Advanced Group Design: Getting More from Your Groups

This article covers four advanced workflows for organizing Groups to maximize your analysis. Each advanced workflow expands the analysis into a project complex enough to show each technique. Picking up where the What is a Group? article leaves off, it offers a continuation of the Heartland and Music Festival example. 

In that article, we built a project with six Events organized into three Groups:

• Visitor Spending: Includes four visitor Commodity Output Events (hotel, restaurant, transportation, entertainment), scaled to represent all festival visitors.
• Venue Construction: Models the one-time construction of a performance venue.
• Festival Operations: Represents the ongoing annual operations of the festival.

Today we'll start from this foundation and grow the project one capability at a time, demonstrating more advanced modeling capabilities, all driven by our Group setup. 

OVERVIEW: GROUPS CARRY MODELING RULES

A Group is often introduced as the place where your Events get an economic address — the Region, Data Year, and Dollar Year that tell IMPLAN where and when your activity happened. That framing is correct, but it undersells what a Group can do. A Group is also a boundary for modeling rules, therefore several advanced workflows open up that a single-Group project never reaches.

ADVANCED GROUP WORKFLOW USING THE HEARTLAND FOOD AND MUSIC FESTIVAL

The four advanced workflows that will be covered in the article are: 

1. Isolating an Industry Contribution Analysis (ICA) so its buyback restriction doesn’t contaminate the standard impacts running alongside it.
2. Linking regions with MRIO to trace how spending in one region spills into a neighboring economy that supplies it.
3. Duplicating and versioning Groups to spin off scenario variants without rebuilding Events from scratch.
4. Running controlled comparisons across time, holding the activity constant so any difference in results is attributable to the change you made.

Note: All four scenarios are connected by a single principle: Events that require different modeling treatments belong in different Groups. Designing your Group boundaries deliberately is what separates a routine analysis from an advanced one.

ISOLATING AN INDUSTRY CONTRIBUTION ANALYSIS

Our first advanced modeling technique answers a different kind of question than the rest of the project. So far, every Event models a change such as new visitor spending, a new venue, ongoing operations that bring new money into Missouri. 

Suppose the client also wants to know something about the economy as it already stands: how much does Missouri’s existing performing-arts industry contribute to the state economy right now?

That tells us that this analysis is a contribution, because the existing footprint of an industry already embedded in the region and it’s measured with an Industry Contribution Analysis (ICA) Event.

Why ICA Needs its Own Group

Traditional impact modeling presumes that fresh demand cascades infinitely throughout a local supply network. Conversely, evaluating the existing footprint of an industry within its native regional economy requires preventing that sector from repurchasing its self-generated commodities. This circular feedback loop can artificially inflate total economic output. IMPLAN eliminates this distortion using built-in ICA constraints.

This analytical boundary governs the complete Group rather than individual Events. 

Consequently, any additional Event within that specific Group automatically adopts those identical modeling restrictions.

For this reason, the performing-arts contribution cannot simply be placed alongside Festival Operations even though both center on Missouri’s performing-arts sector. While Operations measures traditional economic impacts, the contribution assessment employs an ICA. Placing both Events within the same Group would distort the operational modeling, making it necessary to keep them strictly isolated.

Step 1: Add the ICA Event

Rather than entering an explicit dollar figure, we specified a 25% contribution percentage. Both approaches are valid: you may input a direct Industry Output value as long as it remains within the region's total industry output or designate a proportion up to 100%. Utilizing a percentage often proves more streamlined, as it eliminates any risk of accidentally overstating the sector's baseline regional capacity.

Note: Industry Contribution Events take IMPLAN Industries as Event Specifications. Though our Festival Operations Event uses Commodity 3478–Performing Arts; the matching industry is 478–Performing Arts Companies.

Step 2: Create a Dedicated ICA Group

The ICA Event sits alone in its own Group. Because the buyback restriction applies to every Event in a Group, isolation keeps the visitor and operations impacts clean and independently interpretable. 

Notice that this new Group utilizes the same geography, Missouri, as our primary trio of Groups. This design is fully supported: Groups derive their identity from their designated role and titling, rather than spatial boundaries alone. (This core behavior becomes central to our upcoming methodology.)

Step 3: Assign the ICA Event to that Group Only 

Our final step is to assign the ICA Event to it's designated group. Separating the ICA event from other events keeps the ICA restrictions isolated to that event only. 

WHEN WOULD YOU COMBINE AN ICA EVENT INTO THE SAME GROUP?

Suppose you don’t want the performing-arts contribution (ICA) and the Festival’s Operations as two separate numbers. Instead, you want to know, “What is the total contribution of the region’s existing live-events sector (the performing-arts industry and the Festival’s own operations) together?”

In that framing, Festival Operations aren't new money rippling outward without limit; they’re part of the existing footprint being measured. For this reason, we want both Events under the buyback restriction, in the same Group, because the shared rule is now the intended modeling treatment.

The two arrangements answer separate questions: 

1. Separate Groups: “How much does the performing-arts industry contribute?” and, independently, “What is the impact of festival operations?” Two clean, independently interpretable answers. Here, mixing them would be an error.
2. One shared Group: “What is the combined existing contribution of the region’s live-events sector, operations included?” One answer, where every Event should carry the ICA restriction.

In the later case our modeling workflow is simplified because we no longer need a separate ICA group: 

1. Create out ICA Event 
2. Drag the ICA Event into the Existing Festival Operations Group
3. Run the project and view Results

VIEWING RESULTS

With any multi-event, multi-group IMPLAN analysis, the default results pages report the total impact of the project: 

To directly answer the client's question of how much the existing preforming arts industry contributes to Missouri's economy, we need to filter on the ICA Event.

LINKING REGIONS USING AN MRIO

WHY MULTI-REGIONAL INPUT OUTPUT (MRIO) IS A RELATIONSHIP BETWEEN GROUPS

Every rule we’ve discussed so far operates inside a single Group. The ICA buyback restriction, Group Scaling, the Data Year each shapes how the Events within one Group behave. MRIO differs, because an MRIO is a rule that operates between Groups.

You represent each region as its own Group, then switch MRIO on for the entire Project. IMPLAN then traces the economic activity that leaves one Region’s Group and lands in other Regions’ groups. In an MRIO project, a Group is no longer just “where the Events happen”  it becomes one node in a linked system of regional economies.

For this next scenario, we will start with our three original Groups–Festival Operations, Venue Construction and Visitor Spending. The first three Groups all model activity landing inside one economy, but in reality spending doesn’t respect a boundary line. In previous examples, we modeled the festival in a general region of Missouri. Now we're going to more precisely specify the festival’s location and model the spillover effects on the rest of the state. 

Key Rules for Multi-Regional Input-Output (MRIO) Modeling:

1. Unique Region per Group: Each Group must represent a unique area. Sharing a Region triggers the error: “the following Region(s) are included in multiple Groups.”
2. Events belong exclusively in the Group where direct activity occurs. Secondary Groups stay empty to capture supply chain spillover (e.g., all festival Events sit in Jackson County, leaving Rest of Missouri empty).

ONE HOST REGION, SPILLOVER TO THE REST OF THE STATE

Suppose the Heartland Music and Food Festival occurred in Jackson County, Missouri and you want to know, “How much of the festival’s economic activity actually benefits the rest of Missouri, such as the suppliers and workers outside Jackson County who feed the event?” 

An MRIO analysis allows use to directly answer this question, looking at how much of the festival’s economic activity remains within the local host area, and what portion migrates outward to supporting businesses throughout remaining parts of Missouri.

In this scenario, Jackson County is the origin of spending. As visitors spend money within Jackson County, a portion of those funds promptly moves outside the county to suppliers, distributors, and employees operating in other parts of Missouri. Standard single-region modeling treats these external flows as economic leakage, halting further tracking. In contrast, Multi-Regional Input-Output (MRIO) analysis captures these dynamics by mapping how initial expenditures in one area propagate through secondary, supplying regions.

Because a single-region setup cannot evaluate these cross-border effects, proper modeling requires establishing two geographically distinct, interconnected regions that enable IMPLAN to follow trade flows between them. 

In this framework, the festival in its entirety is housed within a single host county by consolidating all designated Events into a single “Jackson County Group”. A secondary Group titled Rest of Missouri contains no direct Events, serving purely as a receiving node to capture indirect trade leakage across the supply chain:

• Region 1 — Jackson County: The location hosting the festival and where direct spending takes place.
• Region 2 — Rest of Missouri: The supporting economy that captures regional spillover effects.

Note: MRIO mandates that each group represent a unique geographic area with no overlapping boundaries. Consequently, all festival activities must be assigned exclusively to one single Jackson County Group.

Step 1: Move ALL Events To Primary Group 

To setup up our MRIO, we must move all event to a single Jackson County, Group to avoid introducing overlapping geographies to our model.

Consolidating all Events into a single Jackson County Group alters the core focus of the analysis in two key ways:

1. Combined Group-level totals: Placing visitor spending, site construction, and annual operations in a single Jackson County Group means the Group-level results sum one-time capital investment impacts with recurring operational impacts. These totals shouldn't be reported as a single annual footprint, because the distinction between one-time and recurring activity matters for public policy and strategic planning. The single-Group structure is therefore a reporting consideration, not a limit on the analysis.
2. Shift in the primary subject: The analytical focus transitions from evaluating the festival itself to analyzing Jackson County's economy as it absorbs a consolidated sum of economic activity. Consequently, the festival moves from being the primary unit of study to serving as an input into the county-level model.

Note: In our analysis all events happen in Jackson County and we have a single Group with Events. However, it is important to note that MRIO does not limit our economic activity to one region. It only requires that our Groups do not contain overlapping geographical regions.

Step 2: Add Groups For Spillover Regions

In single-region analysis, it's only necessary to identify the region of direct economic activity. However, with MRIO we must now also consider the geographical scope of our spillover effects. In same cases this may be a single region, in other's it may be many regions, depending on our stakeholders.

For our example, we want to capture the impacts to Jackson County and the spillover effects to the "Rest of Missouri". We can do this by adding a combined region, containing all countries except Jackson County.

The Region will be added to the right hand side and you can now click ADD TO IMPACTS. 
 

This Group operates as a receiving economic area designed to capture indirect spillover effects. Even without direct Events, it acts as a connected regional node, offering IMPLAN a structured target for inter-regional trade flows.

Step 3: Enable MRIO for the Project

Check the MRIO box at the top of the Groups panel. This is a single Project-level switch, not a per-Group setting and it turns on multi-regional linkage across all the Project’s Groups at once.

Step 4: Run and Read the Results by Region

Run the analysis, then read the Results by Region. You can now see how much of the festival’s impact stayed in Jackson County versus how much landed in the rest of Missouri through the supply chain:

Note: There are no events in “Rest of Missouri” Group, so there is no option to filter by Group Name. However, you can filter by “Jackson County.”

DUPLICATING MULTIPLE GROUPS

THE SAME FESTIVAL RUNNING EVENTS ACROSS THREE COUNTIES

Consider an expanded scenario where the festival spans operations across Jackson, Boone, and St. Louis Counties. The question now becomes, “How do economic flows originating within a given county spill over into the neighboring regional economies?” 

For this scenario, every county operates as a dedicated Group tied to its own Region, acting simultaneously as a direct origin of spending and a receiving node for economic spillover.

Duplicating groups provides a convenient way to do this without leaving the Impacts Page. 

Step 1: Remove the "Rest of the Missouri Group" that has empty events. 

Now that we have three dedicated regions, we can no longer use the “Rest of Missouri” Group, because it includes Boone County and St. Louis County. 

Step 2: Unselect MRIO and duplicate the Jackson County Group twice. 

To maintain our focus on structural Group architecture, we allocate the total economic activity equally among the three counties by assigning each jurisdiction an identical portion of operational expenses, site development, and attendee expenditures. 

Step 3: Change Duplicate Regions to “St. Louis County” and “Boone County” 

Establish three dedicated Groups, one for each county where the festival will take place. This requires updating both the Group Title and Group Region. We've labeled our groups Jackson County – Festival, Boone County – Festival, and St. Louis County – Festival, anchoring each to its respective county. 

Step 4: Enable MRIO checkmark, then click Run. 

Same Events, different Regions.

EXTENSION: MAINTAINING AN EMPTY SPILLOVER GROUP

We can still model the spillover effects to the “Rest of Missouri” but we need to create a new combined region that excludes all three primary counties (Boone, St. Louis, and Jackson). To do this, we create another customized Region, this time removing Jackson County, Boone County and St. Louis County from our selected counties. We’ll give this region a different name, so we’re able to easily distinguish this region from our earlier region.  
 

The Project now has 4 Groups again:

Leave the Group empty with no Events:

After running the event, you’re now able to filter throughout Regions to view spillover effects, not only to the individual counties but also to the rest of Missouri:

Both configurations rely on identical mechanics: 

1. Anchoring Groups to unique spatial boundaries. 
2. Placing Events where economic activity occurs.
3. Enabling project-level MRIO. 

The key difference lies in Group architecture, which is a strategic design choice that determines your model's analytical focus. Reconfiguring the same Events unlocks distinct analytical perspectives.

RUNNING CONTROLLED COMPARISONS 

While prior methodologies adjusted the geographic setting of the event, this framework alters the temporal dimension. 

By holding the underlying operational inputs constant and shifting a solitary time variable, any resulting variation in output is directly traceable to that specific adjustment. This isolated methodology establishes a true controlled comparison: identical Events, uniform values, and static regional boundaries, modifying only a single target parameter.

WHY TEMPORAL COMPARISONS CANNOT BE MRIO

This warning highlights a core constraint within IMPLAN: Multi-Regional Input-Output models mandate a uniform Data Year across all participating Groups. Modifying the Data Year to 2023 for one set and 2024 for another breaks this alignment, disabling the MRIO checkbox and preventing multi-regional execution.

While Multi-Regional Input-Output analysis alters geographic parameters while holding temporal variables static, controlled comparative modeling fixes the spatial boundary and adjusts time frames. These two methodologies serve opposing functions and cannot run concurrently. Consequently, establishing unique identity criteria across identical territories by varying the Data Year, Dollar Year, or Group name is what enables a methodologically sound comparison.

Maintaining a static Dollar Year ensures output differences reflect shifting underlying supply chains rather than price level shifts. Comparing 2023 and 2024 demonstrates how evolving economic conditions alter the overall footprint.

SAME EVENT OVER MULTIPLE YEARS

Suppose you ask, "Would this same festival produce the same economic impact regardless of which year's economy it lands in?"

Economies aren't static. The industry mix, supply-chain relationships, and multipliers embedded in IMPLAN's data shift from one Data Year to the next. To isolate that effect, we hold everything else constant and expose the identical festival to two different Data Years.

For this type of analysis, we're going to keep the Dollar Year constant and vary only the Data Year.

This is the crux of the comparison. We keep the Dollar Year the same (2026) across every Group, so the dollar values mean exactly the same thing in each. What we change is the Data Year, 2023 versus 2024 , which swaps the underlying economic structure the festival is modeled against.

If we changed the Dollar Year instead, we'd be measuring deflation — how price levels move between years — not how the economy itself absorbs the activity. Holding Dollar Year fixed and moving only Data Year isolates the structural change in the economy as the single variable.

Step 1: Uncheck MRIO

Confirm MRIO is unchecked. With different Data Years coming, it must be off.

Step 2: Duplicate each County Group so you have 6 Groups total

Note that when you duplicate the Groups, the Events placed in the original Groups are copied to the new Groups. This is ideal for this scenario, as we want to compare the impacts of the same events occurring across two different economies.

Step 3: Set Data Years

To compare how impacts might vary between 2023 and 2024, we're going to update the Data Year on one set of the county Groups to 2023, keeping the dollar year consistent across all Groups. Changing the Data Year is what allows us to compare how our impacts might vary across different economies. 

When changing the Data Year, rename each Group so the year is legible on the Results screen (2023 Jackson County, 2024 Jackson County, and so on). This naming convention will make it easier to filter and review results. In addition, because same-region Groups are distinguished by name and year, these titles are doing analytical work, not just labeling.

Step 4: Run the Project and Review the Results

On the Results screen, filter by Group Name. The dropdown lists all six Groups by year, and you select the trio for one year at a time — the 2023 Groups together, then the 2024 Groups together.

By default, the Results page combines all Groups in the project, both the 2023 and 2024 runs, into a single total, with every value expressed in the selected Dollar Year (2026). This combined view is useful when you want the cumulative impact of an event across multiple years. In this example, though, the goal is to compare one year to the next, so a summed total would hide the change we're trying to see. Instead, use the Group Name filter to review the 2023 Groups and 2024 separately.

2023 Results: 

2024 Results: 

Because the Events, values, regions, and Dollar Year are all identical across the two runs, any difference between the 2023 and 2024 totals is attributable to one thing only: the change in the underlying economy between those Data Years. 

This enables us to compare the economic impact of the festival for every year. We can only view the effects one year at a time. 

CONCLUSION: ONE SET OF EVENTS, MANY ANALYSES

Starting with a simple setup of six Events distributed across three Groups, the core definition of the festival remained completely unchanged throughout. The underlying activity didn’t change. What changed was the architecture of the Groups around it and each new arrangement asked the model a different question:

1.  Isolating an ICA used a Group boundary to quarantine a modeling rule — the buyback restriction — so a contribution and an impact could be measured without contaminating each other.
2. Linking regions with MRIO used Group boundaries as the edges of a system, tracing how spending in one region spills into the economies that supply it.
3. Duplicating Groups let us stand up an entire multi-region or multi-year structure from work already done, without rebuilding a single Event.
4. Controlled comparisons held the activity perfectly still and moved one variable — the Data Year, so the difference in results could be attributed to that one change and nothing else.

None of these scenarios require new Events. They required thinking about Groups deliberately as the unit of analytical control rather than as folders to sort Events into after the fact.

Achieving these outcomes does not demand creating new Events. Instead, it requires using Groups as dynamic tools for analytical control rather than basic organizational folders.

Before expanding your event list, consider whether a refined Group structure would better serve your project. By organizing existing Events under appropriate rules and Group configurations, you can unlock insights that remain inaccessible within a single-Group setup. Ultimately, the core value of Groups lies in transforming a static set of activities into a multitude of targeted analyses through deliberate design.

The real power of Groups shows that the same activity, structured deliberately, becomes many analyses.

RELATED READING

What is a Group in IMPLAN? Why Groups are More Important Than You Think

Introduction to Multi-Regional Input-Output 

Combining Regions

Economy Changing Over Time

Written September 30, 2026