Occupation Data
INTRODUCTION
The Occupation Data header contains information about all of the Standard Occupational Classification (SOC) data in your Region. The SOC categorizes jobs into one of 823 detailed occupations which contains 459 broad groups, 96 minor groups, and 23 major groups. This article outlines descriptions of the terms and categories found within the tables.
Occupation Data only includes Wage & Salary Employment. No Proprietor Employment is included.
Level |
Name |
Records |
Total |
All occupations |
1 |
Major |
2-digit |
23 |
Minor |
3-digit |
96 |
Broad |
4-digit |
459 |
Detail |
5-digit |
823 |
WAGE & SALARY INCOME VS EMPLOYEE COMPENSATION
Wage & Salary income includes base salary and/or wages, employee paid social insurance tax, bonuses, stock options, severance pay, profit distributions, and reimbursements for meals and lodging. Employee Compensation (EC) includes all the above as well as the employer portion of social insurance tax, employer contributions to 401K, and reimbursements for special benefits, such as gym memberships. As EC includes all additional costs of employment, it is often described as the total cost of an employee (or of employees) to the employer or fully-loaded payroll. The difference between them is referred to as supplements to wage and salary income and includes the employer portion of social insurance tax, employer contributions to 401K, and reimbursements for special benefits.
FINDING THE DATA
You can locate the Occupation Data by navigating Behind the i on the Regions screen. The Occupation Data has its own tab full of useful information.
AREA OCCUPATION SUMMARY
This table lists by-occupation data for the region. By default, occupations are presented at the detail level. Use the filter option to select one of the detail levels presented in the table at the beginning of this article.
- Occ Code = occupation code
- Occupation = description of the occupation
- Wage and Salary Employment = regional wage and salary employment for the occupation; excluding Proprietors which are not included in IMPLAN’s occupation data
- Wage and Salary Income = regional wage and salary income for the occupation
- Supplements to Wages and Salaries = value of benefits and bonuses paid to wage and salary workers; the difference between Employee Compensation and Wage and Salary Income
- Employee Compensation = regional employee compensation for the occupation
- Hours Worked = hours worked by the occupation
- Location Quotient = wage and salary employment based location quotient for the occupation, as compared to the U.S. as a whole
AREA OCCUPATIONAL AVERAGES
This table lists by-occupation average data for the region. By default, occupations are presented at the detail level. Use the filter option to select one of the detail levels presented in the table at the beginning of this article.
- Occ Code = occupation code
- Occupation = description of the occupation
- Average Wage and Salary Income = average wage and salary income for the occupation
- Average Supplements to Wages and Salaries = average value of benefits and bonuses paid to wage and salary workers; the difference between Employee Compensation and Wage and Salary Income
- Average Employee Compensation = average employee compensation for the occupation
- Average Hours per Year = average hours worked per year for the occupation
- Average Wage and Salary Income per Hour = average per hour rate of wage & salary income for the occupation (W&S/hours worked)
- Average Supplements to Wages and Salaries per Hour = average per hour rate of supplements to wage & salary income for the occupation (supplements to W&S/hours worked)
- Average Employee Compensation per Hour = average per hour rate of employee compensation for the occupation (EC/hours worked)
INDUSTRY OCCUPATIONAL DETAIL
This table lists by-industry by-occupation data for the Region. By default, occupations are presented for IMPLAN Industry 1 – Oilseed Farming. Use the Filter option to select a different Industry.
- Occ Code = occupation code
- Occupation = description of the occupation
- Wage and Salary Employment = regional wage and salary employment for the occupation; excluding Proprietors which are not included in IMPLAN’s occupation data
- Wage and Salary Income = regional wage and salary income for the occupation
- Supplements to Wages and Salaries = value of benefits and bonuses paid to wage and salary workers; the difference between Employee Compensation and Wage and Salary Income
- Employee Compensation = regional Employee Compensation for the occupation
- Hours Worked = hours worked by the occupation
INDUSTRY OCCUPATIONAL AVERAGES
This table lists by-industry by-occupation average data for the Region. By default, occupations are presented for IMPLAN Industry 1 – Oilseed Farming. Use the Filter option to select a different Industry.
- Industry Code = IMPLAN Industry Code
- Description = description of the IMPLAN Industry
- Occ Code = occupation code
- Occupation = description of the occupation
- Average Wage and Salary Income = average wage and salary income for the occupation
- Average Supplements to Wages and Salaries = average value of benefits and bonuses paid to wage and salary workers; the difference between Employee Compensation and Wage and Salary Income
- Average Employee Compensation = average employee compensation for the occupation
- Average Hours per Year = average hours worked per year for the occupation
- Average Wage and Salary Income per Hour = average per hour rate of wage & salary income for the occupation(W&S/hours worked)
- Average Supplements to Wages and Salaries per Hour = average per hour rate of supplements to wage & salary income for the occupation(supplements to W&S/hours worked)
- Average Employee Compensation per Hour = average per hour rate of employee compensation for the occupation (EC/hours worked)
CORE COMPETENCIES
AREA SUMMARY
These tables summarize the regional composition of each competency.
Ability, Knowledge, and Skills
- Competency Code = Unique competency identifier from O*Net competency codes
- Description = Competency description from O*Net competency descriptions
- Ext Description = further details on the competency description
- Competency Points = The sum of competency points are a measure of the human capital endowments in the Region given the current occupation mix of Employment in the Region. For more information on how competency points are calculated, see the article Occupation Data Details.
- Competency Share of Total = A measure of the relative size of a competency when compared to the total sum of like competencies; how important this competency is compared to other like competencies. Example formula (Ability: Oral Comprehension Competency Points / Sum of Ability Comprehension Points).
- Location Quotient = Competency based location quotient as compared to the U.S. as a whole
Education Required, Work Experience Required, and On-the-Job Training
- Competency Code = Unique competency identifier from O*Net competency codes
- Description = Competency description from O*Net competency descriptions
- Employee Count = The sum of Wage and Salary employees estimated to exhibit the specified competency. For more information, see the article Occupation Data Details.
- Employee Count Share of Total = A measure of the percent of total wage and salary employees that exhibit the given competency.
- Location Quotient = Wage and Salary Employee count based location quotient as compared to the U.S. as a whole
INDUSTRY SUMMARY
These tables summarize the regional composition of each competency by industry. Use the filter to switch which industry’s information is displayed as Industry 1 – Oilseed farming will be displayed by default.
Ability, Knowledge, and Skills
- Industry Code = IMPLAN Industry code
- Description = IMPLAN Industry description
- Competency Code = Unique competency identifier from O*Net competency codes
- Description = Competency description from O*Net competency descriptions
- Ext Description = further details on the competency description
- Competency Points = The sum of competency points are a measure of the human capital endowments in the Region given the current occupation mix of Employment in the Region. For more information on how competency points are calculated, see the article Occupation Data Details.
- Competency Share of Total = A measure of the relative size of a competency when compared to the total sum of like competencies; how important this competency is compared to other like competencies. Example formula (Ability: Oral Comprehension Competency Points / Sum of Ability Comprehension Points).
Education Required, Work Experience Required, and On-the-Job Training
- Industry Code = IMPLAN Industry code
- Description = IMPLAN Industry description
- Competency Code = Unique competency identifier from O*Net competency codes
- Description = Competency description from O*Net competency descriptions
- Employee Count = The sum of Wage and Salary Employees estimated to exhibit the specified competency. For more see the article Occupation Data Details.
- Employee Count Share of Total = A measure of the percent of total Wage and Salary Employees that exhibit the given competency.
OCCUPATION SUMMARY
These tables summarize the regional composition of each competency by occupation. Use the filter to switch which occupation’s information is displayed.
Ability, Knowledge, and Skills
- Occ Code = occupation code
- Occupation = description of the occupation
- Competency Code = Unique competency identifier from O*Net competency codes
- Description = Competency description from O*Net competency descriptions
- Ext Description = further details on the competency description
- Competency Points = The sum of competency points are a measure of the human capital endowments in the Region given the current occupation mix of Employment in the region. For more information on how competency points are calculated, see the article Occupation Data Details.
- Competency Share of Total = A measure of the relative size of a competency when compared to the total sum of like competencies; how important this competency is compared to other like competencies. Example formula (Ability: Oral Comprehension Competency Points / Sum of Ability Comprehension Points).
Education Required, Work Experience Required, and On-the-Job Training
- Occ Code = occupation code
- Occupation = description of the occupation
- Competency Code = Unique competency identifier from O*Net competency codes
- Description = Competency description from O*Net competency descriptions
- Employee Count = The sum of wage and salary employees estimated to exhibit the specified competency. For more information, see the article Occupation Data Details\
- Employee Count Share of Total = A measure of the percent of total Wage and Salary Employees that exhibit the given competency
SORTING
The title bar displayed across the top of each table contains buttons that can change the viewing attributes. Clicking next to the name in the grey box will sort the column of interest either in ascending (first click) or descending (second click) order.
DOWNLOADING
The export function allows data to be exported directly to a PDF or CSV file by clicking on the gear icon. Clicking on the ellipses will download each table individually.
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Occupation Data Use Cases
DEVELOPING TARGETED JOB TRAINING INITIATIVES WITH REGIONAL STRENGTHS & WEAKNESSES
Preparing the local workforce to fill the jobs that local businesses have open (or will have open in the future) can help close the economic loop within a regional economy. That is, the ideal situation would be for local jobs to be filled by local workers. This helps close the regional loops by preventing wage leakage due to commuting and by grounding local businesses within the community. Whether it is the local community college, job skills training programs, or vocational up-training programs it’s important that there is alignment between the training received by workers and the skills needed by local businesses. The goal of this article is to demonstrate how a community can evaluate the types of jobs in the local economy, the skills needed to fill those jobs, and the expected wages of those jobs so that local education partners can train local workers to fill them and keep more talent, more businesses, and more money in the local economy. Check out the article on Location Quotients to learn how the Occupational LQs can help inform these decisions: What can an LQ do for you?
EMPLOYMENT REQUIREMENTS: NEW HOSPITAL OPERATIONS
Whatever your impact or contribution, one of the most valuable ways to tell your story comes from the jobs your activity supports. Whether it’s by directly employing people, the jobs your activity supports through your supply chain, or through jobs supported by wage spending, jobs are one of the most tangible and important measures of your impact or contribution.
As an example, let’s consider a new hospital being built in Mecklenburg County, NC. This is a new, state-of-the-art, hospital in an underserved urban area. Once the hospital is finished it will employ 500 workers. The company building and operating the hospital would like to show the County Board of Commissioners what the Economic Impact will be of the operations of the hospital. The Board of Commissioners has said that in addition to creating economic impacts, they will need to show that at least 40% of the 500 expected jobs (or at least 200 jobs) at the hospital will go to workers who have education levels less than a bachelor’s degree in order to qualify for certain requirements for tax exemptions.
Framing a full economic impact of a hospital is covered in detailed articles on Construction and Hospitals, so here we will keep things simple and focus on fulfilling the requirement of 40% of jobs going to a workforce with less than a bachelor’s degree. After setting up and running our analysis, we can use the Occupation Data to determine what the education level is of the workers expected for the hospital. Here’s how this project looks on the Impact screen:
By looking at the Core Competencies on the Results tab of our analysis, we can find the Education table below that shows the distribution of the education levels of the Direct Jobs.
Adding up the workers at education levels below “Bachelor’s Degree” the firm is able to show that they expect more than 350 of the 500 (or more than 70%) of the jobs at the hospital to go to workers who have education levels less than a Bachelor’s Degree.
Additionally, if we include not only the 500 Direct Jobs in the hospital, but ALL jobs through the supply chain and support through the spending of wages (i.e. Direct, Indirect, and Induced jobs), we can see that more than 70% of the nearly 800+ jobs in Mecklenburg County supported by the operations of the hospital are expected to go to workers who have less than a Bachelor’s Degree. This means that the hospital more than doubles the requirements for the tax exemption, which is sure to make the Board of Commissioners quite happy!
EVALUATING DIFFERENT OCCUPATION OPPORTUNITIES (NMTC)
New market tax credits are powerful incentives that the economic development community can use to entice new businesses or encourage business retention within the local economy. However, it can be difficult to understand what different opportunities bring to the local economy. This section demonstrates how analysts might compare two (or more) economic development opportunities in terms of the numbers, types, and wages of the jobs that would be expected to accompany them.
Let’s say the IMPLAN Economic Development Corporation™ has received approval from Mecklenburg County, NC to provide a New Market Tax Credit (NMTC) to a new business being brought into the county. The objective of the NMTC is to provide more jobs in the county for low-skill, low-income workers in coordination with affordable housing projects in progress. So, the requirements for the NMTC are that the project must support at least 100 jobs in occupations that make an average wage of less than $45,000 per year in Mecklenburg County.
There are two applications for the NMTC. The first application is for a Restaurant Co-op which would house four different restaurants and will employ a total of 125 people. The other application is for a retail business which provides tutoring, test preparation, and other educational services which will employ 225 people. Here’s how this looks on the Impacts screen:
Looking at the Occupation Data in IMPLAN for Mecklenburg County, we can see there are 7 major occupation categories that have an average annual wage of less than $45,000.
Food preparation workers and education services workers are both in this group. Since we know the education services business is planning to employ 100 extra employees, we could reasonably expect that it may produce more jobs with wages below $45,000. However, with the new Occupation Data in IMPLAN we can actually look at these individual occupations and how many jobs are in each category.
First, let’s look at the jobs breakdown for the Education Services Business:
Next, let’s take a look at the breakdown for the Restaurant Co-op.
Digging deeper into the occupational distribution of the jobs supported by these projects it’s apparent that the restaurant co-op project is likely to produce more jobs with annual salaries below $45,000, even though the education services business is going to initially employ 100 more people.
Using this type of analysis can help guide local development groups towards policy that are more aligned with their stated goals. Let’s hope the four new restaurants are fantastic. Maybe we’ll finally get that good Thai food restaurant we’ve been hoping for!
Occupation |
Average Annual Wage |
Jobs Supported by the Restaurant Co-Op |
Jobs Supported by the Education Services |
Food Preparation and Service Related Occupations (35-0000) |
$21,622.75 |
114.38 |
4.26 |
Personal Care and Service Workers (39-0000) |
$22,642.71 |
0.77 |
6.24 |
Military (99-0000) |
$26,534.78 |
0 |
0 |
Building and Grounds Cleaning and Maintenance Occupations (37-0000) |
$30,194.95 |
1.61 |
2.69 |
Healthcare Support Occupations (31-0000) |
$32,338.17 |
0.49 |
0.84 |
Protective Service Occupations (33-0000) |
$39,842.25 |
0.57 |
2.48 |
Education, Training, and Library Occupations (25-0000) |
$42,512.70 |
0.33 |
54.13 |
TOTAL JOBS |
– |
118.15 |
70.64 |
BUSINESS LOCATION OR EXPANSION
As organizations look to expand, locate, or relocate, one of the primary questions they must answer is how prospective areas match the labor force needs of the business. That is, are the workers needed by the business available in the area? What are the differences in wages between workers in one area versus another? The answers to these questions often play a crucial role in the decision-making process for business evaluating location decisions. This section demonstrates how this type of analysis can be done using the IMPLAN application and the Occupation Data within it.
As an example, let’s consider a financial consulting firm that wants to expand its operations into North Carolina. The firm is considering Charlotte, Raleigh, and Asheville as possible locations. They would like to know how many Accountants, Actuaries, Bookkeepers, and Financial Analysts are in each of those areas and how much each makes on average.
Charlotte is located in Mecklenburg County, Raleigh is in Wake County, and Asheville is in Buncombe County, so we’ll use those three counties as the Regions for this analysis.
The Occupation Codes for each of the interested jobs are in the table below (broad categories).
Occupation Common Name |
SOC Code |
SOC Code Description |
Accountants |
13-2010 |
Accountants and Auditors |
Actuaries |
15-2010 |
Actuaries |
Bookkeepers |
43-3030 |
Bookkeeping, Accounting, and Auditing Clerks |
Financial Analysts |
13-2050 |
Financial Analysts and Advisors |
For each of these Occupations, the firm would like to know the number of wage and salary employees, the annual take-home wages, and the location quotient in each of three identified counties. This will help them understand how each of the proposed laborsheds are prepared to accommodate the needs of the firm. Using the data Behind the i on the Regions screen, we can gather the data requested and display it in the tables below.
Buncombe County |
|||
Occupation |
Location Quotient |
Wage and Salary Employment |
Average Wage and Salary Income |
Financial Analysts and Advisors |
0.64 |
380 |
$109,403.72 |
Bookkeeping, Accounting, and Auditing Clerks |
0.94 |
1,431 |
$36,831.00 |
Actuaries |
0.42 |
8 |
$98,672.70 |
Accountants and Auditors |
0.86 |
1,048 |
$67,457.31 |
Mecklenburg County |
|||
Occupation |
Location Quotient |
Wage and Salary Employment |
Average Wage and Salary Income |
Financial Analysts and Advisors |
2.10 |
6,608 |
$179,280.94 |
Bookkeeping, Accounting, and Auditing Clerks |
1.21 |
9,752 |
$57,625.92 |
Actuaries |
1.75 |
175 |
$165,185.81 |
Accountants and Auditors |
1.40 |
9,078 |
$113,093.86 |
Wake County |
|||
Occupation |
Location Quotient |
Wage and Salary Employment |
Average Wage and Salary Income |
Financial Analysts and Advisors |
1.06 |
2,696 |
$135,293.58 |
Bookkeeping, Accounting, and Auditing Clerks |
1.05 |
6,848 |
$48,785.09 |
Actuaries |
1.18 |
95 |
$153,844.24 |
Accountants and Auditors |
1.16 |
6,089 |
$92,513.26 |
From these data, the firm can see that there simply aren’t enough of the workers they would need in Buncombe County and thus Asheville would not be a wise place to locate. However, both Mecklenburg and Wake Counties have Location Quotients above 1.00, implying that these occupations are more available in these counties than the U.S. average. That means both counties are generally well stocked with the workers this firm would need.
Further, between Mecklenburg and Wake Counties there is a trade off to consider. Mecklenburg County has more employees in every relevant occupation than does Wake County. However, those workers all earn more income in Mecklenburg County, implying that labor costs of the firm would be higher in Mecklenburg County than costs for the same amount of workers in Wake County. The firm will now have to decide whether the increased availability of workers in Mecklenburg are worth the projected increased costs.
This type of analysis provides the firm with data upon which to make sound planning decisions about where the new operation should be located.
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Using Occupation Data in IMPLAN
INTRODUCTION
IMPLAN has added its occupation employment by industry data to the IMPLAN application. This dataset shows estimates of occupation employment, wages, hours, and core competencies which include knowledge, skills, abilities, education, work experience, and on-the-job training levels for each of 823 different occupations. Occupation data is available for all models, including models of combined regions, aggregated industry models, and customized models. Occupation data will match the current Data Year in IMPLAN (ie 2018 Data Year will show 2018 Employee Compensation).
There are two different types of Occupation Data that are useful during your analysis: Regional Occupation Data and Occupation Impact Data.
Regional Occupation Data shows the Occupations, Wages, and Core Competencies that are already present in the Region you are studying. You can find this data Behind the i on the Region page.
Occupation Impact Data shows the details for the Occupations, Wages, and Core Competencies that accompany your specific impact or contribution.
DETAILS
Occupation data can be used to examine study area data to gain important insights into a Region’s existing labor force, the skill requirements of various Industries, and more. The Occupation Data can also be seen in the Results of any impact or contribution analysis run within the IMPLAN application.
The data levels generally correspond to the BLS’s Standard Occupational Classification (SOC) codes. Major is the most aggregated, followed by Minor, Broad, and Detail. All data is based on national averages but brought to the regional level in IMPLAN. As with Employment, you may see fractional Occupations, especially for small firms where a few employees are spread across multiple Occupations.
The SOC categorizes occupations into one of 823 detailed occupations which contains 459 broad groups, 96 minor groups, and 23 major groups.
Level |
Name |
Records |
Example |
Total |
All occupations |
1 |
All Occupations |
Major |
2-digit |
23 |
51-0000 – Production Occupations |
Minor |
3-digit |
96 |
51-7000 – Woodworkers |
Broad |
4-digit |
459 |
51-7030 – Model Makers and Patternmakers, Wood |
Detail |
5-digit |
823 |
51-7032 – Patternmakers, Wood |
WHERE TO FIND OCCUPATION DATA
REGIONS OVERVIEW
The tables located in the Regions Overview provide insight into the model Region’s occupation makeup. Descriptions of the data found here are detailed in our Occupation Data – Behind the i article.
IMPACTS
Distributions of occupation employment, wage, and core competencies are applied only to regional and results data. At this time, you cannot run impacts based on occupations.
RESULTS
The Occupation Impact Data shows the details for the Occupations, Wages, and Core Competencies that accompany your specific impact or contribution in the Results tab. Descriptions of the data found here are detailed in our Occupation Data – Behind the i article.
The Occupation Impact Tab opens the Occupation Impacts screen containing a single table detailing regional employment and wage impacts by-occupation. Use the Filters to limit results to desired Region, Group, Event, and Industry. The Filter can also be used to select a different occupation aggregation level.
The Occupation Impact Averages tab opens the Occupation Impacts screen containing a single table detailing the average regional employment and wage impacts by-occupation. Use the Filters to limit results to desired Region, Group, Event, and Industry. The Filter can also be used to select a different occupation aggregation level.
Core Competency opens the Competency Impacts screen containing tables for each core competency category (Ability, Knowledge, Skills, Education Required, Work Experience Required, On-the-Job Training Required). Use the Filters to limit results to desired Region, Group, Event, Industry, and occupation. The filter can also be used to select a different occupation aggregation level.
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Scaling Groups & Events
INTRODUCTION
IMPLAN is an annual, linear model. This means that analyzing $100 would yield the same Results as analyzing $1 and multiplying the results by 100. The scaling feature in IMPLAN allows you to set up Events and then adjust all the Events by this figure.
SCALING GROUPS
Scaling is especially fun when looking at a group of Events for a tourism impact. When analyzing tourism impacts, we have a list of expenditures usually by individual visitors. We can set this up as shown with one Event for each affected Industry. In this example, we are examining a basic visitor spending for a weekend with the Idaho Falls Chukars. We know that each visitor spends approximately $2,000 on a weekend trip across six spending categories.
If we are told there were 5,000 visitors, we could just multiply each Value by 5,000. However, we can also use scaling to tell IMPLAN to multiply each value by 5,000. Drag your Events into the Group. Then on the number 1 next to the Group name, type 5,000. This will multiply each Event by 5,000.
Our Results will show the impacts of $2,000 total spending for each of the 5,000 guests. Our Direct Output is therefore just shy of the $10M in total spending, due to the fact that we used one retail Industry and therefore margins were applied. For more information on this, check out the articles Margins & Deflators and Why don’t my Direct Effects match my Direct inputs?
SCALING EVENTS
Perhaps we are told that only 3,000 of the total 5,000 attendees used hotel rooms, transit, and airfare. We can scale each Event individually to reflect this new information. Ensure that the scaling on the entire Group is 1. Then click on the menu icon and select scaling.
Now you can edit the 1s that are located to the right of each Event in the Group. In our example, we enter 3,000 for Hotel, Transit, and Airfare, and 5,000 for Restaurant, Retail, and Sports.
This will then tell IMPLAN to multiply the first three Events by 3,000 and the last three Events by 5,000. We would expect a Direct Output of slightly less than $8.4M and we see $7.9M in our Results.
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Volunteers: Estimating the Economic Impact of Free Labor
INTRODUCTION
Volunteers don’t get paid, not because they’re worthless, but because they are priceless.
Sherry Anderson
Unfortunately, not all productive activity is actually included in GDP. Volunteer or unpaid work is not included as it is not only difficult to measure, it has no explicit economic activity related to them that can be measured using I-O analysis. However, the results of that volunteer activity as measured by other economic activity related to the workers (additional tourism, increased productivity, etc.), then one can use that activity as the input, claiming the value exists due to the volunteer work.
Not all volunteers are essential. Some may perform functions that are wonderful, although not required and the organization would still operate without them. However, it is hard to think about an organization like Girl Scouts or the American Legion running without volunteers. If the argument can be made that “but for” these volunteers, none of the activity would occur, then estimations of their economic impact make sense. However, care needs to be taken when outlining your methodology to avoid valuing volunteer work as full Output-producing jobs.
VALUING VOLUNTEER TIME
The Independent Sector estimates the Value of Volunteer Time for each state and the U.S. each year. The current estimation is $25.43 per hour at the national level. Their methodology is based on average hourly pay “of all production and non-supervisory workers on private non-farm payrolls” from the BLS. They note that following the Financial Accounting Standards Board guidelines, volunteer time should only be included if they are performing a specialized skill. Note that the Value of Volunteer Time is based on the volunteer work, not the person’s paid employment. So if a rocket scientist volunteers for a park beautification, the value of his or her time should be based on the earnings of a groundskeeper and not a rocket scientist.
Another way to value volunteer time is using the BLS Occupational Employment Statistics data. This data is released by occupation, so a closer estimate to the actual value of a certain job duty can be found. This is best utilized when a volunteer is performing a specialized skill like a trained medical doctor volunteering to do health screenings at a community event.
A third school of thought is that the value of a volunteer’s time should be based on the hourly wage that the person normally receives for their paid employment. This is problematic on a few levels and is the least preferable method, but may provide useful insight in some situations.
ANALYZING THIS IN IMPLAN
Now that you have estimated the value of the volunteer time you want to capture in IMPLAN, create an Industry Employee Compensation Event in the Industry that reflects the work. Enter the value of the volunteer work in the Employee Compensation. If you know the number of volunteers, add that in using the advanced menu. Also in the advanced menu, zero out the Proprietor Income field. Run your analysis.
You may only want to estimate the value of the Intermediate Inputs that would be required because of volunteer time, as well as the associated Indirect and Induced Effects. In this case, we recommend that you run either an Industry Employee Compensation Event or an Industry Employment Event and zeroing out Proprietor Income. Then on your Results, deduct the Direct Employment and Direct Employee Compensation associated with the volunteers. So if you ran $500,000 in an Industry Employee Compensation Event, on the Results screen you would zero out the Direct Employment and deduct $500,000 from Direct Labor Income, Direct Value Added, and Direct Output.
Life’s most persistent and urgent question is, what are you doing for others?
Martin Luther King, Jr.
Pandemic: Additional Considerations when Modeling the Coronavirus
INTRODUCTION
As the world continues to see changes stemming from the COVID-19 pandemic, more questions arise on best practices for how to look at the economic impact of the latest developments in IMPLAN. Here are some considerations to think through when analyzing your impacts.
ASSUMPTIONS
The assumptions inherent in Input-Output analysis remain the same. However, two specifically come into play.
The Constant Make Matrix assumption states that an Industry will always produce the same mix of Commodities regardless of the level of production. In other words, an Industry will not increase the Output of one product without proportionately increasing the Output of all its other products. Given the increased production of items like personal protective equipment, this assumption could be called into question when manufacturing firms like Ford have pivoted to make respirators and masks; not a usual Commodity coming from automobile manufacturers.
On the flip side, another assumption states that there are No Supply Constraints. This means there are no restrictions to raw materials and employment to produce an unlimited amount of product. During this pandemic, there is excess capacity as plants are operating at reduced capacity and there are a lot of unemployed people. Because of this, businesses are willing to accept lower prices and also not increase prices, so increases in demand (i.e., the changes analyzed in Input-Output) are less likely to change prices and Input-Output is therefore more reasonable.
PURCHASING CHANGES
People and businesses normal purchasing patterns have changed. Industries may be spending less on payroll and travel, while spending more on technology. These changes will affect how the multipliers react.
Additionally, household purchases are changing. There are fewer trips to restaurants and far less travel and entertainment. So, the multipliers for household spending are also affected.
The government issued stimulus checks to people that are working and those that are not. This money will likely be saved by those working and spent by those who are currently not employed.
Finally, while some people have been furloughed or lost their jobs entirely, unemployment benefits are being collected. In some cases, people are actually earning more with the expanded unemployment benefits. This money may be spent or saved.
INDUSTRY CHANGES
Industries are changing, too. Trade across the U.S. and the world has changed. Also, the relative sizes of Industries have changed. Some tech and manufacturing industries, for example, have grown, whereas in-person service industries (e.g., restaurants, sporting events, other personal services) have declined. Some businesses have been completely shuttered while some are booming and employing additional staff and shifts. The Paycheck Protection Program (PPP) has given out loans to small businesses to pay their employees during the pandemic in order to ensure that Employee Compensation levels are maintained, benefits retained, rent is paid, and utilities are continued. These forgivable loans might make the difference between a business staying open or closing for good.
ANALYZING THIS IN IMPLAN
Some of the changes to the economy are easily modeled in IMPLAN like adding 100 new jobs at a manufacturing plant with an Industry Employment Event. However, given the systemic nature of many of the current changes, Analysis by Parts (ABP) is often the best approach. Using ABP allows you to tailor the Leontief Production Function by inputting your own Direct Effects and even modifying your Indirect and Induced Effects.
For example, the Indirect and Induced Effects can be altered to move spending that was going to restaurants to spending at grocery stores. Also, an assumption could be made that people are saving more money than normally because they cannot go anywhere and therefore less disposable income is expended. On the other hand, many people have lost income and are probably spending a higher percentage of income than in the model. In these cases, you can export the SAM, rearrange and rebalance it, and recalculate your own multipliers that more accurately reflect what you want to show.
Governments have also been reporting large losses in sales tax revenues. Most of this stems from reduced overall economic activity, not from changes in effective tax rates. By adjusting your inputs and results, you should not have to do much editing to the tax impact report. However, effective personal income tax rates are based on labor income and capital income; reduced capital income (e.g., from dividends and rent) might require adjustments to personal income taxes. Corporate income taxes likely also would need to be edited. In this case, we recommend supplementing the direct tax impacts with your own estimates.
DATA RESOURCES
The following lists some data sources on how the economy has changed to help inform how to edit direct effects, the model, and/or results.
Several news outlets, research organizations, banks, and consultancies have identified particular industries and occupations affected by changes related to COVID-19.
Brookings: The places a COVID-19 recession will likely hit hardest
JP Morgan: COVID impact on markets: stimulus and valuations
New York Times: The Workers Who Face the Greatest Coronavirus Risk
Moody’s: Coronavirus impact on sectors
Government sources have some timely information as well.
BEA: Aggregate preliminary data on components of GDP and Value Added
BLS CES: Changes in Industry Employment and Compensation
BLS Labor Productivity: Changes in Productivity
Census: Small Business Pulse Survey Data
CONCLUSION
Until all of the dust settles from the economic changes spurred by the pandemic, we will not know the full extent of the impact. Be cautious not to overstate the effects in your analysis. Consider taking additional care in analyzing the Results by Industry and Type of Effect. As there are likely to be winners and losers, consider both sides of the equation by using a Net Analysis.
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Housing: Owning, Buying, Selling and Renting
INTRODUCTION
Owner-occupied dwellings is a Special Sector developed by the BEA to ensure consistency in the flow of funds.
Owner-occupied dwellings is necessary to include as an Industry in IMPLAN because home-ownership generates wealth (the home can be rented out to others or saves the owner from having to pay rent); the Value Added portion of this Industry is counted as part of GDP along with the Value Added of every other Industry in the country. Owning and maintaining a home is also a major area of spending for homeowners; this Industry captures the economic effect.
The Owner-occupied dwelling Industry Production Function only contains:
- Output = Total value of home ownership
- Intermediate Inputs = Non-mortgage commodity spending
- Taxes on Production and Imports (TOPI) = largely made up of Property Tax
- Other Property Income (OPI) = difference between the rental value of the home and the non-mortgage costs of home ownership (Output – Intermediate Inputs – TOPI); Mortgage Interest is included in OPI
Here’s the Industry Detail for the U.S. 2018 Economy:
Note there is no Employment or Labor Income generated in this Industry. Below, the components of production have been converted to percentages of Output for the Industry as a whole in the U.S. 2018 Economy:
Notice in the screenshot below, Industry 449 does not exist in the NAICS Bridge (546 Industry Scheme) because the industry does not correspond to a NAICS Code. NAICS Codes are used to classify business establishments. The Owner-occupied dwellings Industry is not a business establishment and therefore does not have a NAICS Code.
The owner-occupied dwelling Industry is different from the other two real estate related Industries in IMPLAN; Industry 448 Tenant-occupied housing and Industry 447 Other Real estate.
Tenant-occupied housing (448) includes the value of operations for lessors of residential rentals.
Other Real estate (447) includes the value of operations for residential property managers, lessors of nonresidential buildings, and offices of real estate agents and brokers (offering services other than residential leasing).
Owner-occupied dwelling and Tenant-occupied housing are the number one and three Industries (in the U.S. 2018 Economy) in terms of Total Household Demand. Logically this seems very appropriate as they respectively represent the value of home-ownership and home renting/leasing.
RENT PAYMENT IMPACTS
The economic effect of rent payments associated with some Event is accounted for in the Induced Effects in the Results of the given Event when analyzed in IMPLAN.
If you’d like to directly analyze rent payments (made to a leasing establishment as opposed to a home-owner), the total value of rent payments can be analyzed via an Industry Output Event in the Industry 448 – Tenant-occupied housing. When analyzing this Industry, the necessary service of renting out the property via a property manager will be captured as an Indirect Effect to IMPLAN Industry 447 – Other real estate. This is because property management service is an Intermediate Input that Tenant-occupied housing purchases from Other real estate.
HOME OWNERSHIP IMPACTS
The economic effect of home-ownership costs associated with some Event is accounted for in the Induced Effects estimated in the Results of the given Event when analyzed in IMPLAN.
MORTGAGE PAYMENTS ARE NOT INCLUDED IN ANY INDUSTRY
The exchange of cash for principal is essentially an asset swap. Input-Output models do not account for existing assets – only annual change (e.g., net savings, new construction, equipment purchases). This means mortgage payments are an asset swap and are not part of the current accounts, and are therefore not included in any Industry. An Industry’s production function only includes current accounts; it does not include investment.
Households make a payment to the capital institution in IMPLAN. This payment is accounted for when analyzing Household Income Events. The payment is net savings, which includes paying down debt (i.e., principle). Mortgage payments are included in this payment to capital by Households.
HOMEOWNERS RENTING OUT THEIR HOME
When a homeowner rents out their home, either to a vacationer or a resident, the income they earn is often used to pay their mortgage and maintain the home. The surplus income they earn can be spent in a variety of ways.
Rent income earned by a homeowner can be analyzed via a Household Income Event. The Event Value should not include any fees for the paid service of property management. This approach assumes the homeowner will distribute the income like any other income earned. As mentioned in the previous section, Household Income Events accounts for the Household Income groups average net savings (net payments to capital). The portion of total Household Income for the income group that is allocated to net savings in the Region and Data Year is assumed to be the portion of every dollar entered in a Household Income Event that will be allocated to net savings. Because the capital institution in IMPLAN is not internalized in the Multipliers, there will be no effect in the Results from income allocated to capital. In other words, IMPLAN does not assume how capital payments will be spent, so these payments are treated as leakage. Another portion, accounting for income taxes, is also treated as a leakage.
The remaining Household Income is distributed across the average makeup of Commodities purchased by the given income group. Commodities purchased with this Household Income that are produced by Industries within the Region generate Induced Effects.
The allocation of Household Income includes a payment to IMPLAN Industry 449 – Owner-occupied dwellings which represents the value of the house as if it was rented. Owner-occupied dwellings pay for repair and maintenance services, real estate fees, and bank financing fees (such as refinancing a mortgage). These payments generate the iterations of Induced Effects from owning, repairing, and maintaining the home. Owner-occupied dwellings also pay taxes (largely property taxes). Once again, tax revenue is treated as a leakage. The residual going to Other Property Income is considered “imputed income” for households and includes mortgage interest. OPI is another leakage in IMPLAN.
Analyzing rent income to a homeowner via a Household Income Event is not perfect because the allocation of Household Income, after accounting for leakages, is an average for all households in that income group, not just the homeowners. Because spending by households that do pay rent are included in these averages, Tenant-occupied housing will also receive a payment.
BUYING AND SELLING A HOME
The cost associated with buying a constructed home is included in the cost of owning a home. The net income gained when a home is sold can be analyzed only if where, when, and on what the income will be spent is known. If the seller of the home uses the income to buy another house in the Region you may have real estate and financing fees for the new mortgage that can be analyzed accordingly. If the seller of the home is moving out of the Region, which is often the case, there is no impact to the Selected Region.
BUILDING A HOME
Constructing a new home does of course create an impact due to the need for materials and labor. The cost of constructing a home can be analyzed via an Industry Output Event in the Construction of new single-family residential structures Industry. More information on considerations when analyzing construction are found in the article Construction: Building the Analysis.
QUANTIFYING HOMEOWNERS’ PROPERTY TAX
TOTAL REGIONAL HOMEOWNERS’ PROPERTY TAX
The Taxes on Production and Imports (TOPI) for IMPLAN Industry 449 – Owner-occupied dwellings is made up largely of property taxes, therefore you can identify the total property taxes paid on homes as approximately the value of TOPI for IMPLAN Industry 449 – Owner-occupied dwellings. This value can be found in the Region Details: Behind the i in
Study Area Data
> Industry Detail (shown for U.S. in screenshot above)
> Find the row for the Owner-occupied dwellings Industry and the intersecting column for TOPI.
HOMEOWNER PROPERTY TAX EFFECTS
The ratio of total TOPI to total Output in a given Region for a given Industry is called the TOPI coefficient. This coefficient can be found Behind the i in
Social Accounts
> Balance Sheets
> Industry Balance Sheet
> Value Added tab
> Filter by Owner-occupied dwellings Industry, find the TOPI coefficient.
The TOPI coefficient for IMPLAN Industry 449 – Owner-occupied dwellings can be multiplied by the value of Owner-occupied dwelling Output generated in your IMPLAN Results to calculate the associated approximate value of Property Taxes paid by homeowners on their homes.
You can find the Owner-occupied dwelling Output effects generated in your IMPLAN Results by navigating to the Output tab of your Results. Find the Owner-occupied dwelling row in the Industries by Impact table (sort by Induced Effects). Here you’ll see the Output Effects to the industry. Unless you analyzed an Event in the Owner-occupied dwelling Industry, you will only see Induced Output to this Industry.
Supply Chain versus Value Chain
INTRODUCTION:
It is important to be able to distinguish between the concepts of a Value Chain and a Supply Chain. Supply Chains describe the chain of Intermediate Input purchases, or backward linkages, that are included in an Industry’s Spending Pattern and the Spending Patterns of its supplier, and so on. These purchases are incorporated into a new product that is then sold either to another Industry or to final demand.
A simplified Supply Chain of t-shirt may look like:
Cotton → Fabric → T-shirt
On the other hand, Value Chains describe how a good changes in value, after its produced, through the process of selling the good via a wholesaler or retailer. Throughout the Value Chain, Commodities increase in value, but unlike through a Supply Chain, do not change physically from the factory door to the final sale. A Value Chain provides a breakdown of the Total Revenue of a good into the Marginal Revenues of each Value Chain component (which sum to Total Revenue).
The Value Chain of the t-shirt includes each element of value of the t-shirt at time of purchase at the retailer:
tshirt_value_chain.png
DETAILED INFORMATION:
WHERE TO FIND AN INDUSTRY’S SUPPLY CHAIN
In IMPLAN, you can explore an Industry’s Supply Chain from Behind the “i” in
Social Accounts >
Balance Sheets >
Industry Balance Sheet >
Commodity Demand, Filtering by the Industry of interest
Here you will find a list of all of the Industry’s Intermediate Inputs. Gross Absorption and Gross Inputs reflect all demand. Regional Absorption and Regional Inputs reflect local demand. Filtering by the Industries that produce the Regional Inputs allows you to explore the next round of the Supply Chain. Exploring the Supply Chain will exponentially widen in each round of suppliers purchasing from their suppliers.
WHERE TO FIND A PRODUCT’S VALUE CHAIN
The Value Chain of a good cannot be found Behind the “i” in IMPLAN. Instead you’ll need to explore the Excel file of the IMPLAN Margins. The full value of good is available in the Commodity tab of this excel file. These Margins are not regionally specific.
For example, let’s look at the Value Chain for Mens and boys cut and sew apparel, which includes T-shirts:
tshirt_value_chain_excel.png
The chart below displays all non-zero values in the Value Chain:
Commodity
Commodity Name
Margin Commodity
Margin Commodity Name
MarginValue
3125
Men’s and boys’ cut and sew apparel
3125
Men’s and boys’ cut and sew apparel
0.372592838
3125
Men’s and boys’ cut and sew apparel
3400
Wholesale services – Other nondurable goods merchant wholesalers
0.123361792
3125
Men’s and boys’ cut and sew apparel
3401
Wholesale services – Wholesale electronic markets and agents and brokers
0.009337612
3125
Men’s and boys’ cut and sew apparel
3409
Retail services – Clothing and clothing accessories stores
0.477160698
3125
Men’s and boys’ cut and sew apparel
3414
Air transportation services
0.001306514
3125
Men’s and boys’ cut and sew apparel
3415
Rail transportation services
9.04E-05
3125
Men’s and boys’ cut and sew apparel
3417
Truck transportation services
0.01615018
This shows that 37% of the value of a t-shirt sold at a retailer is the Marginal Revenue/Producer Price of the t-shirt. 12.3% of the Purchaser Price of a retail t-shirt is the Marginal Revenue for the merchant wholesaler and 47.7% is the Marginal Revenue for the retailer, and so on.
ANALYZING A SUPPLY CHAIN VS. IMPACTING A VALUE CHAIN
The Supply Chain of the affected Industries are being analyzed In every IMPLAN analysis. This is the foundation of Input-Output analysis and is how IMPLAN calculates backward linkages.
The full Value Chain of a good is only analyzed when Total Revenue has been entered into a Commodity Event.
Only the retail portion of Total Revenue is analyzed when Total Revenue has been entered into an Industry Event with a retailer or wholesaler specified.
SUPPLY CHAIN LEAKAGES
Remember, Supply Chains describe the chain of Intermediate Input purchases, or backward linkages, that are included in an Industry’s Spending Pattern and the Spending Patterns of its supplier, and so on. When Intermediate Input purchases are bought from Industries in the Selected Region this supports an economic effect in that Region. In the case of the Direct Industry’s Supply Chain, local industry purchases of Intermediate Inputs specifically support Indirect Effects. All of an Industry’s purchases cannot be sourced from Industries within the Region even at the U.S. level. The purchases of imports and institutional production (other than srap produced by households) are exogenous to a Region’s multipliers in IMPLAN. This simply means these purchases are leakages and do not generate effects.
IMPLAN calculates the Supply Chain leakages for you by setting the Local Purchase Percentage (LPP) on Commodity purchases in an Industry’s Supply Chain to the Regional Purchasing Coefficient (RPC) for the given Commodity unless you specify otherwise. The only time IMPLAN will default the LPP of a Commodity purchase to 100% is in a Commodity Event.
IMPLAN determines the industry and institution producers of a Commodity in a Region based on the Market Share for the Commodity.
VALUE CHAIN LEAKAGES
As mentioned, only the retail portion of Total Revenue is analyzed when Total Revenue has been entered into an Industry Event with a retailer or wholesaler specified. The other portions of Total Revenue are leakages because without knowing what was purchased at the retailer or wholesaler, the full Value Chain cannot be identified.
Because the components of a Value Chain for a good are made up of Commodities, some portion of the Commodities in the Value Chain may be produced by institutions in the Region, in which case these portions are leakages.
When LPP is set to a value less than 100% in a Commodity Event evaluating a Value Chain there will also be leakages. When LPP to is set to “SAM” in this situation, the leakage of each Commodity in the Value Chain is based on the RPC for each Commodity.
Adding an Industry that Doesn’t Exist Yet
INTRODUCTION:
Sometimes the Industry that we want to examine does not exist in our study Region. The Industry might be new to the area. It also might be a new Industry altogether. It also might be an Industry that only recently became legal. This article outlines the best way to model these new Industries in IMPLAN.
DETAILS:
The economy is always changing and Industries come and go. Federal data follows these changes by adjusting the North American Industry Classification System (NAICS) codes. IMPLAN follows suit, so when the NAICS codes change, so do IMPLAN Sectors.
There are two ways to create a new Industry depending on what you want to accomplish. Both methods are outlined in this article.
INDUSTRY THAT EXISTS IN THE US:
If you have a Region that will be gaining an Industry that does exist somewhere in the US, you can follow the steps outlined in the article Adding an Industry by Customizing a Region.
BRAND NEW INDUSTRY:
Federal data lags in categorizing these new Industries, which can pose a problem for researchers. When the newest widget is invented, it is usually categorized with a similar product or under one of the catch-all codes like “all other…” One interesting case is that of the legalization of marijuana or cannabis production and sales. The 2017 NAICS do have information on where the pieces of the supply chain fall for marijuana:
- Marijuana, grown under cover – Industry 6 – Greenhouse, nursery, and floriculture production
- Marijuana, grown in an open field – Industry 10 – All other crop farming
- Marijuana merchant wholesalers – Industry 400 – Wholesale – Other nondurable goods merchant wholesalers
- Marijuana stores, medical or recreational – Industry 412 – Retail – Miscellaneous store retailers
Let’s say we want to examine the effect of a $100M in cannabis agricultural production in Ohio and we know that a full $5M of that will be spent on legal fees. We also know there will be 25 employees each making $50,000.
First we have to decide what Industry we want to use. In this case knowing the climate in Ohio, we make the assumption that the agriculture will be most closely related to IMPLAN Industry 6 – Greenhouse, nursery, and floriculture production.
Create an Industry Spending Pattern Event for $100M in Industry 6. If we know more details about the spending of cannabis growers as compared to other greenhouse products, we can edit the spending pattern to further reflect this new Industry. Remember in this example we know that $5M will be spent on legal fees specifically. To model this, we click the Advanced button to open the details of the Industry Spending Pattern and scroll down to Commodity 3455 – Legal Services. Next, we override the value with 5%; the dark blue color shows the edited Commodity. If any further information is known including Industries that could be deleted entirely from the Spending Pattern, that can be done at this point as well. More details on this can be found in the articles Editing Industry Spending Pattern Events or Editing Institutional Spending Pattern Events.
You’ll notice that the Sum of Percentages at the bottom is now 104.94%.
To fix this and return to 100%, click on the Advanced button and choose Normalize.
Now, a full 5% will be spent on legal fees. Make sure to choose Total Output (instead of Intermediate Expenditures). Remember: Output = Intermediate Expenditures + Value Added.
Next, we create a second Event for our Labor Income (25 employees x $50,000 = $1,125,000). We can then add them to our Group and Run our economic impact.
Notice that there are no Direct Effects in our Results so we will need to add those back in manually. We know our total Output is $100M and our Labor Income is $1,125,000 with 25 employees. Now we need to navigate back to our Regions screen and click
Study Area Data >
Industry Summary
Here we can find IMPLAN’s known totals for Sector 6 – Greenhouse, nursery, and floriculture production. Calculating the ratio of the overall Value Added to the overall Output shows that VA is, on average for this Industry, 37% of the total Output. Therefore, we multiply $100M in Output by 37% to yield a Direct Value Added for this project of $36,727,998.33. The template for these calculations is here.