Combining Regions

INTRODUCTION

Combining Regions in IMPLAN allows you to create Regions of grouped geographies to analyze. The great part is once you create a Combined Region, it will always be available for you to use. 

 

DETAILS

You can create a custom Combined Region in IMPLAN with just a few simple clicks. From the Regions screen, select all of the Regions you want to combine. Click the Menu Button in the Selected Regions field.

 

Combine_Regions_-_Menu_Button.jpg

 

A popup will appear asking you to name your new Combined Region. Remember to give it a name that makes sense as this will now be searchable in from your Regions or Impacts screen.

 

Combine_Regions_-_Name.jpg

 

IMPLAN is now building your Combined Region. You will know it is complete when the teal icon appears next to the i as shown below.

 

Combine_Regions_-_Complete.jpg

You can now proceed with your analysis by clicking Create Impact in the bottom right corner of your screen. 

HINTS

You can always start a new Project using this Combined Region by searching for the title you gave it on the Regions screen.

Note that there is a limit of 200 geographies in Combined Regions.

 

MRIO

Combining Regions is very useful when you are using MRIO. For example, you may want to compare your county to the remainder of the state. To ensure that your Project runs efficiently, you can create a Combined Region for all of the other counties in your state. You can then MRIO your county to the Combined Region of the remaining counties. So instead of having to use MRIO with Brewster County, TX and the other 253 counties, you can use MRIO with Brewster County and a Combined Region of the 253 other Texas counties.

Did You Know?

INTRODUCTION:

IMPLAN Economists have received a lot of questions over the years. We have compiled a considerable repertoire of best practices and helpful tips from the trenches. This article outlines the most popular extras we want to share with you.

 

Sunflower_-_Candi.png FILTERS:

When in doubt, FILTER. When exploring Sector/Commodity specific data in the Region Details Behind the “i”, you will need to use the filter to choose the Industry or Commodity you want to see.  Otherwise, you get the default Sector 1 – Oilseed farming (or Commodity 3001 – Oilseeds). And we know we aren’t always looking for information on sunflowers.

Also utilize filters on your Results screen. You can filter to segment your results by Region, Impact, Group, and Event Name. You can also filter the Dollar Year in your Results to choose the year in which you’d like to report your results. When navigating between the tabs on your Results screen, always double-check that the filters you applied stayed with you.  

 

Ruler.png SIZE MATTERS:

The size of the impact you are running does matter. If you are trying to run an MRIO on one zip code versus the other 41,701 zip codes in the U.S., it likely won’t finish as that money will keep bouncing around the country for quite some time. IMPLAN recommends no more than 7 regions in any MRIO.

If you need to run several studies and one is larger, IMPLAN recommends starting with the smaller one.  All of your impacts will be put in a queue, so we don’t want that small study getting trapped behind that large one.  

 

Magic_Wand.png FINAL TRICKS:

You can have multiple tabs of IMPLAN open at the same time. You can set up multiple projects, compare Results across projects, or just look cool. 

IMPLAN works best in Chrome, Edge, Firefox, and Safari. Internet Explorer is not supported – as Microsoft quit using it in 2016.  You can try it, but the map won’t even show up on the Regions screen, so we don’t recommend it.  

If you are having issues with something loading improperly or taking longer than usual, hit save and refresh your browser.  This corrects the majority of issues.

You can search within Spending Patterns by using Control + F.

Older datasets (2012-2014) can be accessed by starting at the Project Screen and selecting New Project rather than starting your project at the Regions Screen.

Estimating Employee Compensation Adjustments for Known Commuting Rates

INTRODUCTION:

IMPLAN estimates an in-commuting rate for all regions – how many people work in the region and go home to another region. This article will show you how to see what IMPLAN estimates as the in-commuting rate and how to adjust it if you have more specific information for your project. The good news is that although there are quite a few calculations to adjust for your known in-commuting rate, we have a spreadsheet to ease the pain.

 

FINDING IN-COMMUTING RATES:

There are a few steps to figure out the in-commuting rate in IMPLAN. To view the data for your region, click behind the “i” to find the study area data for your region.

 

Commuting_Rate_-_Behind_the_i.jpg

 

Next, we can find the in-commuting data for the Region. Navigate to: 

     Social Accounts > IxC Social Accounting Matrix > Aggregate IxC SAM

The Social Accounting Matrix (SAM) can be interpreted as columns making payments to rows. We will be focusing on the Employee Compensation (EC) (5001) column. The payments from the EC column to the Domestic Trade (28001) and Foreign Trade (25001) row are the household income dollars paid to employees that work in the Region but do not live in the Region, or in-commuters. This is referred to in IMPLAN as Commuter EC – the remaining portion of Employee Compensation (EC) once payroll taxes and foreign commuting are removed.

To get an estimate of the in-commuting rate for your region, add the Foreign Trade (20551) + the Domestic Trade (28001) row and divide by the total EC. Below is an example from North Carolina in 2017.

 

Commuting_Rate_-_Domestic___Foreign_Trade.jpg

 

So, we take 

     $537,597,426.39 + $7,204,800,286.95 = $7,742,397,713.34. 

Then divide that by the total EC

     $7,742,397,713.34 / $284,542,852,368.49 = 2.72% 

This is the in-commuting rate which you can calculate using the file Commuting Rate – Calculations by filling in the teal boxes.

 

Commuting_-_Finding_In-Commuting.jpg

 

ADJUSTING FOR KNOWN IN-COMMUTING RATE:

Adjusting for your known in-commuting rate is necessary when IMPLAN’s in-commuting rate differs from yours, particularly when the difference is significant. The portion of EC earned in the Region by these in-commuters, according to the in-commuting rate, is treated as a leakage in IMPLAN.  Because EC by definition occurs at the site of employment, EC earned by in-commuters is still considered Direct EC to the Region, but because it is earned by non-residents, this EC will not generate any Indirect or Induced Effect in the Region. 

Now that you have your commuting rates, we can use one more table to adjust IMPLAN’s estimated regional commuting rate to your known regional commuting rate. Remember, your known commuting rate needs to be the value of the compensation that leaves the region. It cannot include payroll taxes.

We have four variables:

     MyEC = original, unmodified employee compensation
     MyCR = your known commuting rate
     SamCR = commuting rate reported in the SAM
     NewEC = the EC value you want to use when running the analysis

So the formula is:

     NewEC = MyEC* [ (1-MyCR) / (1-SamCR) ]

For our NC example, we see

     NewEC = $5,000,000 * [ (1-0.00%) / (1-2.72%) ] = $5,139,855.22

 

Commuting_-_Adjusting_In.jpg

 

This number, $5,139,855.22 is what you will run through your EC event. But you aren’t quite done yet.

After the analysis has been run, add the difference (MyEC – NewEC) back to your direct EC effect. This is because the adjusted EC value (NewEC) used to run the analysis was just a means of estimating the appropriate Induced Effect based on the known commuting rate. In this example, the difference between MyEC and NewEC is the -$139,855.22. Since EC is a component of Value Added and Value Added is a component of Output, you also need to add this figure to the Direct Effects of Value Added and Output.

 

Commuting_-_Results.jpg

 

OUT-COMMUTING:

Sometimes you as the researcher might want to look at data on out-commuting from your region.  Out-commuting will show up as a payment from the Foreign Trade (25001) and Domestic Trade (28001) columns to the nine household (10001-10009) rows.  To get a out-commuting rate, divide the sum of these payments (across the 9 household types in both the Foreign Trade and Domestic Trade columns) by the sum of the nine household column totals. For this one, it’s easier to do your calculations in Excel. 

Use the three dots icon to download the IxC Aggregate SAM table. 3_Dots.jpg

You will then add up the totals of AB10 to AC18 to get the total trade value for the first household group.  Then add the totals for each of the nine household categories J30 to R30, to get the total household value. 

So, in our NC example, we take

     $10,786,102,053.85 / $469,722,964,305.19 = 2.30% 

This is the out-commuting rate which again you can calculate using the file Commuting Rate – Calculations by filling in the teal boxes.

 

Commuting_-_Finding_Out-Commuting.jpg

Known Issues in IMPLAN

INTRODUCTION:

This article is designed to offer IMPLAN users some important descriptions of, temporary solutions for, and status updates regarding known technological issues which have been identified and are actively being addressed within IMPLAN. Should you encounter any issues which are not addressed in this article, please let us know by contacting us at (800) 507-9426 or at [email protected] Issues which were previously identified, but have since been resolved, are documented in IMPLAN’s product release notes which are published upon the release of new features and capabilities within the IMPLAN tool.

 

ISSUES BEING ADDRESSED:

Each of the technological bugs described below have been identified and are being addressed (as you read!) by the IMPLAN Product Team. So don’t worry—we’re on it! But, should you encounter any of these issues between now and the deployment of their official fixes, there are workarounds which can provide effective (albeit manual) solutions in the meantime.

 

1. MRIO ANALYSES TAKE LONGER TO FINISH CALCULATING RESULTS

The Issue
An MRIO study requires a much higher number of calculations than non-MRIO studies as the trade of all the sectors are analyzed between regions. As such, we expect these types of studies to take longer to finish than non-MRIO studies. The amount of time it takes to finish will depend on the number of events and the number of groups that the user has entered. The expected time for an MRIO analysis to finish can range between a few minutes for smaller studies to several hours for larger studies.

The Workaround
The best practice for MRIO and other larger studies is to leave yourself lots of time to run the analysis! Keep an eye on the Progress Panel in the lower right corner of the Impact screen that will provide notification of the progress of the study. Notification will be provided if the study has timed out. That can happen for various reasons with the most common being that the size of the study exceeds our processing limits. While we have made great strides in increasing the processing power, you still may see longer wait times. That said, in order to minimize wait times or potential for “timing out” with regard to processing performance, we suggest limiting your MRIO analyses to a) include a maximum of ten Events and b) limit your number of combined Groups to four or five. If your MRIO analysis absolutely must include more than ten Events or Groups which contain more than three Combined Regions, we suggest that you contact your Customer Success Manager at 800-507-9426 or [email protected] to personally discuss potential strategies for managing the size and/or complexity of your study.

 

2. WHEN RUNNING MULTIPLE STUDIES, SMALL STUDIES CAN TAKE AN UNUSUALLY LONG TIME TO FINISH

The Issue
The user kicks off a large study and wants to run a second study while the first is processing. Even if the second study is a small study, it doesn’t finish in the expected time frame.

The Workaround
The best practice for larger studies, including MRIO that we expect to take longer, is to plan ahead. Run smaller studies first since studies that are kicked off after a large study get placed in queue and will not start until the large study has completed. This is to prevent too much data hitting the server at once and crashing the system. The second study analysis will automatically begin without any further action from the user.

 

3. GROUPS WITH MORE THAN 100 EVENTS MAY NOT COMPLETE.

The Issue
Groups with more than 100 events may not complete or may take a long time to complete.

The Workaround
We are still exploring, testing, and discovering the current limits of the tool’s processing power and the amounts of data that it can communicate to our servers at any one time. That said, in order to minimize wait times or potential for “timing out” with regard to large studies, we recommend keeping the number of Events in your Groups to less than 100. If your study requires more, contact your Customer Success Manager by calling 800-507-9426 or emailing [email protected] to personally discuss potential strategies for managing the size and/or complexity of your study.

 

4. FILTERING IN REGION OVERVIEW

The Issue
When looking at the data in the Regions Overview, there is an intermittent issue facing filtering.  In a section that requires the use of a filter to choose a specific Industry or Commodity, the list of possibilities is not always populating after the first use of the search.

The Workaround
To do another search, just navigate back to the same screen and the Industry or Commodity list should be there again.  

 

5. ALL EVENTS WIPING OUT DESPITE SAVING THEM 

The Issue
If the users time out on the impacts screen, all events are being erased even if their work is saved. This occurs when a user is idle for too long causing the project to crash. Our product team is actively working towards fixing this issue. 

The Workaround
We recommend that a user leave the impact screen while the project is running or any other time that they are NOT actually active creating or editing on the page.

 

6. NO NOTIFICATION WHEN AN INDUSTRY DOES NOT EXIST IN A REGION 

The Issue
When running an Industry Event on a Sector that does not have any activity (no Output, no Employment, etc.) in the Region used in the analysis, the user will get all zero results. This is because there is no data or Sector averages for that industry in the Region, which is necessary for IMPLAN to estimate the effect of some change in that Sector within the given Region. IMPLAN does not provide any notification to the user that the Sector does not exist. When running an Industry Event on a Sector that doesn’t exist in the Region in combination with other Events that successfully produce non-zero results, it is very possible to overlook the fact that one of the Events has produced all zero results. 

The Workaround
You can ensure the Sectors you will be impacting exist by checking for data on the given Sector in the Region Details. Study Area Data (Industry Summary table) is a great place to check. Users can model the effects of a new Sector being introduced to a given Region by either first adding the Sector by customizing your Region and then modeling the effect using an Industry Event or by taking an Analysis-by-Parts approach

 

7. THE EVENTS TEMPLATE WILL NOT UPLOAD EVENTS WITH INDUSTRY SPECIFICATIONS GREATER THAN 536

The Issue

When uploading large numbers of events using the events template, industry specifications cannot be greater than 536 sectors. For the 2018 data set and later, this means that industries 537-546 cannot be included in an events template.

The Workaround

We would encourage that you familiarize yourself with our special industries before using them in a study. As industries 537-546 are commodity-only or administrative payroll specifications, it is unlikely that they would be included for a study that makes use of the events template. If you need to include them, these industries can be added manually by clicking “Add New Event.”

 

8. DIRECT OUTPUT EFFECT OF COMMODITY EVENTS NOT EQUAL TO EVENT VALUE WHEN THEY SHOULD MATCH

The Issue

When only Industries are the producers of a Commodity in a given Region (according to the Commodity Market Share), 100% of the Event Value for these Commodity Output Events should generate Direct Output. Therefore, when Dollar Year on the Impacts Screen matches the Dollar Year on the Results Screen the Event Value and Direct Output Effect should be exactly equal. Things are working just fine when Dollar Year and Data Year are the same in the Impact Screen. The issue arises whenever Dollar Year and Data Year on the Impact Screen do not match. In this situation, currently, there is some discrepancy between the Event Value and the Direct Output when there are multiple Industry producers (and no Institutional producers) even though these values should match when Dollar Year in the Impact Screen and Results Screen are the same.

The Workaround

Aside from disregarding the slight discrepancy, a workaround to this issue is to multiply the Commodity Output Value by the Commodity Market Shares to convert the Commodity Output into Industry Output. The portions of Commodity Output produced by Industries can be analyzed as multiple Industry Output Events.  

 

9. CUSTOMIZED REGIONS

The Issue

Chrome is the recommended browser for using IMPLAN.  When customizing a Region, if you are using Edge as a browser, it will not allow any number below one to be entered. 

Once you have completed your Region customization, the Customized Region will not automatically appear in the Selected Region field. 

The Workaround

Once you have completed your Region customization, search for and select the name of the Customized Region in the search field on the Region screen. The Region will be shown in the Selected Region field. If the Region is still building your will see a spinning wheel on the Region window. Once the Region is built, you can check for your customization Behind the i

 

10. NORMALIZATION RESETS UNEDITED LPP’S TO THE SAM VALUE

The Issue

In an industry spending pattern, the option to set a local purchase percentage for each commodity is available. If you uncheck the box labeled “SAM” which sets the LPP for the commodity to the RPC pulled from our Social Accounting Matrix (SAM), the software will update the LPP to 100%. If you uncheck the SAM box to set to 100% and then use the Normalize feature, the checkbox is reset to the SAM value.

The Workaround

The appropriate methodology would be to make edits to your commodities and normalize, then set custom LPP’s if they are known. If you would still like to enter LPP’s before normalization, any commodity that you intend to have a 100% LPP for should have the LPP field cleared, and a “1” entered.

 

11. FALSE ERROR AFTER CANCELING A PROJECT

The Issue

If you are running a project, hit cancel, hit run again, and then navigate away from the Impacts Screen, when you return to the Impacts Screen a coral error bar will appear. There is actually no error, however.

The Workaround

Good news! There is nothing you need to do (aside from ignoring that coral error message).

 

12. COMMODITY EVENTS AND USER-DEFINED LPP

The Issue

When using a Commodity Event, with Total Revenue (default) selected, and changing the LPP to anything besides the default 100%, the change in LPP is not sticking.

The Workaround

This fix is in the works and should be out soon. To adjust for this, only enter the percentage that you want applied to the multipliers in the Event Value.

 

13. COMBINED REGION NAME CHANGE

The Issue

If you have two Combined Regions with the same name, and you run a project using one of them, you may find the name changed when you return to the Impacts screen.

The Workaround

Have no fear on this one. Although the name may have changed, because you have two identical Combined Regions with the same name, IMPLAN just pulled in the other name for it.

 

Total Revenue versus Marginal Revenue

INTRODUCTION:

When a good is purchased via a retailer or wholesaler, the price that is paid (Purchaser Price) for the good differs from the actual Producer Price of the good.  This is because it includes the cost for transportation and the cost of the wholesale and/or retail service of selling the good conveniently to the consumer. The cost of transportation and the wholesale/retail service are called Margins. The Producer Price of a good plus the costs of Margins equals the Purchaser Price of the good. 

TMR1.png

Margins are only applicable when you are modeling Industry Output Events with a retail or wholesale Sector specified and when you are modeling Commodity Output Events with a Commodity specified that can be sold via a retailer or wholesaler. Margins are not applicable to Industry Employment, Employee Compensation, or Proprietor Income Events. When you are in a scenario where Margins are applicable, you can indicate in the Advanced Menu of the Event whether your Event Value is a Total Revenue value, equivalent to Purchaser Price, or if your Event Value is Marginal Revenue, equivalent to Producer Price. When choosing between Total Revenue and Marginal Revenue is not an option, the Event Value is assumed to be the Producer Price. 

TRM2.png

MARGINS IN INDUSTRY OUTPUT EVENTS:

TOTAL REVENUE

Total Revenue represents the increased income resulting from the total sale of goods and services. IMPLAN defines Total Revenue as Total Purchaser Price of Commodities Sold. Total Revenue in an Industry Output Event, with a retail or wholesale Sector specified, indicates to the software that the Event Value is a retail/wholesale sales price (Purchaser Price) and includes Margin Costs for transportation, wholesaling, and retailing services, in addition to the Producer Price of the goods sold. 

In the case of applying Margins by selecting Total Revenue in an Industry Output Event, there is no way for IMPLAN to identify the good/Commodity purchased.  Therefore, the only impact that will be estimated is the portion of the Event Value that gets assigned as the Margin cost associated with the Sector specified in the Event.  This means, when Total Revenue is selected in an Industry Output Event with a retail or wholesale Sector specified, there will only be a Direct Effect in your Results to the Sector specified and it will only be a portion of the Event Value. 

For example, let’s say we know visitors during a week long event in our Region spend on average $15 per day at the gas station. If there are 1,000 visitors spending $15 per day at gas stations in our Region for 7 days, the total spending at the gas stations by these visitors would be $105,000. To model this in IMPLAN we’d be limited to modeling this via an Industry Output Event with the “Retail – Gasoline stores” Sector specified because we do not know the good/Commodity that these visitors are purchasing at the gas station. It is likely mostly gas, but they are likely also purchasing things like snacks and slushies. Because the $105,000 is the total spending by visitors at the gas station, this value is a Purchase Price and we should leave “Total Revenue” selected in the Advanced Menu to apply Margins. 

 TRM3.png

The 2017 Retail Margin Coefficient for the Sector Retail – Gasoline stores Sector is .164.  This can be found in the 2017 COMMON (IMPLAN5) MARGINS.

This means on average 16.4% of Total Revenue at a retail gas station is the retail gas station’s revenue, or Marginal Revenue. This Marginal Revenue is the value of the retail gas stations service of selling goods in a convenient place; in other words Marginal Revenue in this example is equivalent to the retailer’s Output. 

Therefore, when this Event is Run, we’d only see $105,000 x .164 = $17,220 of Direct Output to the Retail – Gas Store Sector and no other Direct Effects (when Dollar Year = Results Dollar Year). The other $87,780 of spending at the gas station would be a leakage in this analysis because without knowing which Commodities were purchases with the $105,000, IMPLAN cannot appropriately estimate what portion of the $87,780 is the Producer Price of Commodities and what portions of this value is the transportation cost and wholesaling cost. 

Note: When additional Event Values are entered in the Advanced Menu (Employment, Employee Compensation, and/or Proprietor Income) IMPLAN assume that even when Total Revenue has been selected in the Industry Output Event, these values can only be known for the Sector specified, the retailer or wholesaler. Therefore, no Margins will be applied to these values. Margins are only applicable to the Output Event Value. 

MARGINAL REVENUE 

Marginal Revenue represents the increased income resulting from the sale of one or more unit of goods and services. Marginal Revenue in an Industry Output Event with a retail or wholesale Sector specified indicates to the software that the Event Value is a only the Producer Price of the goods sold, and does not include any Margin Costs for transportation, wholesaling, and retailing services. 

By selecting Marginal Revenue in an Industry Output Event with a retail or wholesale Sector specified, you are indicating to the software that no Margins should be applied, and therefore the full Event Value will be applied to the multipliers of the Sector specified, just as the software would treat the Event Value for a non-retail/wholesale Industry Output Event. Although it is possible to know the Marginal Revenue to a retailer, it is much more common to know the Purchaser Price paid at the retail/wholesale establishment than the Producer Price.

MARGINS IN COMMODITY OUTPUT EVENTS:

TOTAL REVENUE 

Total Revenue in a Commodity Output Event indicates to the software that the Event Value is a retail/wholesale sales price or Purchaser Price and includes Margin Costs for transportation and wholesaling/retailing services in addition to the Producer Price of the goods sold. 

There is a difference between applying Margins by selecting Total Revenue in Industry Output Events and Commodity Output Events. When the item purchased is known you should use a Commodity Output Event. Total Revenue is still the appropriate selection when only the price paid at the retailer/wholesaler is available to you. Instead of specifying the retailer or wholesaler where the item was purchased, the Commodity purchased should be specified. Margins cannot be applied in Industry Events when a non-retail/wholesale Sector has been specified. 

This is very different than the previous gas station example when we only knew that $105,000 was spent at the gas station.  Now, let’s assume we know $100,000 of the $105,000 is spent specifically on gas at the gas station. By using a Commodity Output Event, specifying Commodity 3156 – Refined petroleum products and leaving “Total Revenue” selected in the Advanced Menu, IMPLAN estimates the impact of the full Event Value, unlike in the case of applying Margins to an Industry Output Event. IMPLAN will use Commodity margins to distribute the Total Revenue to each producer in the Value Chain. This process converts the provided Total Revenue into Marginal Revenue values for the producer, transporter(s) and wholesale/retailer(s).   

When taking this approach, it is recommended to check the “SAM” box in order to set your Local Purchase Percentage (LPP) to the Regional Purchasing Coefficient (RPC) as shown below. This will set the LPP for the production of each Commodity in the Value Chain to the Regional Purchasing Coefficient for the respective Commodity. This tells IMPLAN to assume each Commodity will be purchased locally at the same rate in which each Commodity was on average purchased locally during the Data Year.

 TRM4.png

2017 Margin Coefficients for the Refined petroleum product Commodity: 

Sector

Margin Sector

Margin Coefficient Value

3156

3156 

0.63

3156

3395

0.19

3156

3402

0.17

3156

3408

0.00002

3156

3409

0.001

3156

3410

0.002

3156

3411

0.008 

3156

3413

0.005

Total

 

1.00

Data can be found in the document  2017 COMMON (IMPLAN5) MARGINS.

When examining the Margins for Commodity 3156 -Refined petroleum products, we see the full list of Margins above. The Margin from 3156 to 3156 shows the Margin Coefficient Value as 0.63. This means 63% of the Total Revenue is Marginal Revenue/Producer Price of refined petroleum products (gas) purchased at the gas station. 

Commodity 3395 – Wholesale trade distribution services represents 19% of the total cost. Commodity 3402 – Retail services – Gasoline stores gets allocated 17% of the retail purchase of the gas.  The same goes for Commodities 3408-3413; all transportation sectors. 

Therefore when this Event is Run, we see Direct Output Effects to each Sector that produces the above Commodities in the Region. 

To illustrate this, here are the Detailed Direct Output Results of this Event analyzed as if the gas station in our example is located in Mecklenburg County, NC (2017 Data Year): 

TRM5.png

These Direct Output Effects sum to $36,403.14 rather than the full Event Value modeled, $100,000, largely due to the setting of LPP to SAM. Notice there is only $52.52 of Direct Output in Petrochemical manufacturing and $14.15 in Extraction of natural gas and crude petroleum (both producers in the Region of refined petroleum) even though the Producer Price for the gas according the Margins was $100,000 x .63 = $63,000. This is because in Mecklenburg County, NC 2017 the RPC for refined petroleum is only 0.11% (found in Region Details > Social Accounts > Reports > Commodity Summary, in the Commodity Averages Summary table see “Average RPC” column). The remained of the $63,000 is a leakage, suggesting most of the gas pumped in Mecklenburg County is produced outside of the Region. The largest Direct Output Effects are to the Wholesale and Retail Sectors because these services are the most locally available portion of the Value Chain in this example. Nonetheless, this approach captured much more of the local impact of the hypothetical visitor gas spending than using an Industry Output Event in the Retail – Gasoline stores Sector. When we took the approach of using an Industry Output Event in the Retail – Gasoline stores Sector the only Direct Output Effect was to the specified retailer of $17,220 based on a Total Revenue of $105,000 (based on $100,000 of Total Revenue this value would have been $100,000 x .164 = $16,400). Notice here in the case of our margined Commodity Output Event, Direct Output Results are quite similar for Retail Gasoline stores at $16,523.72, shown above (based on a Total Revenue of $100,000). In addition to the Direct Effect on the gas station Sector, we also see the local Direct Effects to gasoline producers, transporters and wholesalers.

MARGINAL REVENUE 

By selecting Marginal Revenue in a Commodity Output Event, you are indicating to the software that no Margins should be applied, and therefore the full Event Value will be analyzed as Output of the specified Commodity. 

Marginal Revenue is most commonly the appropriate selection in a Commodity Event when the Commodity specified in the Event was not purchased via a retailer or wholesaler, even though it is available via retail/wholesale sellers. For example, large corporations often source goods directly from manufacturers, but households would purchase these same goods via a retailer. 

Let’s reconsider our example of retail spending at the gas station. If we knew the price of the gas produced by the petroleum refinery before the gas was shipped out, we could enter this Producer Price as the Event Value of our Commodity Output Event with the Refined petroleum products Commodity specified, and select “Marginal Revenue” in the Advanced Menu of the Event.  Again, it is possible to know Marginal Revenue to a producer, but it is much more common to know the Purchaser Price paid at the retail/wholesale establishment than the Producer Price.

MARGINS IN SPENDING PATTERN EVENTS:

Spending Patterns in IMPLAN are made up of a list of Commodities, so think of Spending Patterns as a grouping of Commodity Events. The key difference between a Spending Pattern and a grouping of Commodity Events is that the Spending Pattern Event will not produce a Direct Effect. Learn more about using Spending Patterns and groups of Commodity Events in the context of Analysis-by-Parts here

Another variation between Spending Pattern Events and Commodity Events is that the value of each Commodity within a Spending Pattern can only be edited by updating the coefficient associated with the given Commodity. The coefficient on each Commodity reflects the spending per dollar of Intermediate Expenditures (by default) or Output, depending on what is selected in the Advanced Menu of the Spending Pattern Event. There is no option to apply Margins to the Commodities within a Spending Pattern Event because they are already marginned. As mentioned, when Margins are not applicable the Event Value is assumed to be the Producer Price/Marginal Revenue. The same is true for the coefficients associated with the Commodities of a Spending Pattern Event. 

Because Spending Patterns in IMPLAN are marginned, the coefficient for each Commodity in a Spending Pattern reflects the portion of spending that will be allocated as the Producer Price of each Commodity. Therefore, the coefficient on retail/wholesale Commodities in a Spending Pattern reflects the portion of spending that will be estimated as the Marginal Revenue to the given Commodities. 

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

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 [email protected], or call us at (800) 507-9426.