IMPLAN Users Guide

Occupation Data – Behind the i

Occupation Data Use Cases

Using Occupation Data

Occupation Data

August 12, 2020/0 Comments/in Advanced Analysis, IMPLAN Users Guide, Support Site/by Joe Demski

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.

DOWNLOADS

Using Occupation Data in IMPLAN

Occupation Data Use Cases

August 12, 2020/0 Comments/in Advanced Analysis, IMPLAN Users Guide, Support Site/by Joe Demski

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.

DOWNLOADS

Using Occupation Data in IMPLAN

Occupation Data – Behind the i

Using Occupation Data in IMPLAN

August 12, 2020/0 Comments/in Advanced Analysis, IMPLAN Users Guide, Support Site/by Joe Demski

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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Occupation Data Use Cases

Occupation Data – Behind the i

Institutional Spending Pattern Events

August 12, 2020/0 Comments/in Basic Analysis, IMPLAN Users Guide, Support Site/by Joe Demski

INTRODUCTION

Institutional Spending Patterns, like all Spending Patterns, are made up of a list of Commodities, although Institutional Spending Patterns include government payroll Commodities to capture spending on Labor. Because Institutions are Final Demanders, in the case of Institutional Spending Patterns, each Commodity in the Spending Pattern is treated like a Commodity Output Event and will create a Direct Effect.

Event Type	Institutional Spending Patterns
Event Use	Analyzing general fiscal spending changes
Event Specification ”WHO”	Federal Government NonDefense Federal Government Defense Federal Government Investment State/Local Govt. Other Services State/Local Govt. Education State/Local Govt. Hospital & Health State/Local Govt. Investment Capital Inventory Additions/Deletions
Event Value “WHAT”	Total Spending on Goods, Services and Labor

INSTITUTIONAL SPENDING PATTERN EVENTS

IMPLAN can be accessed via app.IMPLAN.com. Once you are logged in, you will be directed to the IMPLAN dashboard. From the dashboard you can navigate to the Regions, Impacts, or Projects screen.

Once you have chosen your Region and named your new Project, you will be directed to the Impacts screen. From here, click on Add New Event to create a Institutional Spending Pattern Event. In this set of examples, 2018 Nevada data is utilized.

Institutional Spending Pattern Events are unique in that they describe both Intermediate Inputs and Value Added within the same Spending Pattern. This results in these Spending Patterns producing ‘mixed results;’ where the reported Direct Effects describe both what we would generally consider Direct Effects (income, Employment and Value Added) and the first-round Indirect Effects that arise from the government spending its budget. Institutional Spending Pattern Events can be edited. Learn more in the article Editing Institutional Spending Pattern Events.

In this example, we want to look at a potential $1M increase in education spending by the government. We will give our new Event a Title, select the Type as Institutional Spending Pattern, the Specification is 12002 – State/Local Government Education, and the Value is $1,000,000.

Now that you have your Event, ensure that it is highlighted in teal by clicking on the Event or checking Select All Events at the top of the screen. Now the Events can be dragged into your Group on the right.

You will know when the Events have populated in the Group when the number in the upper right of the Group box equals the number of Events.

Now click Run in the bottom right of the Impacts Screen.

The Institutional Spending Pattern Event Type represents a general spending distribution for measuring broad Institutional activity in your Region.

ABP: Introduction to Analysis-By-Parts

Editing Institutional Spending Pattern Events

Industry Contribution Events

August 12, 2020/0 Comments/in Basic Analysis, IMPLAN Users Guide, Support Site/by Joe Demski

INTRODUCTION

Industry Contribution Analysis (ICA) is a method used to estimate the value of an Industry or group of Industries in a Region, at their current levels of production. While the focus of the analysis still looks at backward linkages, the purpose of this analysis differs from the standard economic impact analysis. ICA shows the relative extent and magnitude of the Industry, event, or policy in the study area.

ICA is a unique method which applies a constraint upon the model by removing feedback linkages (buybacks) to the Industry being analyzed. This method can also be used with single firms, but if/when it is, the results of this method should be considered conservative.

ICA denotes that the study is looking at how the current state of an Industry supports other businesses in the local economy. When this involves a single firm, the analyst will need to determine if the Impact Method or Industry Contribution Analysis Method should be used. The results in either case should be described with words such as “contributes to” or “sustains.”

Event Type	Industry Contribution
Event Use	Estimating effect of an existing Industry’s production
Event Specification ”WHO”	Industry 1-546 for which the contribution is being measured
Event Value “WHAT”	Output or % of Industry

INDUSTRY CONTRIBUTION EVENTS

IMPLAN can be accessed via app.IMPLAN.com. Once you are logged in, you will be directed to the IMPLAN dashboard. From the dashboard you can navigate to the Regions, Impacts, or Projects screen.

Once you have chosen your Region and named your new Project, you will be directed to the Impacts screen. From here, click on Add New Event to create a Industry Contribution Event. In this set of examples, 2018 Nevada data is utilized.

In this example, we want to look at the contribution of the entirety of the hotel Industry in Nevada. We will give our new Event a Title, select the Type as Industry Contribution Analysis, the Specification is 507 – Hotels and motels, including casino hotels, and the Value is 1. The buttons after the Event Value give us the choice between entering a dollar value or entering a percentage of the total Industry. In that we want to see the entire Industry, we will select the %. Check out the article Picking an Industry for assistance in choosing the appropriate IMPLAN Industry.

You will know when the Events have populated in the Group when the number in the upper right of the Group box equals the number of Events. You will also see a new icon in teal indicating that this is an Industry Contribution Analysis.

Now click Run in the bottom right of the Impacts Screen.

To see how the constraints on the hotel Industry worked, navigate to the Output tab in the Results. The largest Industry in terms of Output was Industry 507 – Hotels and motels, including casino hotels with a total of $15,193,837,564.97. On the Industries by Impact table, you can see that the entirety of this was in the Direct Effect and the Indirect and Induced Effects in this Industry are zero. If we had used a standard Industry Output Event, there would be additional Indirect and Induced Effects in our Results.

ICA: Introduction to Industry Contribution Analysis

Industry Spending Pattern Events

August 12, 2020/0 Comments/in Basic Analysis, IMPLAN Users Guide, Support Site/by Joe Demski

INTRODUCTION

Industry Spending Patterns include all Intermediate Inputs for a given Industry. Industry Spending Pattern Events are most appropriate to use when an analyst has the data required to build a customized Spending Pattern which reflects specific purchases by an Industry or when the ratios in an Industry’s Leontief Production Function must be modified to a degree beyond that which is achievable by simply customizing an Industry Event.

Event Type	Industry Spending Patterns
Event Use	Analyzing a change in Intermediate Inputs
Event Specification ”WHO”	Industry 1-546 making purchases of Intermediate Inputs
Event Value “WHAT”	Intermediate Inputs or Output (per Advanced Menu)

INDUSTRY SPENDING PATTERN EVENTS

IMPLAN can be accessed via app.IMPLAN.com. Once you are logged in, you will be directed to the IMPLAN dashboard. From the dashboard you can navigate to the Regions, Impacts, or Projects screen.

Once you have chosen your Region and named your new Project, you will be directed to the Impacts screen. From here, click on Add New Event to create an Industry Spending Pattern Event. In this set of examples, 2018 Nevada data is utilized.

At the most basic level, Industry Spending Pattern Events can be used to examine the economic impact of only the Intermediate Inputs of an Industry. Intermediate Inputs are the purchases of non-durable goods and services that are used to produce other goods and services rather than for final consumption. Industry Spending Patterns can be edited through the Advanced Menu. Learn more in the article: Editing Industry Spending Pattern Events.

In this example, we want to look at only the spending on Intermediate Inputs by casinos. We will give our new Event a Title, select the Type as Industry Spending Pattern, the Specification is 503 – Gambling industries (except casino hotels), and the Value is $1,000,000. Check out the article Picking an Industry for assistance in choosing the appropriate IMPLAN Industry.

Now that you have your Event, ensure that it is highlighted in teal by clicking on the Event or checking Select All Events at the top of the screen. Now the Event can be dragged into your Group on the right.

You will know when the Events have populated in the Group when the number in the upper right of the Group box equals the number of Events.

Now click Run in the bottom right of the Impacts Screen.

The first thing you will notice is that there are no Direct Effects. Industry Spending Pattern Events only affect spending on Intermediate Inputs, so there will only be Indirect and Induced Effects. Learn more in the article Explaining Event Types.

Industry Spending Pattern Events are often used as part of Analysis-by-Parts (ABP). ABP allows you to examine a specialized Industry or a firm that is not reflected by an IMPLAN Industry due to differences in the structure of the Leontief Production Function and/or Spending Pattern that cannot be edited via a single Industry Event. This type of analysis is typically used to split the stemming ripple effects of an Industry Impact into its individual impact components – budgetary Spending Pattern (Intermediate Inputs), which can be analyzed using an Industry Spending Pattern Event, and payroll, which can be analyzed using Labor Income Event(s).

ABP: Introduction to Analysis-By-Parts

Editing Industry Spending Pattern Events

Understanding Intermediate Inputs (II)

Household Income Events

August 12, 2020/0 Comments/in Basic Analysis, IMPLAN Users Guide, Support Site/by Joe Demski

INTRODUCTION

Household Income Events are appropriate to model a change in Household Income isolated from Industry production and payroll. In Household Income Events, you can specify the specific income group(s) receiving the income. Examples would be a government stimulus check or tax refund.

Household Income values should include all new Household Income the residents will receive including personal tax and savings. IMPLAN will automatically deduct personal tax and savings. Household spending of income on imported goods and services will also be treated as leakage.

Event Type	Household Income Events
Event Use	Analyzing a change in Household Income separately from production
Event Specification ”WHO”	HH LT15K, HH 15-30K, HH 30-40K, HH 40-50K, HH 50-70K, HH 70-100K, HH 100-150K, HH 150-200K ,HH GT200K
Event Value “WHAT”	Total Household Income for specified income group

HOUSEHOLD INCOME EVENTS

IMPLAN can be accessed via app.IMPLAN.com. Once you are logged in, you will be directed to the IMPLAN dashboard. From the dashboard you can navigate to the Regions, Impacts, or Projects screen.

Once you have chosen your Region and named your new Project, you will be directed to the Impacts screen. From here, click on Add New Event to create a Household Income Event. In this set of examples, 2018 Nevada data is utilized.

Household Income represents the income received by people for their participation in production, from government and business transfer payments, and from returns on capital (e.g., interest payments, dividends). Household Income Events allow the analyst to pick which income group(s) will receive the gain or loss of income.

Specification	Explanation
Households LT15k	Those earning less than $15,000
Households 15-30K	Those earning between $15,000 and $30,000
Households 30-40K	Those earning between $30,000 and $40,000
Households 40-50K	Those earning between $40,000 and $50,000
Households 50-70K	Those earning between $50,000 and $70,000
Households 70-100K	Those earning between $70,000 and $100,000
Households 100-150K	Those earning between $100,000 and $150,000
Households 150-200K	Those earning between $150,000 and $200,000
Households GT200K	Those earning greater than $200,000

In this example, there will be a stimulus check for households that earn less than $40,000. We are told this will result in $2,000,000 of new income in Nevada for households earning less than $15K, $1,500,000 for households earning between $15-30K, and $3,000,000 for households earning between $30-40K. Therefore, we will set up one Household Income Event for each household level.

Now that you have your Events, ensure that all are highlighted in teal by clicking on them individually or checking Select All Events at the top of the screen. Now the Events can be dragged into your Group on the right.

You will know when the Events have populated in the Group when the number in the upper right of the Group box equals the number of Events.

Now click Run in the bottom right of the Impacts Screen.

The first thing you will notice is that there are no Direct or Indirect Effects. Household Income Events only affect household spending, so there will only be Induced Effects. Learn more in the article Explaining Event Types.

In this example, we included three Events, so the default on the Results screen will include all three. If you want to look at the impacts for just one Event, click on Filters and search for the Event Name that you want to see.