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CIP (Classification of Instructional Programs) – 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.

Competency – A specific area of knowledge, skills, or abilities in the O*NET framework.  For example, a competency may be academic (mathematical knowledge), practical (mathematical program-solving skills), or physical/cognitive (number facility, mathematical reasoning).

Completions – The number of students in a given year that completed a specific course of study.

Earnings per Worker (Industry) – Total yearly earnings of a regional industry (wages, salaries, profits, benefits, and other compensation) divided by the number of jobs in the industry.

Earnings per Worker (Occupation) – By default, shown as median hourly earnings (wage/salary) of workers in that occupation.

Growth Period – A timeframe defined by a start and an end year.  In this report, published data is employed for each year in the growth period it is available.  For future years (and sometimes the year previous to the present) EMSI projections are used.

Industrial Mix Effect – See Shift Share.

Industry – A group of businesses that produce similar goods and services, and share similar production processes for creating the goods and services they sell.  Industries are classified using NAICS codes.  Note that in the NAICS system, what a business produces is given less importance than the process used to create it.

Industry Cluster – A group of industries closely connected by supply chains and/or similar labor pools.

Input-Output Models – A mathematical representation of the economic relationships among all industries, especially with reference to how much each industry purchases from each other industry.

Inverse Staffing Pattern – A table of percentages that shows, on average, how occupations are divided up among industries.  For example, a (simplified) inverse staffing pattern for registered nurses may show that 70% of RNs are employed by hospitals, 10% by local government (i.e., public schools), 10% by nursing homes, and 10% by offices of physicians.

Jobs – This column in the data represents full and part-time jobs, which can explain some situations in which the number of jobs in a region seems unusually high compared to the total regional population.  Unlike individual government sources, EMSI’s data includes jobs held by nearly all types of workers: payroll workers (who have unemployment insurance), farm workers, railroad workers, military workers, and sole proprietors.

Job Zone – A measure of how much education and/or training is required by an occupation, from Job Zone 1 (little or no preparation) to Job Zone 5 (extensive preparation needed).

Location Quotient (LQ) – A way of comparing a region to a larger reference region according to some characteristic or asset.  Suppose X is the amount of some asset in a region (e.g., manufacturing jobs), and Y is the total amount of assets of comparable types in the region (e.g., all jobs).  X/Y is then the regional concentration of that asset in the region.  If X’ and Y’ are similar data points for some larger reference region (like a state or nation), then the LQ or relative concentration of that asset in the region compared to the nation is (X/Y) / (X’/Y’). 

Multiplier – A number showing how changes (jobs, earnings, or sales) in one industry will propagate to other industries in a regional economy.  For example, a jobs multiplier of 3 for an industry means that a change of 100 jobs in that industry would lead to a total change of 3*100 = 300 jobs in the whole economy.  Note that this 300 includes the original 100 jobs, meaning the additional change is 200.

NAICS (North American Industry Classification System) – A system of classifying North American industries using six-digit codes.

O*NET – A national database quantifying the competencies (knowledge, skills, and abilities) required by over 800 occupation types.

Occupation – A worker job description that contains a defined set of tasks and responsibilities.  Occupations are classified using SOC codes (and O*NET extensions to SOC).

Occupational Compatibility Index – This number is intended to score the compatibility of two occupations in terms of the knowledge, skills, and abilities they require: a score of 100 means complete compatibility, while a score of 0 means no compatibility.  It is an artificial number created by EMSI’s proprietary algorithm and O*NET data.

OES (Occupational Employment Statistics) – A federal data source produced by the Bureau of Labor Statistics.

Program – A post-secondary course of study (or group of related courses) as defined by a standard CIP code.

Projection – A method of trending past population numbers by demographic group into the future.

Requirements (Industry or Cluster) – The dollar amount of goods or services that one industry or industry cluster needs to purchase from another industry.

Replacement Jobs – The number of job openings (over a given timeframe) expected in an occupation as the result of turnover – e.g., employees changing occupations, retiring, etc.  It is derived by multiplying estimated annual turnover by the number of years in the given timeframe.

Shift Share – A standard method of regional economic analysis that attempts to separate regional job growth into its component causes.  The three main causes identified are the “national growth effect,” which is regional growth that can be attributed to the overall growth of the entire U.S. economy; the “industrial mix effect,” which is regional growth that can be attributed to positive trends in the specific industry or occupation at a national level; and the “regional competitiveness effect,” which is growth that cannot be explained by either overall or industry/occupation-specific trends.  Shift share can be applied to job decline as well as job growth.

SOC (Standard Occupation Classification) – The system of classifying occupations that uses six-digit codes.

Staffing Pattern – A way of showing what percentage of jobs in a given industry are in a specific occupation.  For example, a (simplified) staffing pattern for the industry “Hospitals” might show that 10% of jobs in the hospital industry are occupied by surgeons, 15% by general practitioners, 20% by nurses, 5 % by information technology support staff, 5% by janitors, 1% by chief executives, and so on.  We use regionalized staffing patterns that are available by OES region.

Source: Economic Modeling Specialists Inc., 2007.