Other property income

Other property income (OPI) represents gross operating surplus minus proprietor income. OPI includes consumption of fixed capital (CFC), corporate profits, and business current transfer payments (net).  It includes income derived from dividends, royalties, corporate profits, and interest income. Thus, OPI provides a source of income for households, business, and governments.  However, I-O models by default treat OPI as a leakage, meaning that any OPI generated as part of an analysis will not generate any additional effects.  This is because the assumptions that income generated from OPI will go to recipients within the region and that those recipients will spend that income in a typical manner may not be valid. Learn more about multiplier internalization here.

OPI has also been referred to as Other Property Type Income (OPTI).

Other labor income (OLI)

Opportunity Cost

NSK

Generation and Interpretation of IMPLAN’s Tax Impact Report

Generation and Interpretation of IMPLAN’s Tax Impact Report  

 

FILTERING:

To filter by Direct, Indirect, and Induced taxes in IMPLAN, simply open the Filter window and click into the “Impact” Filter. This will provide you the option of “Direct”, “Indirect”, “Induced”. Making a selection and clicking “Run” will apply the filter and only show the Tax Results as specified. if no selection is made, you are viewing total Tax Results. 

 

tax_filter.png

 

TAX IMPACT DETAILS:

The table below clarifies the underlying level of detail of all line items in an IMPLAN tax impact report. In principle, the tax impact report captures all tax revenue in the study area across all levels of government that exist in that study area for the specific industries and institutions affected by an event or group of events. The underlying data that support the tax impact report, however, do not embody that much detail.

For example, IMPLAN does not have systematic reports of state government tax revenue by county; IMPLAN has same-year state government tax revenue by state and must allocate that to counties based on proxy information (we do have county-level data for some states, and use this to build a model for the allocation process).  Also, IMPLAN obtains detailed TOPI data by geography (even for each city within a county), but does not have any industry detail about the specific TOPI line item. A third note: for the data by city, we often must aggregate that to the county level, so that a model of two cities in the same county will have the same implied effective tax rates. In other words, city-specific data will be used, but averaged across all cities within a county.

Please note that all line items are controlled to nationwide, current-year controls estimated by the Bureau of Economic Analysis (BEA) in the National Income and Product Accounts (NIPAs) with no industry resolution and two level-of-government distinctions, Federal and State & Local. For example, the NIPAs might give a value of $15 billion in State & Local income tax in 2017, which would be reflected in the 2017 IMPLAN data.

Industrial and geographic resolution are reported at their maxima and nest more aggregate levels. For example, if IMPLAN has raw data on property tax at the county level, that implies we also have state-level data.

Timeliness lags are reported vis-à-vis the dataset year. For example, a 1 year lag for 2017 IMPLAN data means that the underlying data have a reference year of 2016. Timeliness is especially relevant for knowing whether changes in tax laws or economic conditions are reflected in the IMPLAN dataset.

The table below does not report the combined State & Local level of government, since (other than nationally in the NIPAs as explained above) IMPLAN does not collect any data at this level; it’s simply an aggregate for legacy and convenience purposes.

Level of Government Tax Impact Item Maximum Industry Resolution of Underlying Data Maximum Geographic Resolution of Underlying Data Timeliness of Underlying Data

City/Special District

TOPI: Property Tax

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

City/Special District

TOPI: Motor Vehicle License

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

City/Special District

TOPI: Severance Tax

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

City/Special District

TOPI: Other Taxes

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

City/Special District

TOPI: Special Assessments

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

City/Special District

Personal Tax: Income Tax

n/a

County level

1-2 years lag

City/Special District

Corporate Profits Tax

None

County level

1-2 years lag

City/Special District

Personal Tax: Motor Vehicle License

n/a

County level

1-2 years lag

City/Special District

Personal Tax: Property Tax

n/a

County level

1-2 years lag

City/Special District

Personal Tax: Other Tax

n/a

County level

1-2 years lag

County

TOPI: Property Tax

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

County

TOPI: Motor Vehicle License

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

County

TOPI: Severance Tax

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

County

TOPI: Other Taxes

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

County

TOPI: Special Assessments

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

County level

1-2 years lag

County

Personal Tax: Income Tax

n/a

County level

1-2 years lag

County

Corporate Profits Tax

None

County level

1-2 years lag

County

Personal Tax: Motor Vehicle License

n/a

County level

1-2 years lag

County

Personal Tax: Property Tax

n/a

County level

1-2 years lag

County

Personal Tax: Other Tax

n/a

County level

1-2 years lag

State

TOPI: Property Tax

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

State level

0 years lag

State

TOPI: Motor Vehicle License

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

State level

0 years lag

State

TOPI: Severance Tax

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

State level

0 years lag

State

TOPI: Other Taxes

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

State level

0 years lag

State

TOPI: Special Assessments

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

State level

0 years lag

State

Personal Tax: Income Tax

n/a

State level

0 years lag

State

Corporate Profits Tax

None

State level

0 years lag

State

Personal Tax: Motor Vehicle License

n/a

State level

0 years lag

State

Personal Tax: Property Tax

n/a

State level

0 years lag

State

Personal Tax: Other Tax

n/a

State level

0 years lag

Federal

Social Insurance Tax- Employee Contribution

None

State level

0 years lag

Federal

Social Insurance Tax- Employer Contribution

None

State level

0 years lag

Federal

TOPI: Excise Taxes

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

National level

0 years lag

Federal

TOPI: Custom Duty

TOPI aggregate at BEA sectoring (approximately 80 sectors).

TOPI detail has no industry resolution.

National level

0 years lag

Federal

Corporate Profits Tax

None

National level

0 years lag

Federal

Personal Tax: Income Tax

n/a

National level

0 years lag

Federal

Personal Tax: Estate and Gift Tax

n/a

n/a

n/a

 

 

Updated September 3, 2019

NPISH

Compensation Adjustment in New IMPLAN to Account for Known In-Commuting Rate

To view the commuting data for your region, go to Explore > Social Accounts > IxC Social Accounting Matrix. If the Employee Compensation column makes a payment to the Domestic and/or Foreign Trade row, then there is in-commuting into the region (income earned by workers who work in the area and live outside the area). The ratio of Domestic plus Foreign trade value divided by the Employee Compensation column total gives you an estimate of the in-commuting rate. This rate is referred to as the “samCR” below. If the cell is empty, then there is no in-commuting into the region. 

commuting.PNG

The equation below allows you to adjust IMPLAN’s estimated regional commuting rate to your known regional commuting rate.

 

newEC = EC*[(1-userCR)/(1-samCR)]

where:

EC = original, unmodified employee compensation

userCR = your known commuting rate

samCR = commuting rate reported in the SAM

newEC = the EC value you want to use when running the analysis

 

 

For example, if the SAM shows that the average in-commuting rate in your region is 10% but you know that for your industry it is 20%, then: new EC = $1,000,000*(0.8/0.9) = $1,000,000*(0.88888) = $888,888.

After the analysis has been run, add the difference (EC – newEC) back to your direct EC effect since by definition EC occurs at the site of employment. Since EC is a component of Value Added, you should update the calculation of Value Added to include the difference. Because payroll taxes (social insurance taxes) are paid at the site of employment, the direct effect payroll taxes in your tax results will also need to be revised to their pre-EC adjustment levels. Create a separate activity, duplicate your event but use your EC value that has not been adjusted for commuting, and perform the analysis in a new scenario. Use the resulting direct effect EC payments to social insurance as replacement values for your adjusted EC analysis and re-sum the direct effect and total effect tax impact tables. This way you correctly account for the in-commuters’ direct effect, but you have also made sure that they did not generate any further local impact.

Local Purchase Percentage (LPP) & Regional Purchasing Coefficients (RPC)

INTRODUCTION:

Local Purchase Percentage (LPP) and Regional Purchasing Coefficients (RPC) are two of the most frequently misused and misunderstood fields in IMPLAN. This article describes what these concepts mean and when they should be changed from the default settings.

 

LPP IN INDUSTRY EVENTS:

Local Purchasing Percentages (LPP) indicates to the software how much the Event impact affects the local Region and should be applied to the Multipliers. The key thing to remember when considering Local Purchase Percentage is that the LPP modifies only the Event values, and it does this before those values are applied to the Multipliers.  If we have properly defined the Region, then in most circumstances all of the Industry Production we are modeling occurs within our selected geography and thus LPP should be 100%. When LPP is less than 100%, the remaining portion (or 1-LPP) is then assumed to be affecting a different Region. The portion happening outside the Region of your analysis does not create any local effect.  

Local Purchase Percentage describes the amount of the Direct Effect that is taking place within the Region. For example, if we are constructing a building in a county, all the construction activity takes place in that county, even if all the laborers and the requirements for the building are not sourced in the county, so LPP should be 100%. Likewise, if the operations of new or expanded business are occurring entirely inside our county, even if their employment or materials are sourced elsewhere the LPP should be 100% because the business operations themselves are local.

Changing LPP from 100% in an Industry Event is infrequently appropriate, but one classic example is that of airline ticket purchase. Although $850 may be spent on a ticket, the cost associated with that ticket is divided between the point of origin and destination. Therefore the total budget spent on airline tickets for a business trip may be $850, but the local airport only incurs half of the associated cost.

    • A Local Purchase Percentage of 100% means that all of the Industry Production is occurring within the Model region and all the Employment occurs within the Model region.
    • By definition in Input-Output, Employment is at the site of work, so all employees, regardless of where they live, are counted as employment for the region if they work at a site within the region.
    • Considerations of where an employee lives are taken into account by means of the Employment Compensation and Proprietor Income fields. (Please see the Related Articles section on the topic of Commuting).

 

LPP IN COMMODITY OUTPUT EVENTS:

Again, Local Purchasing Percentages (LPP) indicate to the software how much the Event impact affects the local Region and should be applied to the Multipliers. The key thing to remember when considering Local Purchase Percentage is that the LPP modifies only the Event values, and it does this before those values are applied to the Multipliers. When LPP is less than 100%, the remaining portion (or 1-LPP) is then assumed to be effecting a different Region. The portion happening outside the Region of your analysis does not create any local effect. 

Commodity Output Events are typically used to model a purchase or purchases of a certain Commodity. When the Commodity is known to be produced locally in the Region, leaving LPP at 100% is appropriate. It is common that the location of production of a purchased Commodity is unknown, in which case you can let IMPLAN determine what portion of the production may affect the local Region by selecting the SAM Checkbox next to the LPP field in the Advanced Menu. 

 

LPP IN SPENDING PATTERN EVENTS:

Industry and Institutional Spending Patterns start the analysis, not from the standpoint of the sales or Employment of the Industry or Institution, but instead from the budgetary purchases made by the local organization.

LPP in an Industry or Institutional Spending Pattern tells the software what portion of the line item Commodity was purchased locally and therefore affects the local Region.  When LPP is less than 100%, the remaining portion (or 1-LPP) is then assumed to be effecting a different Region. The portion happening outside the Region of your analysis does not create any local effect. 

Unlike the Industry or Institution itself, we typically cannot say where the production, transport, and wholesaling of the items purchased by our target organization was sourced, and we would not want to assume that these are local purchases. Since this methodology, unlike an Industry Event or Commodity Event, starts not from the Industry or Commodity itself, but from the first round of Intermediate Expenditures, the LPP on these purchases needs to reflect local availability. Thus the LPP is by default set to the SAM Model Value. You would only want to change the LPP on the Commodities within a Spending Pattern if you had information on where the Commodity was produced. 

 

LPP EXAMPLE:

To help envision this more clearly, we can take a look at a quick example to see how the software uses Local Purchase Percentage.

    • An Event for Sector 12 Dairy Farming has shows the following:
      • Industry Sales: $1,000,000
      • Employment: 6
    • If Local Purchase Percentage is set to 50% then the Direct Effects will be $500,000 of Output and 3 jobs. Why?
      • To calculate, the software first multiplies the Industry Sales value by the Local Purchase Percentage ($1,000,000 * 0.50 = $500,000)
      • The software then recalculates the Employment and Labor Income based on this adjusted Industry Sales value, which is half of the entered value and thus generates half the Employment and Labor Income.
      • Deflators are applied to adjust the entered value down to the year of the data set. This makes the dollar values used to calculate the Multipliers equivalent to the entered dollar values.
      • The resultant value is applied to the Multipliers to determine the Indirect and Induced Effects.
      • Thus, the LPP does not provide any information about any of the items purchased by the Sector in the Event field. Regional availability of Intermediate Expenditures and Indirect Effects are determined by the Regional Purchasing Coefficient.

 

RPC DETAILS:

A Regional Purchasing Coefficient (RPC) is the percent of Total Demand that is met by Local Supply. In more detail, it is the proportion of the Total Demand for a Commodity, by all users in the Region, that is supplied by producers located within the Region. The RPC is the value used when LPP is set to the SAM Model Value.

For example, if the RPC for the Commodity fish is 0.8, then 80% of the demand by local fish processors, fish wholesalers, and other fish consumers are met by local fish producers. Conversely, 20% (1.0-RPC) of the demand for fish is satisfied by imports. 

The RPC value is derived from the Trade Flow Model that looks at the movement of Commodities domestically and known rates, by Commodity, for foreign trade. The RPC value tells us, for every Commodity we purchase, how much of our total requirement for that Commodity is obtained from local sources according to the Region and Year of the Data Set. This value is built into the Multipliers, so you never need to make any adjustments to your Event to account for locality of the goods and services required for your production.

The RPC does not assume that all local production goes to local demand (i.e., Regional Supply Coefficient (RSC) may not be 100%). Each Commodity’s RPC can be found in Regions Overview > Social Accounts > Reports > Commodity Summary, Average RPC column.  Values for the RPC are between 0 and 1. Also, the LPP is equal to RPC only when the regional availability of the product is unknown. RPC values cannot be changed in app.implan.com.

 

RELATED TOPICS:

Commuting

Margining: When the Item Being Purchased Is Known

Margining: When the Item Being Purchased in Unknown

The New IMPLAN: What’s so New About It?

CONTENTS

The contents of this article are outlined below. If you already know what you’re looking for, click on a link to advance to a specific section!

1. No more time wasted setting up
2. Faster calculations
3. More fluid workflows
4. Multi-tab functionality
5. A few labels…
6. Easier-to-edit industry spending patterns
7. Better SAMs
8. Multi-angular MRIO
 

INTRODUCTION

From Quesnay’s Tableau économique to Leontief’s Nobel Prize-winning work on modern input-output analysis, impact modeling has a fascinating history. In the nearly 50 years since Leontief, I/O’s application has evolved in both universality and convenience to a degree which few could ever have imagined. Today, coupling improvements in analytical methodology with innovative technologies looks to propel economic impact modeling to its greatest heights yet. And the new IMPLAN hopes to be the primary means by which it reaches them. 

WHY IS THERE A NEW VERSION OF IMPLAN?

The honest answer is…we knew we could do better for you. We value the loyalty, support, and trust that you, our users, have given us over the past 25+ years. If we hope to continue receiving them from you over the next 25+ years, we need to earn them. So, we’ve been hard at work improving our data, revolutionizing our tool, and strengthening our organization to better serve your economic needs.

The new IMPLAN is the culmination of nearly 3 years of work. We’ve been revisiting our legacy tools, listening carefully to (and actively engaging with!) the economics community, and then employing the latest in computing power to meet even its toughest demands. We’re really excited about what we’ve built and we think you will be too. 

The new platform is designed to alleviate the day-to-day challenges of both economists and non-economist alike. From public policymakers to institutional researchers to economic scholars and beyond, those who rely on input-output modeling to shape our understanding of communities both large and small can now do so more conveniently than ever before. In fact, understanding theses groups’ creative uses of IMPLAN was pivotal in helping us develop a tool which now provides even more ways to access, review, and interpret both regional economic data and powerful impact results.

Plus, with a whole bunch of enhanced features, doing all of those classic things you’ve always done with IMPLAN is not only still possible, it’s easier. You don’t have to click on confusing buttons anymore. You don’t have to navigate overcomplicated screens anymore. And you don’t have to execute procedures in a strict and unforgiving order anymore. The new IMPLAN’s flexibility and ease-of-use is unrivaled by any previous iteration you may be familiar with.

Seriously, this version is a game changer.

HOW IS IT BETTER THAN THE VERSION I USE?

1. NO MORE TIME WASTED SETTING UP

Most single geography “Models” you used to spend valuable time building ad hoc are now simply saved as pre-built Regions. So, instead of going through the motions of opening a saved Model or rebuilding one from scratch because you deleted it, now you just select your study’s Region from a dropdown menu. That’s it. And if you edit it, customize it, rename it, save it, come back to it later, edit it again… Whatever. Literally a brand-new, untouched version of that very same Region is always available to you in that little dropdown menu. No more Model-building responsibilities on your shoulders. IMPLAN’s already done it for you—even before you’ve started.

2. FASTER CALCULATIONS

The new IMPLAN’s analysis engine relies on Amazon Web Services (AWS) to do all its dense, heavyweight computing in the cloud. So, this version has enough muscle under the hood to match the demands of even the most sizable IMPLAN projects. This all but eliminates any “wait time” for complex calculations to finish (…sorry to anyone who’s used those procedural interruptions as an excuse to take a break in the past!).

3. MORE FLUID WORKFLOWS

Jump into an analysis from wherever is most natural to you. Know what real-life impact you want to model but not sure exactly what geography you want to study? No problem. Tell IMPLAN everything you know about the impact first, then come back and select a Region later. Know which regional economy you want to analyze but not sure of all the details of your real-life impact quite yet? That’s cool too. Tell IMPLAN which geography you want to study first, then come back and create an Event later.

With IMPLAN’s new project-based workflow, you can start anywhere. Duplicate Events, Regions, and more, and then mix-and-match them quickly and easily without having to jump through a bunch of hoops to set it all back up again. Honestly, you can run 10 complete analyses and be reviewing their results with the same amount of energy it used to take to run just one.

4. MULTI-TAB FUNCTIONALITY

The new IMPLAN isn’t just more convenient to start using, faster in calculating results, and more practical to operate—it’s also categorically more comfortable to interact with. For instance, the new IMPLAN lets you spread your work out across multiple tabs inside your internet browser while you’re working. Within the confines of even a single Project, you can open three different tabs in your internet browser and separate the REGIONS, IMPACTS, and RESULTS screens across them in order to look at all your study’s components at the same time. You don’t have to keep going back and forth between screens to clarify your Region, check your inputs, or confirm your outputs anymore. Apart, IMPLAN and the internet were already awesome. Now they’re awesome together.

WHAT ARE THE BIGGEST DIFFERENCES WITH THIS VERSION?

5. A FEW LABELS…

Some of the new IMPLAN’s most obvious changes pertain simply to how things are labeled or referred to within the tool. The biggest of these changes is with regard to how the tool now refers to the different kinds of impact studies IMPLAN users can run. 

Table_1__the_new_IMPLAN_article_.png

Specifically, what used to be referred to as an “Activity Type” is now simply called an Event Type. While the name may have changed, that’s all that’s changed. Defining an Event Type works exactly the same way as selecting an “Activity Type” always did; simply pick the one that best describes the kind of impact study you’re performing.

So… Why the change? Fair question. The reason is because while “Activities” and “Events” used to exist as two separate items that were created after a regional “Model” was built, the new IMPLAN allows you to create as many Events as you want and then apply them to as many geographies as you want all on the same screen! There’s no longer a need to group or cluster old Events inside an Activity (or any sort of container) within your study because the new tool allows you to simply drag-and-drop new Events into multiple geographies instantly. Now, just create Events by defining the Event Type, entering your study’s inputs, and then dragging those Events into whichever geographies you want to apply them to.

But wait… It gets better.

Because studies are now constructed by simply applying Events to geographies, the new tool allows you to exert greater control when creating Events by changing the way it refers to IMPLAN’s most common Event Type: the “Industry Change”.

Table_2__the_new_IMPLAN_article_.png 

When you open the Type menu on the IMPACTS screen in the new IMPLAN, you’ll see a few more options than you did in previous iterations of the tool. But, they’re all still technically “Industry Change” as far as the computer’s concerned.

Huh? Don’t worry, all this means is that we’ve taken it upon ourselves to itemize the “Industry Change” Event Type for you so that you can specifically define (sooner rather than later) whichever facets of the economy your study’s real-life impact will affect.

For instance, if you’re creating an Event and know the dollars of labor income that your study’s real-life impact will generate, simply select Industry Employee Compensation as your Event Type. Or, if you’re creating an Event and know the number of jobs that your study’s real-life impact will create, simply select Industry Employment as your Event Type.

In previous versions, each of these scenarios would have simply been recognized as an “Industry Change”. But now, the options that IMPLAN asks you to select from when setting up your study are a little easier to distinguish from one another because the terminology that the software uses has become more pointed, relevant, and intuitive. Plus, we think this small change will help you, the user, be able to tell which of your finished Events holds which your study’s values more easily since they’ll be indicated visually. This way, you won’t need to open up Events to see what values you’ve entered into them anymore. Nor will you need to remember which order all your Events are organized in on the screen. Instead, each Event will just tell you what it represents right in the Event Type field!

In addition to these changes, we’ve also modified the names of each of the other Event Types ever-so-slightly just to be consistent with the changes we’ve made to what was previously known as the “Industry Change”. For instance:

Table_3__the_new_IMPLAN_article_.png

See what I mean? We just removed the “change” part of the label because, well, we don’t need it anymore. When defining your Event Type now, Commodity Output will do; the “change” is implied simply by virtue of the fact that you’re creating an Event.

6. Easier-to-edit industry spending patterns

Another important change in the new IMPLAN is the simplification of the process by which you can edit industry spending patterns. In previous iterations of IMPLAN you had to import an industry spending pattern into the tool itself, customize it to your liking, and then use that customized spending pattern as a proxy for your study’s selected IMPLAN Sector. Guess what? Now, even that’s easier.

To edit an industry spending pattern in the new IMPLAN, simply select Industry Spending Pattern as your study’s Event Type and then modify it to your liking. Then, you’re done.

7. Better SAMs

Social Accounting Matrices (SAMs) offer complete pictures of the flow of both market and non-market funds throughout economies in a given year. Market flows occur between the producers of both industrial and institutional goods & services and the industrial and institutional purchasers of those goods & services. Serving as the perennial backbone of the tool’s entire dataset, this essential component now includes even more to explore than it has in previous iterations of IMPLAN.

For instance, in IMPLAN Pro and IMPLAN Online:

  • SAMs reported commuting as net flows.
  • Sub-national SAMs consolidated all reported commuting into the Domestic Trade account.

But, in the new IMPLAN:

  • SAMs report commuting as gross flows, meaning you can see total in-commuting and out-commuting at both state and county levels.
  • SAMs retain all reported foreign commuting within the Foreign Trade account.

These improvements to the SAMs both ensure greater accuracy and make it easier to observe estimated commuting flows both into and out of economies. Additionally, some payments to governments have been reclassified in the new IMPLAN, like rents and royalties paid from Other Property Income (OPI) to governments rather than from Taxes on Production & Imports (TOPI). Such changes serve to align the new IMPLAN’s SAMs with National Income and Product Accounts (NIPAs) from the Bureau of Economic Analysis (BEA) to improve the quality of both tax and impact results.

8. Multi-angular MRIO

In IMPLAN Pro, Multi-Regional Input-Output (MRIO) analyses allow users to observe the “ripple effects” of impacts which are occurring in a specific economy throughout its neighboring economies. However, when exercising this capability, the software is unable to let users observe any “Direct Effects” upon economies besides that in which the original impact is occurring. So, despite offering insights into multiple economies, MRIO analyses are limited to a singular, outward perspective. In order to observe the impacts of events themselves which occur in neighboring economies, those events need to be modeled in their own analyses. IMPLAN users who practice this technique know how time-consuming this process can become.

Imagine a tool with which you could a.) perform multiple separate analyses which each contain only a single Region, but then b.) link those separate Regions to one another such that you’re able to c.) observe both the results of the events occurring within them and the “ripple effects” of those events throughout each of the others respectively…

This is that tool.

IN CLOSING…

Okay, we’ve covered the main stuff at this point.

Lastly, some final, more behind-the-scenes, improvements of note include that this version’s programmatic anatomy is designed to allow for faster uploads of IMPLAN’s annual dataset, the easy addition of future features, and the smooth integration of new functions moving forward.

We know, we know. Those don’t sound like a huge deal. But trust us, they’re pretty big.

In the past, part of what’s prevented IMPLAN tools from evolving as quickly or as seamlessly as you (and we!) would have liked has been a smattering of systematic inadequacies thanks to those tools’ antiquated designs; they simply weren’t dynamic enough in their architecture to accommodate the rapid growth in your analytical needs.

But that was then and this is now. We’ve spent nearly three years on this tool alone and have put in the time upfront to ensure that it not only meets your needs today but can keep up with how quickly they’ll change tomorrow—and even the day after that.

FAQ

Why can I not Margin Sectors I once was able to apply Margins to?

The only marginable industries are Retail and Wholesale sectors.  You’ll no longer be able to margin non-retail/wholesale industries because the margins that were being used were simply for the industry’s margin on its “primary commodity”. This change is to ensure more accuracy. Now, when the item purchased is known, a Commodity Output Event should be modeled using the purchased item as the Specification and Margins can be applied if appropriate. 

Where Margins can be applied, how do I apply Margins?

The options for applying Margins or not applying Margins are now labeled “Total Revenue” and “Marginal Revenue”, respectively. 

Why are my induced results different in the newest release of IMPLAN?

Seeing differing Induced Effects when comparing the results of the same Impact on the same geography and year of data in the new release of IMPLAN to past versions, is due to methodology improvements to the IMPLAN Social Accounting Matrix (SAM). One of the largest changes we’ve incorporated is to use gross commuting flows as opposed to net commuting flows. This means for all regions where there is both in-commuters and out-commuters, the In-Commuting Rate will be larger in the current version of IMPLAN (V5), reducing Induced Effects in the Impact Results.

This is because in these cases, total in-commuting dollars is always larger than net in-commuting dollars because net in-commuting dollars = total in-commuting dollars – out-commuting dollars.

Previously, net in-commuting dollars were divided by total Employee Compensation (EC) in the region to calculate an In-Commuting Rate. Now total in-commuting dollars are divided by total EC to generate an In-Commuting Rate. For single-region analysis, the In-Commuting Rate is applied as a reduction, along with the Payroll Tax reduction, to any EC value (in any Event that generates EC to the region) before it is run through the region as Household Income. This reduction represents the assumed leakage from workers in the Study Area leaving the Region to go home, where they spend their money. Multi-Regional Input Output analysis uses commuting data to track where in-commuters live so there is no change to the way MRIO results are calculated.

Some of the other SAM changes could have a positive effect on Induced Effects, so the overall effect won’t always be a reduction in Induced Effects.

Other SAM changes include:

Previously, sub-national SAMs consolidated all commuting in the Domestic Trade account. Now, foreign commuting stays in the Foreign Trade account. Some payments to government have been reclassified, e.g., rents and royalties are paid from Other Property Income (OPI) to government, rather than from Taxes on Production and Imports (TOPI) to government, in the new IMPLAN. Such changes serve to align IMPLAN SAMs with the Bureau of Economic Analysis’s (BEA) National Income and Product Accounts (NIPAs) and to improve the quality of tax impact results.

 

How do I import Events from Excel?

The feature to import the Event Template into IMPLAN is coming soon. Luckily, Events are much more seamless to enter into the new platform!

How do I search for my Sector using a NAICS code or description?

A new and improved Sector Search feature is coming soon to IMPLAN. Currently you can search for Sectors by NAICS codes and descriptions using this Excel file. 

How do I aggregate Sectors in IMPLAN?

Sector aggregation is coming soon to IMPLAN.

I recently ran an MRIO analysis that is identical to one I have run before. But, this time I got different results. Why?

On 03/27/2019, we deployed a change in IMPLAN which aimed to increase the tool’s performance by imposing a less burdensome (but still realistic) assumption on the system with regard to trade between regions. Specifically, we changed the threshold at which an MRIO analysis in IMPLAN considers the dollars of commodities and/or Employee Compensation being traded between regions to be sufficiently small and subsequently stops processing any further rounds of calculations. Explicitly, we changed this threshold from $0.01 (one penny) to $100 (one hundred dollars).

This is to say, for example, that if the effects of trade between Region A and Region B are being calculated, IMPLAN will stop processing calculations once it reaches the point at which less than $100 of commodities and/or Employee Compensation are transferring between them.

However, while this change will improve the tool’s performance, it will also affect the results of most MRIO analyses (though not all). Specifically, it will produce more conservative estimates. In instances in which this change does affect a study’s results, how big will the difference in them be? Well, we ran back-to-back MRIO analyses to find out; one prior to changing the threshold, and one afterwards. We ran two hypothetical 3-region MRIO analyses in which a $10 million change in total Output was modeled. The first analysis (prior to changing the threshold) took 53 minutes to complete, while the second (after having changed the threshold) took 7.2 minutes to complete. Additionally, the difference in results between the first and second analysis equated to < 0.5% (less than one half of one percent).

We recognize that some analysts may still ask, “Well, my Direct Effects are far less than $10 million. Wouldn’t the observed change in results be greater in scenarios where Direct Effects (in this case, a change in total Output) are smaller?” Well, we ran back-to-back MRIO analyses to test this as well. We ran two more hypothetical MRIO analyses (with 2 regions this time) in which a $190,000 change in total Output was modeled. In this instance, the difference in results between the first and second analysis equated to approximately 0.5% (again, one half of one percent).

Ultimately, because of the change in threshold, we’re seeing significant improvements in IMPLAN’s processing ability with only minimal decreases in the precision of results when performing MRIO analyses. That said, every economic impact analysis is different and each of them has a multitude of factors which serve to influence their results. So, if you have specific concerns regarding how this change may affect your own study, feel free to contact your personal Customer Success Manager (if you know who yours is), email us at support@IMPLAN.com, or call us at (800) 507-9426.

IMPLAN's Core Data Release is now live! Current subscribers can automatically access the new data in-app. If you aren’t a subscriber, you can schedule a demo today to learn more about becoming one.

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