location, adjusted for the individual's age and highest level of education completed at your institution.…

The emp_wageAgeAdjMax column shows the median wage for each profile’s SOC code in the county that best describes the profile’s location, adjusted for the individual’s age and highest level of education completed at your institution. Occupation earnings data comes from the BLS’s OES dataset, adjusted to take into account QCEW and ACS.

Age-adjusted Median Wage estimates current annual earnings using the median wage for the graduate’s most recent occupation and county and adjusting for age and degree level using the Mincer function.

Graduate
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.

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.

Highest Award Checkbox
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…

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.

Job Started After Grad Year Checkbox
When selected, the Job Started After Grad Year checkbox will limit report results to profiles whose most recent job started after the year of graduation. This enables analysis of employment outcomes for selected groupings and…

When selected, the Job Started After Grad Year checkbox will limit report results to profiles whose most recent job started 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 on or before the year of graduation or did not include a job start year.

Match
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…

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.

Multiple Awards
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…

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.”

Normalization
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…

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”.

North American Industry Classification System (NAICS)
The North American Industry Classification System (NAICS) is the standard federal system for classifying business establishments. Each establishment is assigned a six-digit code and category title, organizing them primarily by similar production processes into five…

The North American Industry Classification System (NAICS) is the standard federal system for classifying business establishments. Each establishment is assigned a six-digit code and category title, organizing them primarily by similar production processes into five levels: sectors, subsectors, industry groups, industries, and national industries (national industries are specific to one or more of the United States, Canada, and Mexico). Codes are hierarchical: less detailed categories are derived by removing digits from the end of more detailed codes.

Example

  • 23: Construction (sector)
  • 236: Construction of Buildings (subsector)
  • 2362: Nonresidential Building Construction (Industry Group)
  • 23622: Commercial and Institutional Building Construction (industry)
  • 236220: Commercial and Institutional Building Construction (national industry which in this case is identical to its parent industry)

The NAICS classification is updated every five years to better reflect economic realities.

For information on Emsi’s use of NAICS codes (including departures from the standard classification), see this article.

See also: http://www.bls.gov/bls/naics.htm or http://www.census.gov/eos/www/naics/

Occupation Earnings
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…

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.

Occupational Employment Statistics (OES)
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…

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 1.2 million establishments and 62% 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.
Occupational Information Network (O*NET)
O*NET provides occupation data such as knowledge, skills, and abilities needed to perform the work, as well as education and training requirements and alternate job titles. Emsi incorporates this data throughout its tools in various…

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

Potential Annual Wage
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…

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.

Profiles
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…

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)
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,…

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 (national, 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.
Reside in Region
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…

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.

Standard Occupation Classification (SOC)
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…

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 775 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 23 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 2010, and the update to the 2018 classification is currently happening across various government LMI datasets.

For more information on Emsi’s use of SOC codes (including departures from the standard classification), see this article.