Occupation Data Details


IMPLAN now includes data for occupation employment, wage, and core competency (knowledge, skills, abilities, education, work experience, and on-the-job training levels for each occupation). This new data offering allows for expanding employment impact results to include occupation detail (with associated wage, education, and skill detail) in addition to Industry detail.  These data are also applied to the IMPLAN study area data, providing important insights into a Region’s existing skill force, the skill requirements of various industries, and more. 

This article describes the methods and sources the IMPLAN Data Team uses to estimate these occupation and core competency data.



There are four main sources for the IMPLAN Occupation Data. The most recent data years for each are utilized. 

  • Bureau of Labor Statistics (BLS) Occupational Employment Survey (OES)
  • BLS Employment Projections national industry by occupation matrix
  • Census Bureau American Community Survey (ACS) Public Use Microdata Sample (PUMS)
  • O*NET™ is a trademark of the U.S. Department of Labor, Employment and Training Administration
    •  www.onetonline.org
    • Provides data on knowledge, skills, abilities, education, work experience, and on-the-job training by occupation.
    • A new database version is released whenever updates are made.

For all sources, IMPLAN uses the most recent data release as of the time the occupation data generation process begins.

Occupation Data Year












It is important to note that IMPLAN reports occupation data only for the Wage and Salary Employment component of Total Employment, and therefore only for the Employee Compensation component of Labor Income.  Note that proprietors are excluded from occupation-related IMPLAN data.  This maintains consistency with Occupational Employment Statistics (OES) coverage only of “employees.”

OES data on occupation employment and wages by industry generally are available on an annual basis.  IMPLAN relies most heavily on this dataset for occupation employment and compensation. The raw OES data report employment and wages by BLS SOC code, which has a hierarchy similar to North American Industrial Classification System (NAICS) codes.  The SOC hierarchy occurs in 4 levels: major, minor, broad, and detail.  OES data sometimes omit a level in the SOC hierarchy, e.g., reporting positive values at the detail level and its broad parent level, but nothing at the corresponding parent minor level.  OES data also contain suppressions due to non-disclosure rules, with the result that a broad level code with 10 employees might report only one child detail code with 7 employees, leaving no information about the occupations of the remaining 3 employees.  

When estimating occupation employment by industry, IMPLAN estimates values where that value would be non-disclosed in the OES data and ensures consistency among levels. That is, if 2 detail codes contain 5 employees each, the corresponding broad code will have 10 employees.  Sometimes this requires overwriting published values that are inconsistent with other published values while ensuring that the IMPLAN data maintain as much consistency with the raw data as possible. IMPLAN’s method of disclosing missing values entails using data from aggregations at a higher NAICS or SOC level, as well as controlling lower-level values to known higher-level aggregates. Wage values are treated similarly to employment values in the disclosure process. 

IMPLAN matches the disclosed and consistent occupation by industry employment data to IMPLAN Industries. In some cases, OES does not provide any coverage of an industry, such as agriculture and private households. In this case, IMPLAN supplements the OES data with data from the BLS Employment Projections, which also provide data on occupation employment by industry, but not on occupation wages by industry. Wage data from OES are substituted in this case. IMPLAN uses other BLS data to estimate military occupation employment, and deviates from the SOC system for coding military occupations.   Occasionally, OES provides detail only to a NAICS level that encompasses more than one IMPLAN Industry. In such cases, IMPLAN applies the distribution of occupation employment for that NAICS code to all constituent IMPLAN Industries. Additionally, there are cases in which IMPLAN refines initial occupation estimates for an IMPLAN Industry that uses occupation data for an aggregate NAICS by accounting for a sibling industry that uses occupation data for a more detailed level of the same aggregate NAICS.  

IMPLAN uses the occupation hours data from PUMS to adjust its estimates of occupation wages. The OES data on wages by occupation assume that each occupation works 2,080 hours per year (40 hours * 52 weeks), except in some cases in which it provides only an hourly wage. If IMPLAN followed that assumption, it might attribute too much compensation to occupations that tend to work fewer than 2,080 hours per year.  For example, consider a hypothetical restaurant industry that has only one business. That restaurant has one waiter position, which is hired all year long but only for 20 hours per week and is paid $10 per hour. The waiter position should not be assumed to earn $10 * 2,080 = $20,800 per year; rather, the waiter would earn $10 * 2,080 * (20 / 40) = $10,400 per year. If this business also employs a restaurant manager, who works 40 hours per week and earns $20 per hour, the manager earns a total of $41,600. Properly accounting for hours worked means that the manager earns 80% of the total compensation paid by the business and in the industry, with the waiter earning the remaining 20%. Failing to account for hours worked gives the manager 67% and the waiter 33%.




Developed by Philip Watson, Ph.D.

Each occupation in IMPLAN is deconstructed into their constituent Knowledge, Skill, and Ability (KSA) elements as well as education levels, work experience, and on-the-job training using Bureau of Labor Statistics (BLS) data and O*NET™ data. The KSAs along with education, experience, and training are referred to as the “core competencies.”

O*NET™ data report 33 unique knowledge elements, 35 unique skill elements, 52 unique ability elements, 12 unique educational attainment elements, 11 unique work experience levels, and 9 unique on-the-job training levels.  Descriptions of these core competency elements can be found in the Core Competencies spreadsheet.

In order to generate workforce reports, IMPLAN occupation data must be bridged to the core competencies. However, neither O*NET™ databases nor the BLS provides a bridge between occupations and core competencies; rather, they report values on two scales for each occupation and KSA combination. The scales that are reported are a 0-5 measure of “importance” and a 0-7 measure of “level.”  Combining these two scales into a bridge of occupation employment to measures of KSA endowments requires some assumptions and empirical calculations.  The assumptions used, while not intended to be regarded as the only set of assumptions that could be used to generate the bridges, were empirically tested to ensure that the resulting bridges accurately predicted occupation wages and presented a reasonable measure of the KSAs associated with each occupation.


The first assumption used in generating the bridges is that the two KSA scales would be combined using a multiplicative interaction between the scales. The use of a multiplicative relationship rather than an additive interaction deemphasizes very small values for either the level or the importance and creates a higher weight for KSA elements that have higher values for both the level and the importance. Alternative interactions were also explored, including additive, arithmetic means, and geometric means, but the multiplicative interaction was found to be the best across multiple empirical tests. 

The second assumption is that core competency endowments are associated with the occupations to which people are employed (or potentially unemployed) in a region rather than directly to the people themselves. Therefore when a person moves from one job to another within a region, the core competencies in the region change even though the same people reside and work there as before. More technically, the core competencies generated here are a measure of the expected human capital endowments in the region given the current occupation mix of employment in the region. This assumption relates directly to the third assumption below.

The third assumption in generating the bridge is that every occupation uses the same absolute amount of KSAs and, therefore, the differences between the KSAs associated with different occupations are in their distribution, not their level. This assumption is useful due to the incompatible units across KSA elements. The result of this assumption is that every occupation can be thought of as using 100 units of knowledge elements, 100 units of skill elements, and 100 units of ability elements in varying distributions. When a worker moves from one job to another, the worker’s mix of KSAs change, but the overall amount of total KSAs which the worker possess does not change. Likewise, under this assumption education and training do not necessarily increase the absolute level of KSAs in a region; rather, they simply enable people to move from one occupation to a different occupation with a different (and presumably more valuable) set of associated KSAs.  

The chart below presents a numerical example of calculating units of knowledge elements from the O*NET data. The example is simplified, as O*NET includes many more knowledge elements for the economist occupation.  According to this example, if an economy contains only one job, and that one job is held by an economist, the economy would have 100 units of knowledge, allocated among the three types listed below. The calculation is the same for other occupations and for other KSA types.



The other core competencies outside of the KSAs were more straightforward in their bridging to occupation data. Education, work experience, and on-the-job-training data from the O*NET™ database are reported as proportions of persons employed in a given occupation that each have a given level of education or training, respectively. O*NET™ data report 12 levels of educational attainment, 11 levels of work experience, and 9 on-the-job-training categories. Since they are already reported as proportions, the bridge to IMPLAN occupation data is direct and does not require any additional assumptions.  The education, work experience, and training categories can also be found in the Core Competencies spreadsheet. However, similar to the KSAs, the education and training elements are occupation-based and not individual-based; therefore, they are not to be thought of as the direct endowments of the individuals in a region, but rather as the expected endowments relative to the occupation employment in the region. 

While O*NET data covers the majority of OES occupations, there are occupations in the O*NET database that lack data. For example, the “all other” occupations (SOCs ending in 9: xx-xxx9) often lack O*NET data. In such cases, core competency data for the occupation is estimated using the core competency estimates of sibling level occupations weighted by each competency’s survey response size as provided by O*NET. This approach assumes that the “all other” occupation reflects the competency emphasis of the sibling occupations. Additionally, military occupations were estimated using similar means in order to bridge O*NET data to IMPLAN’s custom military occupation codes.

Given the data and assumptions described above, IMPLAN can estimate endowments of the respective core competencies which will sum to 100 times the same total as the occupation employment total in the region. For example, if there are 1,000 occupation jobs in a given region, then there will be 1,000*100 occupation equivalents of knowledge, 1,000*100 occupation equivalents of skills, and 1,000*100 occupation equivalents of abilities.  These occupation equivalents will vary widely by region based on the industrial and occupation employment mix in the respective region.



Core Competencies

Occupation SOC Codes



Occupation Data Details

Using Occupation Data in IMPLAN

Occupation Data Use Cases 

Occupation Data – Behind the i

Calculating Leakages of Direct Intermediate Inputs


The data Behind the “i” is helpful for better understanding the Results produced in your analysis.


To calculate the leakages due to spending on Intermediate Inputs outside of the Study Region click on the “i” icon next to the Selected Region and navigate to:

Social Accounts

          > Balance Sheets

                       > Industry Balance Sheet

                                      > Commodity Demand


In the Commodity Demand table, you’ll find the following columns (be sure to filter by your Industry of interest):

Gross Absorption = the proportion of Total Industry Output for this industry that goes toward purchases of each commodity. Gross Absorption is calculated as Gross Inputs/Total Industry Output. Total Gross Absorptions will be less than one, with the remainder of Total Industry Output going toward Value-Added.

Regional Absorption = the proportion of Total Industry Output for this industry that goes toward local purchases of each commodity. Regional Absorption can be calculated as Gross Absorption * RPC

Therefore, Total Gross Absorption – Total Regional Absorption = percentage of Total Industry Output that is spent on Intermediate Inputs outside of the Region. This percentage multiplied by Total Industry Output are the Intermediate Input dollars leaked out from the Direct Effect. 


Take for example “Industry 56 – Construction of other new nonresidential structures” in Pennsylvania 2018. The Total Gross Absorption is 52.057% and the Total Regional Absorption is 31.671%.  This means about 52% of Output is allocated to Intermediate Inputs overall, and about 32% of Output is allocated to Intermediate Inputs in the Region. Therefore, about 20% of Output is allocated to Intermediate Inputs outside of the Region. 

% of Total Industry Output spent on non-local Intermediate Inputs =  52.057% – 31.671% = 20.386%

When analyzing $1M of new Output in this Industry in PA, IMPLAN would estimate $203,860 of the $1M of new production as being spent on non-local Intermediate Inputs. The remaining ‭$796,140‬ includes local Intermediate Inputs ($316,710) and Value Added ($479,430). 


Social Accounts

Why don’t my Direct Effects match my Direct inputs?


We get this question a lot at IMPLAN. You run an analysis of $5M and your Results only show $4.8M in Direct Output. Where did the other $200,000 go?

There are seven reasons that these numbers won’t match. Let’s walk through them.




If you are modeling a list of Industries, it is possible that one of them doesn’t exist in your Region. If it doesn’t exist in the IMPLAN data, there will be no effect from that Industry. If you know that the Industry does now operate in your Region, you can add it by Customizing your Region.



In order to see the exact number that you used on the Impacts screen in your Direct Effects, you will need to ensure that your Dollar Year matches on both screens.  For example, if you analyzed your Events using 2018 Dollar Year, filter your Results for 2018 Dollar Year. If different years are used, you will not see exact matches between the Impacts screen and the Results screen. Check out the article on Dollar Year & Data Year for more details.



The only Event Types that will give you a Direct Effect are Industry Events, Industry Contribution Events, Commodity Output Events, and Institutional Spending Pattern Events. Direct Effects are not a part of Labor Income Events, Household Income Events, or Industry Spending Pattern Events. More details are in the article Explaining Event Types.



A Margin is the value of the transportation, wholesale, and retail trade services provided in delivering Commodities from the factory floor to buyers. Margins are calculated as sales receipts less the cost of the goods sold. They consist of the trade Margin plus sales taxes and excise taxes that are collected by the trade establishment. 

Most Input-Output models, including IMPLAN, record expenditures in producer prices (known as Marginal Revenue). This allocates expenditures to the Industries that produce the goods or services. Any Output or sales you want to apply to multipliers that are in purchaser prices (prices paid by final consumers) need to be converted from purchaser price (Total Revenue) to producer prices (Marginal Revenue) or allocated to the producing Industries. Margins enable the move from producer to purchaser prices or vice-versa. 

IMPLAN values are based on the actual costs of producing the product or service being sold. Margins are necessary whenever an item is purchased from a retailer or wholesaler. Margins can be applied to retail and wholesale Industry Events and Commodity Events. 

When margins are applied, you will not see the full Value from your Impacts screen in your Results. The portion that you do see in the Results is the margin coefficient for retail or wholesale Industry. Details can be found in these two articles: Retail and Wholesale: Industry Margins and Retail and Wholesale: Commodity Margins.



The Local Purchase Percentage (LPP) in Commodity Output Events and Spending Pattern Events is by default set to 100%, but this can be edited via the Advanced Menu. Remember, the LPP indicates to the software how much the Event impact affects the local Region and should therefore be applied to the Multipliers.  If the LPP is set to anything less than 100%, you won’t see your inputs match the Results. Learn more in the article Local Purchase Percentage (LPP) & Regional Purchase Coefficients (RPC).



The portion of Commodity supply coming from each source for a given Commodity is called a Market Share. If you are analyzing Commodities, some of the Market Share can come from Institutional Sales (like out of inventory or produced by the government). When LPP is less than 100%, the remaining portion (or 1-LPP) is then assumed to be affecting a different Region. The portion happening outside the Region of your analysis does not create any local effect. 

Because Commodity Market Shares allocated to Institutions will be treated as leakages, these portions of Commodity Output will not be included in the Direct Effect of the Results. Check out where to find the Market Shares in the article on Social Accounts.



When a Commodity Event is run with the same Dollar Year and Data Year and the Results are viewed in that same Dollar Year, the Direct Effect will match the Direct Commodity input. However, when the Dollar Year on the Impacts screen or the Dollar Year on the Results screen do not match the Data Year, the Direct Effect will be slightly different than the Direct inputs. This is because the Commodity Event is adjusted using Commodity deflators/inflators and then those dollars are deflated/inflated on the Results using Industry deflators/inflators. The slight difference in the Results you see in the Direct Effect is due to the differences between Commodity and Industry deflators/inflators.

Tax Impact Report FAQ

1. Why am I seeing negative taxes?

We have an entire article dedicated to this entitled The Curious Case of the Negative Tax: Agriculture Subsidies, Profit Losses, and Government Assistance Programs.

2. Does IMPLAN capture Transfer Taxes, and where are those taxes represented?

IMPLAN does capture Transfer Taxes. They are reported as Taxes on Production & Imports (TOPI) – Sales Tax.

3. All payments to government (other than payroll taxes and end-of-year corporate income taxes) are paid through TOPI.

    • TOPI includes all payments to governments other than payroll and end of year income/profit taxes. TOPI includes excise, sales, and property taxes, fees and fines, and licenses and permits. The sector that collects the sales taxes (retail, lodging, restaurants, etc.) turns the collected money over to government through their TOPI.
    • Payroll taxes include social security, Medicare, unemployment insurance, etc. They include both the employer-paid and employee-paid portions and show up in the SAM as payments from the Employee Compensation column.
    • End-of-year taxes corporate income are paid via the Enterprises (Corporations) column. End-of-year taxes are like an income tax for corporations and is paid out of retained corporate earnings.
    • Property taxes are paid through Sector 361 (HH’s make a payment to sector 361 through their PCEs). So an increase in income run through the HH institution would result in an increase paid to property taxes.

The tax impact report splits the TOPI tax impacts into the various categories based on the picture of that region’s economy. We do not have industry-specific taxes paid (other than total TOPI, which is industry-specific) so the distribution will be an all industry average.

4. Does IMPLAN capture Mortgage Recording Taxes, and where are those taxes represented?

IMPLAN does capture Mortgage Recording Taxes. They are reported as Taxes on Production & Imports (TOPI) – Other Taxes.

Income FAQ

1. Why is the Average Household Income in my Model Overview so High?

In IMPLAN, we base household income on the Bureau of Economic Analysis (BEA)’s “Personal Income” numbers controlled to current BEA National Income and Product Accounts (NIPA) for the nation. In contrast, per capita household income reported by the Bureau of the Census is “Money Income” based. Due to a number of data source differences, definitional differences, and variances in scope and purpose the numbers reported in these two data sources vary significantly. For more information about these differences please explore this article

2. Why are Oil & Gas extraction sector proprietors appearing in a study area that does not in reality contain oil wells?

This is due to how Proprietors are accounted for in the IMPLAN system. Unlike the place-of-work Wage and Salary data used by IMPLAN to account for employees, the Proprietor Employment data are place-of-residence based. That is, a well-owner who lives in NJ but whose well is in another state will show up in the BEA data (and subsequently, in the IMPLAN data) as a proprietor in O&G extraction sector in the NJ data set. That proprietor is then allocated a certain proportion of the U.S. O&G extraction output, since the output data are reported at the U.S. level only.

In addition, the BEA considers ownership by partnership a proprietor. Therefore, it is possible to have many partners in oil and gas in a county in which there is no oil and gas.

3. Why are there different definitions for GRP?

Gross Regional Product describes the wealth in a region and is a common measurement of economic stability and growth. GRP can be measured on an expenditure basis (Final Demand) or on an income basis (Value Added); thus the Model Overview screen provides a breakdown of GRP looking at both measurements.

  • Final Demand describes the value of goods & services produced and sold to final users during the calendar year. These final users would include governments, households, exports (net), and investments (Capital).
  • Value Added describes how income is distributed to these same Institutions or final users.

Looking at the Model Overview

Viewing the Model Overview we can see that just because the method of measurement differs, since both methods measure GRP, the resultant value is the same (some variance will occur after the first seven significant digits due to rounding).



Value Added Final Demand
Employee Compensation: The entire cost of employees including wages and salaries payroll taxes and benefits: sometimes referred to as fully-loaded wages or income. This value is a primary source for households and a source of monies for governments (in the form of payroll taxes) for final demand purchases. Households: Households make payments to industries for goods and services used for personal consumption (PCE) and to governments in the form of taxes, fees, fines, etc. This is the largest component of final demand and is derived from household income in the region as a result of Employment Compensation, Proprietor Income and Other Property Type Income payments to households, as well as governments and payments from other households. Thus payments from these Value Added categories provide the basis of household consumption in final demand.
Proprietor Income: Income for sole proprietors and partnerships that drive household income for final demand and tax payments via income taxes for governments. State & Local Governments: Public education purchases are for K-12 and higher education institutions. Non-education purchases are for all other state and local government administration activities including police protection and sanitation. Funds for these purchases result household income as well as corporate taxes captured from Employment Compensation (payroll taxes and income taxes), Proprietor Income (income taxes), Other Property Type Income (income taxes) and Taxes on Production & Imports (fees, fines, sales taxes, licenses, etc).
Other Property Income: Income derived from dividends, royalties, corporate profits, payments for rent, and interest income. Thus Other Property Type Income provides a source of income for households, business, and govenments. Federal Government: Federal defense includes spending by all agencies in the Department of Defense. Non-defense purchases are made to supply all other Federal government administrative functions. Federal Investment consists of all Federal government demand for capital goods. Funds for these purchases result fromhousehold income as well as corporate taxes captured from Employment Compensation (payroll taxes and income taxes), Proprietor Income (income taxes), Other Property Type Income (income taxes) and Taxes on Production & Imports (fees, fines, sales taxes, licenses, etc).
Taxes on Production & Imports: Sales and excise taxes, customs duties, property taxes, motor vehicle licenses, severance taxes, other taxes, and special assessments. Subsidies are netted out and thus can be negative for some industries in some years. Thus, this is primary source for income for governments for final demand. Capital: Household savings and private industry purchases of capital equipment and construction, driven by corporate profits captured in the Other Property Type Income component of Value Added.
  Exports: Goods and services produced within the geography of the Model sold to both domestic and foreign buyers. These exogenous purchases by industries and households in other regions provide income (Employment Compensation, Proprietor Income and Other Property Income) to local households and corporations and to governments as taxes (Taxes on Production & Imports, Other Property Income and payroll taxes).
  Imports: Purchases of goods and services by households, governments and industries from outside the region that represent a loss of income to the Model geography. Imports are wealth leaked to other regions.
  Institutional Sales: Sales of goods and services by Institutions.  These are subtracted from the other components of Final Demand.

In both cases the total wealth in the economic geography is identical. On the Value Added side we see how Industries contribute to that growth through production, and on the Final Demand side we view how consumption drives local industries to produce products for local demand.

Read More

Employment FAQ

 1. How is IMPLAN Employment defined? 

Employment data in IMPLAN follows the same definition as Bureau of Economic Analysis Regional Economic Accounts (BEA REA) and Bureau of Labor Statistics Census of Employment and Wages (BLS CEW) data, which is full-time/part-time annual average. Thus, 1 job lasting 12 months = 2 jobs lasting 6 months each = 3 jobs lasting 4 months each. A job can be either full-time or part-time.  Similarly, a job that lasts one quarter of the year would be 0.25 jobs.  Note that a person can hold more than one job, so the job count is not necessarily the same as the count of employed persons.

Thus, while IMPLAN employment adjusts for seasonality, it does not indicate the number of hours worked per day. Thus, if you are using a full-time equivalent (FTE) value to add into IMPLAN results or as the proxy for an Event you will want to convert the FTE value to IMPLAN jobs prior to using it with IMPLAN. Conversely, if you need to report FTEs you will want to convert the IMPLAN jobs to reflect those. FTE and wage and salary to Employment Compensation conversions can be found in our Downloads section. Just choose the sectoring scheme appropriate for you data and download the related file. Whichever way you are converting, please keep in mind that FTE jobs are always fewer in number than the equivalent Part-time/Full time jobs.

The BEA calculates the number of FTE employees in each industry as follows:
FTE employees = (total number of employees) * [(average weekly hours per employee for all employees) / (average weekly hours per employee on full-time schedules)]
As for their determination on the number of hours for defining “employees on full-time schedules”, the BEA uses BLS as a source and adopts their definition of full-time which is accounted for as anyone working 35 hours or more.

The output per worker relationships are based on the average annual job. So if a worker works 6 months, they have half the annual output and that worker will need to be entered as 0.5 jobs. To adjust the seasonal employment, take the job count times the number of months worked divided by 12. In equation form:
IMPLAN Jobs = Seasonal Jobs * [(months of seasonal job)/12]
Seasonal Jobs = IMPLAN Jobs / [(months of seasonal job)/12]

To keep in mind:

  1. If an industry is dominated by part-time workers this will be reflected in the earnings per worker.
  2. Employment itself is merely descriptive in the sense that it does not drive the Indirect or Indirect Effects.
  3. The Total Employment figure in the Model Overview screen represents full and part-time annual average including the self-employed, all federal, state, and local government employment and military employment (including overseas military).

2. Why does the Employment count in the Study Area Data differ from what is reported in other data sources? 

IMPLAN jobs include workers that are not accounted for by a number of other data sources. This often means that IMPLAN jobs are larger than many other sources report.

Learn more from our Knowledge Base:

Comparison of IMPLAN source data for Employment and Labor Income

Datasets used to create IMPLAN Employment data

Special Sectors for Employment data

Estimating non-disclosures when creating Employment and Labor Income databases

What if your reported Employment value is actually smaller than the reported value from another data source? This can happen because of BEA’s rules for redefinitions. Following these rules we redefine some reported Employment, income and production to other Sectors. If your IMPLAN Study Area Data shows less Employment than another report, feel free to post the region, data year and Sector on our community section and we can dig a little deeper to see if redefinitions are involved.

3. What are the differences between Employment Multipliers and Employment Effects? 

All Multiplier derivations are based off of Output. Knowing regional total Output and regional total Employment we can create Output per Worker relationships from which we can estimate the number of employees needed per million dollars of Output.

In the Employment Multiplier sheet, Direct, Indirect, and Induced are actual jobs/ million dollars of production.

The resulting Type I and Type SAM Multipliers are derived as follows:

Type 1 (Direct Employment + Indirect Employment/ Direct Employment)

Type SAM (Direct Employment + Indirect Employment+ Induced Employment/ Direct Employment)

4. How does IMPLAN verify the total number of jobs created for modeled Events? 

It is important to remember that IMPLAN jobs are not FTEs. Instead, IMPLAN follows the BEA job definitions, which include full-time, part-time, and seasonal jobs. Additionally, verification of the Direct Effect should be relatively easy based on the economic change defined. Generally speaking, we feel that, unless there are large numbers of jobs reported in the Indirect and Induced Effects, that these impacts are largely supported rather than created. This can be demonstrated by looking at the Detail Results of your impact and also by comparing the jobs associated to the impact to the current Employment in the impact Sectors. Unless the change of Employment in the Indirect and Induced Effects are significant in comparison to the current Employment in the Sector, we recommend considering it supported rather than created. In addition, many attempts have been made over the years to verify Indirect and Induced jobs, and this has proved very difficult to actually discern.

The “536 FTE & Employment Compensation Conversion Table” allows you to convert between IMPLAN jobs and FTEs or FTEs and IMPLAN jobs with simple rations for each Industry for the 536 Sectoring scheme. Using another year? Check out the Downloads section to find the Sectoring scheme you are working with and download its FTE and Employment Compensation conversion table.

5. The jobs associated to my short-term impact analysis seem too small. What went wrong? 

IMPLAN is an annual Model; therefore the Model will assume that the value being entered as Industry Sales represents production over a year’s time. Therefore the Employment estimates provided by the software represent annualized Employment values. When you shrink the quantity of production into a smaller time frame more jobs will be necessitated for the same level of production to occur. Unfortunately there is no ‘best’ way to adjust for this, and the analyst must use their personal knowledge of the region and Event to account for the changes they want to make to the Employment count. There is no preferred method because IMPLAN has a fixed Output per Worker ratio and therefore cannot adjust for the possibility that a single worker may be able to do more if there is sufficient demand for them to do so. It also cannot account for temporary shifts in workforce resulting from short-term events such as the movement of a part-time job to a full-time job for the period of increased production/demand. 

6. How should jobs associated to multi-year construction impacts be reported? 

We recommend that you divide the impact over the number of years of the project and report the average jobs per year.

For example, if a construction site generates 85 jobs across 3 years, then the report would state the supported jobs as 85/3 or 28 jobs per year. This is because the jobs on the construction site are not cumulative, in the same way that an employee working a job for 3 years is not viewed as 3 jobs.

We also recommend considering construction jobs as supported instead of created since construction jobs are typically site-to-site and the jobs on the site are constantly changing based on the state of the construction project.

7. If I have an operational impact that occurs year-over-year are the job impacts summative? 

Job impacts in year over year operational impacts should not be summed. 

Consider this scenario:

Blooms Garden Center opens in 2016 and creates 50 jobs. In 2017, there operations also support 50 jobs, but this does not mean that Blooms Garden Center supports 100 jobs. Instead it means that they support 50 annual jobs. Note also that the jobs are only called ‘created’ in the first year.

What if the company has incremental employment increases planned?

If Blooms Garden Center purposes that they will expand to add 20 jobs in 2018 then:

    • You could look at the impact of their adding 20 jobs in 2018 and the additional sales associated to that.
    • You could look at the effect of 70 jobs at Blooms Garden Center in 2018
    • The new year-over-year impact would be 70 supported jobs.

8. Why do the Government Employment & Payroll Sectors start with an *? 

These are specialty Sectors that represent just government payroll and Value Added. As such these Sectors have no Intermediate Expenditures associated to them and will generate no Indirect Effects. If you are looking to Model the effects of government spending in your region, we recommend you use the appropriate government spending pattern found at Activity Options>Import> Institutional Spending Pattern or a spending pattern from the SpendingPatternsNoPayroll_for_Programs_by_SLGovt. For IMPLAN Pro users, you may already have these spending patterns in you Activity Options> Import> From Another Model>IMPLAN User Data >Utilities if not you can download the library for your Sectoring scheme here. If you are in IMPLAN-Online you can request a spending pattern for your government Activity Type from the list of available spending pattern types and we can send it to you. 

The spending patterns found in your Activity Options menu are updated annually but are more generic in their description. Those found in the library are updated every 5 years but are more specific in their description of government activities.

Available spending patterns from the SpendingPatternsNoPayroll_for_Programs_by_SLGovt are:

Federal Govt operating budget expenditures national defense
Federal Govt gross investment national defense
Federal Govt operating budget expenditures nondefense
Federal Govt gross investment nondefense
State & Local Govt operating budget expenditures elementary and
State & Local Govt operating budget expenditures public educatio
State & Local Govt operating budget expenditures other education
State & Local Govt construction elementary and secondary public
State & Local Govt construction public educational facilities be
State & Local Govt invest other education and libraries
State & Local Govt operating budget expenditures hospitals and c
State & Local Govt operating budget expenditures public welfare
State & Local Govt operating budget expenditures sanitation
State & Local Govt construction hospitals and categorical health
State & Local Govt construction public welfare institutions and
State & Local Govt construction public sewerage systems
State & Local Govt construction sanitation
State & Local Govt operating budget expenditures police
State & Local Govt operating budget expenditures fire fighting o
State & Local Govt operating budget expenditures correctional in
State & Local Govt construction police
State & Local Govt construction fire fighting organizations and
State & Local Govt construction correctional institutions
State & Local Govt operating budget expenditures public highways
State & Local Govt operating budget expenditures natural and agr
State & Local Govt operating budget expenditures other general g
State & Local Govt construction public highways
State & Local Govt construction waterports and airports
State & Local Govt construction government-operated transit syst
State & Local Govt construction other commerce activities n.e.c.
State & Local Govt construction gas and electric utilities
State & Local Govt construction government-operated water supply
State & Local Govt construction redevelopment projects
State & Local Govt construction natural and agricultural resourc
State & Local Govt construction other general government activit

Learn more from our Knowledge Base:

Working with Government Institution Spending Patterns

Government Expenditures and Sales

Working with Military Bases

Electricity Generation + Distribution FAQ

We get a lot of questions on how the electricity generation and distribution data is handled in IMPLAN.  The following outlines the steps.

  • First, generation by sector is gathered in Megawatt-hours (MWH) from the US Energy Information Administration
  • Next, we obtain total revenue for all electricity from the US Energy Information Administration
  • With the value from #2 and the sum of total generation from #1, we calculate a revenue per MWH value, which we multiply against the generation by sector from #1 to get an estimate of revenue by sector.
  • We then split that revenue by sector into generation vs. distribution using the share of price between generators and distributors – http://www.eia.gov/electricity/annual/html/epa_02_04.html.
  • Finally, we split between private, federal, state, and local based on CEW data, which have enough detail for all generation types plus distribution.



Methodology for Development of the 2017 Detailed Production Functions for IMPLAN’s Nine Electrical Power Sectors

Utility Purchases & Energy Rebates

Data FAQ

1. How often do I need to update my data?

In general, we leave this up to you. The multipliers are based on the structure of the economy of the year of the data. If you determine that the data year you have is an accurate representation of the current economy, then there is no need to update and purchase a more recent data year.

Here are some situations when IMPLAN Does recommend updating your data:

  • An obvious change in the local structure of the economy. Not all changes in local economies will be as obvious as New Orleans before and after Hurricane Katrina struck. In many cases, it may be growth of a subsector or even the introduction of new industries. The multipliers are based on the structure of the economy, whether an industry exists and the relationships of the demand and supply of commodities. Due to increases in foreign imports, Output Multipliers typically decrease in size somewhat over time. Likewise, as productivity increase, this also indicates decreased employment needs per dollar of output.
  • When a BEA Benchmark is released, IMPLAN follows suit and introduces those new underlying sets of industry production functions. The economy and technology are constantly changing. When new Benchmarks are introduced, new industries are likely introduced as well. If you wish to keep current with the BEA benchmark when reporting your analysis, a recent purchase of that data region is necessary to maintain consistency.
  • Scrutiny of the project using the data. The more exposure an analysis has to the public, particularly a controversial proposal with an opposing side, the more politically important it is to use “the latest data”.

Please feel free to post any additional questions to our Community.

2. How does IMPLAN handle Employment and other factors (e.g., Output, Income, etc.) in the Study Area Data when the Industry or interest, such as a casino hotel, performs several functions?

By definition, Employment in IMPLAN is a head count and not an FTE equivalent. For more information about the definition of IMPLAN Employment, please see our glossary.

While IMPLAN employment and income figures generally start off larger than CEW figures due to the addition of proprietors and proprietor income, a proportion of some sectors’ activity (employment, output, income, etc.) is later reclassified into other sectors. This reclassification process follows the BEA “redefinition” practice and is designed to reassign products from producing industries in which they are secondary products to the industries where those products are primary. Consider a popular hotel on the Las Vegas Strip. Such a hotel typically boasts a casino, restaurant, gift shop, and concert stage and would not be very well represented by the production function, income per worker ratio, output per worker ratio, and other factors of the hotel and motel sector. Therefore, IMPLAN utilizes the national redefinition table from the BEA to “redefine” certain small portions of the industry’s activity to other appropriate sectors such as Gambling (495), Restaurants (503), various Retail, and Performing Arts (488).

3. Does changing the size of Industry affect the Multipliers?

It seems counterintuitive, but a Sector’s Multiplier does not depend on the Sector’s overall size. Instead, what affects the Multiplier is the underlying relationships used in the creation of the Multiplier (specifically, Labor Income per $1 Output and Intermediate Expenditures per $1 of Output) of that Industry. When you run an impact analysis, it does not matter what the initial size of the Industry is – so long as those Industry relationships are what they should be.

4. Why is my impact smaller at the state level than at the county level?

It is true that generally a larger region has less leakages due to imports and therefore is typically a larger impact. However, depending on the industry mix of the county and the region(s) you are comparing it to, the RPCs for what is regionally available in a smaller region can exceed that of a larger region. This typically occurs when the primary region is a key producer of the commodities being examined in the study (or in generally represents the largest functional economy in the region) and thus a larger area increases demand at a faster rate than it provides additional supply, thus reducing the RPC values. However, a key problem also is that while it may seem like you are comparing like regions, because of the nature of Industry aggregation, you are comparing two distinct Multiplier identities. There are two methods for solving this, MRIO and Customization.

For more on this topic, visit our Knowledge Base article “Size of Your Impact – Small vs. Large Study Region“.

5. Why is my Value Added value negative?

Negative values in Value Added are a common source of confusion. Value Added = Employee Compensation + Proprietor Income + Other Property Type Income + Taxes On Production & Imports. PI, OPI, and TOPI can all be negative and if any one or a set of these sum to a more negative than the positive values (i.e., if the negative components are greater in magnitude than the positive components, then you will end up with negative VA). For example in the 2011 US Model Sectors 348 and 349 (both of which produce commodity 3348) have negative OPI. Negative OPI just means that the industry lost money that year (costs were greater than revenues). You can check to see exactly what is the circumstance in your regional industry by going to the Explore> Study Area Data and looking at the View By: Industry Detail sheet to see the breakdown of Value Added and to view which factor is causing the Value Added to be negative. If the factors involved are Other Property Type Income or Taxes on Production & Imports these do not impact the Indirect and Induced results as both of these factors are treated as leakage.

6. What is the difference between Impact vs. Contribution Analysis?

Two general categories of studies using IMPLAN have emerged over the years:  

Impact Analysis is the more common of the two. This type of study examines the economic impacts of an event or change to the economy (e.g., the opening of a new business). These studies address the general question: What are the marginal impacts of the project?

Contribution Analysis is becoming increasingly more common and concerns the role, importance, or contribution of an existing business, project, or industry. These studies address the general question: What is the contribution of the project to the overall economy of the area? 

Contribution Analysis in IMPLAN
Contribution Analysis using IMPLAN Online
Contribution Analysis using IMPLAN Pro

7. Why are Multipliers from OECD Countries low and how are they calculated?

The difference in Multipliers you will see when running an analysis using OECD data is not in how they are calculated but in the extent of the Indirect and Induced effects within a region for a Sector. The Direct Multipliers are always one. If the Sector in the region imports more of its inputs and if more households make purchases outside the region then the Multipliers will be smaller. In addition, – Germany is a smaller country than the U.S. and would be expected to have smaller RPC’s for many commodities.

Another reason is that in the OECD data, (PI) Proprietor Income is not separated out from OPI (Other Property Income); rather, there is just a single GOS (Gross Operating Surplus) category and it is not endogenized (i.e., it is treated like a leakage like OPI is in the standard IMPLAN data and does not generate Induced effects). Thus, Multipliers for all OECD countries will be somewhat lower due to this (including the U.S. OECD Model). If you are comparing to a ‘normal’ (i.e., 536-sector) U.S. Model, the Multipliers are not comparable because the U.S. Model spends PI, which increases the Induced effects and thus the Type SAM Multipliers.

Learn more about how the OECD data is created, what information it contains, and the possibilities and requirements for ordering custom created city, state, or provincial level international data sets.

Construction FAQ

What Value Is Entered for Industry Sales of a Construction Impact?

For the new construction sectors, output is the total value of the structures being built within the region, but does not include items that are not integral to the structure itself. So the Industry Sales value includes the total construction budget (payroll + non-payroll) plus any profits and indirect business taxes (i.e., taxes on production) paid by the construction firm.
This forum discussion addresses the difference between output and budget.

How Do I See What Soft Costs are Included in the Construction Sectors?
You can view the current spending pattern of the construction sector you are working with in the Regions>Region Overview>Social Accounts>Balance Sheets>Industry Balance Sheet>Commodity Demand (IMPLAN), Explore>Social Accounts Balance Sheet Tab (IMPLAN Pro) or in the Model Overview under the Social Accounts button and the Balance Sheets tab (IMPLAN-Online). You will select your industry from the drop down menu and then click on the ‘Commodity demand’ tab to see how much of each of these items the construction spending pattern purchases. Gross Absorption represents the total amount of each commodity that is required for production, while the Regional Absorption shows the amount of that commodity that will be purchased locally when an analysis is run.

How Can These Be Modified if They Don’t Match My Specific Project?
If you would like to modify this to match your data you can do so by importing the spending pattern Activity and modifying the Event coefficients to match your known values. This method is part of the larger category of Analysis-by-Parts

It is important to note that changing coefficients can change many aspects of the Analysis-by-Parts methodology. If you would like to use this methodology, we can certainly help provide some additional helpful hints. Please call your Customer Success Manager or Email us


How Do I Model the Impact of Fees and Tax Revenues Paid by a Construction Project?

Fees, permits and taxes do not contribute directly to impacts in the model, but you can choose to create separate Activities and Events to examine impacts associated to new government revenues.
Here are a couple of cautions to keep in mind when considering modeling the impacts of government spending.

  1. The Federal government is not likely to change its spending behavior as a result of local economic activity.
  2. Locally collected federal tax dollars are unlikely to return to the region from which they are collected.
  3. State and Local taxes can be run through the State/Local Government Non-Education Spending Pattern. This can be found at:
    • IMPLAN Pro: Activity Options > Import > Institution Spending Pattern
    • IMPLAN-Online: Import> Institution Spending Pattern
  4. Fees and permits are revenue to local government, which can be modelled through the State & Local Government Non-Education Spending Pattern.
  5. It is also important to keep in mind that depending on the definition of your region, not all collected state or county taxes may return to your region.

How Should Jobs Associated to Multi-year Construction Impacts be Reported?

We recommend that you divide the impact over the number of years of the project and report the average jobs per year.

For example, if a construction site generates 85 jobs across 3 years, then the report would state the supported jobs as 85/3 or 28 jobs per year. This is because the jobs on the construction site are not cumulative, in the same way that an employee working a job for 3 years is not viewed as 3 jobs.

We also recommend considering construction jobs as supported instead of created since construction jobs are typically site-to-site and the jobs on the site are constantly changing based on the state of the construction project.

Suppose you are investigating the impacts of a new sports event center like a new football stadium. It is important to distinguish the construction impacts of the stadium (“one-time” impacts) from the operations impacts (“on-going” impacts). Construction impacts arise from the activity of building the stadium, and occur only while the project is being built. These impacts essentially end when the project is complete. For example, job impacts associated with a construction of a site are not “permanent”, because these jobs only exist while the project is underway. Even if a construction projects lasts several years, these positions have a clear termination point.

In contrast, operating the built facility is presumed to be “on-going”, and the impacts are usually described on an annual basis. For our stadium example, the annual impacts would result from the operating budget expenditures to run the stadium, as well as, on- and off-site expenditures of visitors to the events. An analyst reporting the impacts of projects like this should refrain from combining the construction and operations impacts into a single impact estimate; it just makes more sense to keep the two kinds of impacts separate.


1. How does IMPLAN CEW differ from BLS CEW data?

Fully disclosed annual employment and income data is available at the U.S., state, and county level based on the Bureau of Labor Statistics Covered Employment and Wages (CEW) series formerly known as ES202. State employment services departments, as part of the Unemployment Insurance Program, collect the base data and pass it to the U.S. Department of Labor. 

All data elements in this series are disclosed. The non-disclosed elements have been adjusted through a procedure developed by Implan Group LLC. This data is provided at the full SIC or NAICS code level of detail (dependant upon the year of the data ordered). SIC based data is available for the years 1988 to 2000. NAICS based data is available from 2001 forward. The CEW dataset provides annual average wage and salary establishment counts, employment counts, and wage and salary workers data by county at the 6-digit NAICS code level. 

2. How is the CEW data provided?

Your purchased data will be emailed to you. The product includes two Excel spreadsheets.

    • The actual regional data
    • A spreadsheet that provides the NAICS descriptions for every 6-digit NAICS code
    • Spreadsheets include both 2007 and 2012 NAICS codes
    • Separate spreadsheets for private Industry and Government reported data
    • Information includes: Employment, Establishment Count, and wage and salary data.

3. How does CEW data differ from IMPLAN Data?

CEW data differs from IMPLAN data in a number of key ways. Here are the top 5.

6-digit NAICS level detail

Agricultural and Services at 3-4 digit NAICS

Manufacturing at 5-6 digit NAICS

Data includes only Employment, Wages, and Establishment Count Data includes Output, Value Added, Labor Income, Employment (including both wage and salary workers and proprietors), Employment Compensation, Proprietor Income, Other Property Type Income and Taxes on Production & Imports Net Subsidies
Excel spreadsheet format

Analysis Model that includes Multipliers and tools for calculating impacts

Time Series 1998-current

IMPLAN data has experienced changes in Sectoring and data estimation methodologies which make time series estimates challenging. Data is available for 1996-2004, 2006-current.

Data include private and government information (at 3 levels Federal, State, and Local Governments).

Data includes private Industries, State & Local Government Education, State & Local Government Non-Education, Federal Government Defense, Federal Government Non-Defense, Captial Investment, Trade, 9 Household Income Categories.

Only establishments that pay Unemployment Insurance and federal civilian jobs covered by Unemployment Coverages for Federal Employees (UCFE) are captured. 

The data set does not capture:

  • self-employed persons
  • railway employment,
  • religious organizations,
  • military,
  • elected officials,
  • shell fishing and fin fishing, 
  • private education,
  • or any other establishments that have their own social insurance program. Since most farm employment is self-employment, CEW data misses much of the farm data

IMPLAN data is controlled to BEA REA data sets and ultimately BEA US NIPA employment as these data sets attempt to capture all employment in the economy and thus allow us to provide a more complete picture of the economy. Proprietor employment includes:

  • Non-employers,
  • Partnerships.

4. How can the IMPLAN Employment be smaller than the reported BLS figures?

It is possible that IMPLAN’s figure could be lower than the BLS’, although usually the IMPLAN Employment is greater than the BLS CEW reported employment. IMPLAN data usually reports higher Employment values because we include an estimate for the number of proprietors in the region as well, or because more than one NAICS codes is incorporated into a Sector. However there are a few conditions under which the IMPLAN reported values may be less that BLS:

  1. A number of Sectors undergo redefintions of their Employment and Output values following the BEA redefinitions. For Sectors where this occurs, it effectively redistributes the reported values within one Sector or NAICS subset and assigns it to another related Sector (e.g. a portion of hotel employment redefined to casinos and gaming).
  2. The BLS table ‘0’ combines private industry plus government activity whereas IMPLAN separates out these values. The following descriptors provide a breakdown of the tables.
    • 0 = Total Employment (government and private)
    • 1 = Federal
    • 2 = State
    • 3 = Local
    • 4 = International Government (Embassies, etc.)
    • 5 = Private
    • 8 = Total Government
    • 9 = Total Employment Excluding Federal Government

    The data we create will be for Ownership Codes 1,2,3, and 5.

5. Why might the CEW county numbers not sum to the CEW state total values?

The data is consistent within the county therefore subsectors add to their aggregates. However, disclosures for CEW data are only run within a county – there is no vertical checking to the state totals.

They are not controlled to state values for 2 reasons:

  1. The existence of “county” 999 which is the BLS CEW dumping ground for employment that can not be located to a specific county – we leave county 999 out (2014 data and earlier).
  2. The CBP data used to non-disclose CEW data can be highly variant from the reported CEW data, and we don’t want to distribute those CBP “inconsistencies” to other counties.

6. Why isn’t there correspondence between NAICS 23* and IMPLAN Construction Sectors?

Construction Sectors are somewhat unique in that we create our construction Sectors from Census descriptions rather than NAICS codes to assist our users, so that they do not need to construct a building from its component NAICS based parts and also because it is Sector with high proprietors. Thus there is not a direct correspondance between the Sectors for IMPLAN construction and the reported values by NAICS in CEW since the reported CEW figures are distributed to their respective IMPLAN construction Sectors.