Getting the sector and margins right is only one pitfall among many that can make or break an economic impact analysis. To learn more about other obstacles to avoid, watch Identifying Economic Impact Definitions and How to Avoid Common Pitfalls.

There are four margins which affect the cost of a commodity:

1. Retail Margin: The operational cost of the retail store. This is the portion of the cost that the retailer keeps to operate their store, pay their workers, pay taxes, and hopefully make profit.

2. Wholesale Margin: This is the portion of the total cost the wholesaler keeps for their operational expenses.

3. Transportation Margins: This is the portion of the total cost the various transporters keep to move products from their production site to a distributor and from the distributor/wholesaler to the retailer.

4. Cost of Production (or Producer Price): The value of the product when it leaves the 'factory' floor.

Let’s See it in Action

Here’s a look at several scenarios, what sort of impact they describe, and the different results possible from a similar impact.

Manufacturer’s Marginal Revenue Industry Output Impact

If we increase industry output by $1,000,000 in the Non-upholstered wood household furniture manufacturing (IMPLAN Sector 370) in North Carolina, we’ll see a total economic impact of $1,805,702. In this scenario, the model assumes that there is new spending of $1,000,000 on goods produced by non-upholstered wood household furniture goods  (in producer price).

Retailer’s Total Revenue Industry Output Impact

Here’s what happens when the output for Retail - Furniture and home furnishings stores (IMPLAN Industry 397) is increased by $1,000,000 in total revenue in North Carolina. In this scenario, the model assumes that there is new spending of $1,000,000 in purchaser price on goods sold by furniture and home furnishings stores, such that IMPLAN applies the retailer margin of 48.2% to the purchaser price to estimate the retailer’s output.

Retailer’s Marginal Revenue Industry Output Impact

Here’s what happens when the output for Retail - Furniture and home furnishings stores (IMPLAN Industry 397) is increased by $1,000,000 in marginal revenue in North Carolina. This assumes the $1,000,000 is known to be the retailer’s output, such that no margins are applied.

Total Revenue Commodity Output Impact

That brings us to following a commodity itself through the economy as opposed to comparing industry output impacts.

Here’s what happens when the output for Non-upholstered wood household furniture (IMPLAN Commodity 3370) is increased by $1,000,000 in total revenue in North Carolina. This assumes that all $1,000,000 is spent in purchase price for the commodity itself and includes the 4 marginal values listed above. This is what it would look like to purchase the commodity from a retailer, according to IMPLAN’s margins.

These results differ from the increases in $1,000,000 in marginal revenue in the corresponding Sector (IMPLAN Sector 370) due to the fact that the commodity itself is not produced only by Sector 370 but also by other industries. Also, some production of the commodity on average is not sold in the data year and is stored as inventory instead.

Marginal Revenue Commodity Output Impact

Here’s what happens when the output for Non-upholstered wood household furniture (IMPLAN Commodity 3370) is increased by $1,000,000 in marginal revenue in North Carolina. This assumes that all $1,000,000 is spent on the product and only includes cost of the goods purchased. This is what it would look like to purchase the commodity straight from the manufacturer.

Note that the total impact for marginal revenue on a commodity differs from that of a total revenue on a commodity because this type of impact does not apply margins for transportation and other costs.

Let’s Get Real

These sometimes slight changes in total economic impact can seem minuscule or amplified depending on the industries and geographies in question—and by what you’re trying to model. Take, for example, B. B. R. Jablonski, T. M. Schmit, and D. Kay’s study from 2016, Assessing the Economic Impacts of Food Hubs on Regional Economies: A Framework that Includes Opportunity Cost.

The challenge with examining food hubs as distinct to an industry spending pattern inherent to the underlying data of an IO model is that you’ll need to figure out food hubs’ specific unique purchasing habits. This means determining how much food hubs would pay for goods and services while also handling their own product transportation needs. In other words, the framework for modeling food hubs as a unique industry requires defining well-researched food hub margins rather than relying on the modeling system’s underlying assumptions to calculate marginal costs for you.   

“In both models, the expenditures are margined in IMPLAN’s retail trade and wholesale trade sectors, and the technical coefficients are adjusted accordingly. Specifically, in our aggregation scheme, three sectors of RA expenditures require margining: retail store-gasoline stations, wholesale trade, and other retail trade. To account for margining in retail store-gasoline stations (sector 326), we apply IMPLAN’s margin of 14.5 percent to the total retail fuel purchases. Consequently, $54,438 is included in retail store-gasoline stations. The balance, $320,998, is mapped to the production sector (petroleum refineries, sector 115), and the local purchase percentage is taken from IMPLAN for that sector (i.e., we multiply the local purchase percentage for petroleum refineries by $320,998). The same approach is used for the other retail trade and wholesale trade purchases. After aggregating the relevant sectors and accounting for margining, model 1 is complete.”

Jablonski and crew demonstrated how to use an analysis by parts method to define specific margins and redefine what economic output could be given the unique spending patterns of food hubs.

Wrapping it Up

Approaching accuracy in your own economic impact modeling requires a clear understanding of what activity you want to investigate and the limitations of the model or tool you’re using. Margins are merely one element of the measurable economy that can make or break your analysis. But double-checking your assumptions and whether margins were applied appropriately in your analysis process is an easy way to strengthen the foundations of your study.