*NOTE* IMPLAN applied a code modification to the Multi-Regional Input-Output (MRIO) Analysis feature on December 14, 2022. Beginning on October 20, 2022, the MRIO feature was undercounting the first-round of Intermediate Input purchases - or supply chain purchases - from any of the linked Regions in the analysis. This may have resulted in smaller Indirect and Induced Effects on projects run during that time period. The code modification resolved this issue. If you believe this may have impacted your Project Results, re-running the analysis will produce the correct results. We apologize for the inconvenience to any impacted users.
Multi-Regional Input-Output (MRIO) analysis makes it possible to track how an impact on any of the 546 IMPLAN Industries in a Study Area region affect the production of all 546 Industries and household spending in any other region in the US (state to state, county to county, zip code to zip code, county to multi-county, county to state, etc). Now you can demonstrate how an impact in your Study Area disperses into other regions and see how these effects in surrounding areas create additional local effects.
Let’s say a new bank is opening up a HQ in your county and the state gave them a huge tax incentive to locate there. Using MRIO you can show each county how the bank locating in one county will also impact their county, thus making it a valuable investment of state tax dollars.
We live in regional ecosystems and when running an impact analysis you can see how much money leaks out of your city through trade and commuting. Using MRIO you can see where some of that leakage ends up - supporting other cities across the county or state.
There are many reasons you may want to consider using MRIO.
- To improve the methodology of your study
- To improve the regional specificity and limit aggregation bias
- To examine the interconnectedness of multiple regions
- To track leakages from a study region and determining the impacts they create in other regions
HOW MRIO WORKS
MRIO expands backward supply linkages beyond the boundaries of a single-region Study Area. MRIO analyses utilize interregional commodity trade and commuting flows to quantify the demand changes across many regions stemming from a change in production and/or income in another region. This powerful analytical method allows analysts to go beyond a single study region, measuring the economic interdependence of regions.
In an MRIO analysis, the Direct Effect in one region, Region A, can trigger Indirect and Induced Effects in linked regions, capturing some of what would have been a leakage in a traditional I-O model.
Let’s say a new firm is opening in Region A. Some of the construction inputs may be produced in linked Region B and are imported into Region A. Through this trade there is a production change in Region B that triggers a whole new change of spending in Region B. If the construction materials produced in Region B require an input produced back in Region A, thus creating a new branch of backward linkages in Region A.
As always jobs supported by the new production and the affected supply chain earn income, but potentially workers in Region A will live in Region B and visa versa. As dollars trickle to household spending, there is likely trade between the regions in the supply chain. For example, restaurants in region A frequented by the workers that reside in region A may buy produce from a farmer in Region B. The income earned by workers on the farm would trigger a new chain of labor income. Some of the farm workers may live in Region A, so household spending cycles through both regions.
MRIO IS NONLINEAR
Because MRIO must be calculated based on the trade flows between the linked Regions, dollars are bouncing between the MRIO Regions until the spending on each Commodity reaches a threshold of $100. This means the larger the input, the longer it will take to meet the threshold of $100 on each Commodity traded, allowing for more bounces between linked Regions.
So, if you were to analyze an Event Value through an MRIO analysis, then increase that Event Value by a multiple of five, you should expect to get Results slightly larger than five times the original Results as opposed to exactly five times the original Results that you'd expect to see in a non-MRIO analysis.
STEP 1 – SETTING UP THE REGIONS
Charlotte, NC is the second largest financial center in the US. For this example, let’s say a new bank will be opening and you want to see the effect not only in Mecklenburg County, NC, but also in neighboring York County, SC.
First, on the Regions screen, select both Mecklenburg County, NC and York County, SC. Click Create Impact. In some cases you may want to create Combined Regions to build a clustered surrounding area. For example, we want to additionally see the effect on the surrounding Charlotte MSA within North Carolina in which case we could select all the Counties in the Charlotte MSA in NC except for Mecklenburg County and Combine these Regions to form 1 new Region. We must exclude Mecklenburg if we are including it as its own Region, otherwise the effects in Mecklenburg will be double-counted in our MRIO Analysis. Combining Regions is beneficial especially within an MRIO Analysis because it will reduce the analysis run time.
STEP 2 – SETTING UP THE EVENT
Create an Industry Output event in Industry 441 - Monetary authorities and depository credit intermediation for $500M. This Industry was chosen because the primary function of the HQ in this instance more resembles a financial institution than the traditional HQ functionality. If the firm was following a traditional HQ, then Industry 469 - Management of companies and enterprises, would be the correct choice. For more information, visit the US Census Bureau.
Save the Event and drag it into the Mecklenburg County group on the right side of the screen. No event will be added to the York County group as the bank will operate in Mecklenburg.
Ensure that the MRIO checkbox at the top of the screen is checked.
Click Run. When the analysis is complete, click View Results.
STEP 3 – VIEWING THE RESULTS
When you look at the Results screen, you see the Total Direct Output of $500M which has an indirect effect of $141M and an induced effect of $83M, for a total economic impact of $725M. These results include both the impacts on Mecklenburg County, NC and York County, SC.
Mecklenburg County & York County Impact
In order to see how this new bank will affect its home county of Mecklenburg, we need to Filter our results. In the Region box, choose Mecklenburg and then hit the run button on the right.
The Total Direct Output remains $500M, because the bank will be located within Mecklenburg. However, the indirect and induced effects are only showing the activity within this county, so you see a total of $716M in Output impact.
Mecklenburg County Impact
If you change the Region Filter to York County, SC and hit run, you see the impact that the new bank in NC will have on York County, SC. Notice that there is no Direct impact. You do see a total Output impact of almost $5M. This is money that will flow into York County because of the bank operations in neighboring Mecklenburg.
York County Impact
If you add the Economic Indicators from Mecklenburg and York counties, you will get the total Output impact of $725M seen before the Filter was applied.
MRIO works best with up to ten regions. When possible, create aggregated regions to examine the effects on the other areas. For example, to look at the economic impact on Mecklenburg County and the remainder of North Carolina, create a region of the remaining 99 counties.
Every region has a unique set of commuter rates, trade flows, and region specific identities (Output per worker, Intermediate Expenditures to Value Added, etc.). Therefore, you will see different results if you run an impact on a combined Region A + Region B versus an MRIO on Region A and Region B.
Note also that when the LPP is set to SAM or anything less than 100%, the leakage is not traded with the linked regions when using MRIO. If you know that some of that leakage is occurring in a linked region, we recommend analyzing that value in the linked region explicitly.
Written July 10, 2019
Updated September 25, 2023