The Contribution of Agricultural, Forestry, and Fisheries Production to the US and Iowa Economies

By John M. Crespi

In a recent report, USDA’s Economic Research Service summarizes the prevalence of agricultural and food sectors in the United States economy (USDA ERS 2024a). USDA finds that the $1.4 trillion spent in all agricultural, food, forestry, and related industries was about 5.5% of US gross do­­­­­­­­­­­­mestic product (GDP). In their analysis, USDA includes all agricultural and forestry production, food and beverage manufacturing and processing, food retailing, food service, and fishing, as well as textiles, apparel, and leather production. According to the report, US households spent approximately 13% of their 2022 consumption on food—only transportation and housing expenditures ranked higher. USDA reports that the agricultural, food, forestry, and related industries contributed about 1% to 2022’s US GDP and directly created about 3.6 million jobs. 

In this article, I focus only on the initial production portions of the “agricultural, food, forestry, and related industries” in the supply chain in order to ask what the impact is on the US economy from not just the direct spending but also indirect and induced effects that impact other industries. First, I need to define what I mean by the initial production portions of that industry.

The 2-digit, North American Industrial Classification System (NAICS 2022) categorizes US economic activities into 20 major sectors. Using the 2022 coding, I will examine the impact­­ from NAICS Industry 11: Agriculture, Forestry, Fishing, and Hunting. This is a large sector, which I shall refer to as an “industry” although obviously it is aggregated over many different industries. Importantly, Industry 11 does not include food or beverage manufacturing and processing, so it is only the production portion of the supply chain for farming, livestock production, timber harvesting, and the harvesting of animals from farms, ranches, or natural habitats and sold for food or sold to other industries for further processing. 

Table 1 shows the value-added contribution of Industry 11 to both Iowa and the United States from 2019 to 2022 (the latest year for which data are available). Value added is the addition to GDP for the state and the United States (state GDP is more often called Gross State Product). Value added is the difference between an industry’s sales revenue (its gross output) and the cost of that industry’s intermediate inputs. Table 1 shows that in 2022, Industry 11 directly added $18.6 billion and $270.8 billion to the Iowa and US economies, respectively. Or, one could say that of the $270.82 billion added to US GDP from Industry 11, Iowa contributed about 7%. However, this is only part of the direct contribution, as dollars generated in Industry 11 also had impacts across the economy when they were spent.

There are approximately two million farms in the United States with 86,000 of those in Iowa. Forestry acreage is about two-thirds the size of farmland acreage in the United States and in Iowa it is about 10% the size of Iowa’s farm acreage. Table 2 presents a simple breakdown, and table 3 then compares the cash receipts for the top 5 agricultural commodities in both Iowa and the United States.

In some states, fisheries and forestry are larger than agricultural production. In Iowa, on the other hand, fisheries and forestry are much smaller than agriculture as a whole.¹ 

I use an economic impact model, often called an input-output (IO) model, developed by IMPLAN.² In this model, think of all industries in the economy as feeding inputs into each other to produce outputs that are then sold to consumers or sold to another industry along the supply chain. IO models make adjustments for imports and exports as well as government services while also accounting for employment and taxes to the various industrial sectors. IO models determine the linkages between and among different industries in the production of goods and services in the economy and can measure how dollars spent in one industry impact another. 

Relying on data collected by a variety of agencies in federal and state governments, an IO model determines the production linkages of all of an economy’s industries. IMPLAN uses what is called a Leontief production function (named for Wasily Leontief, 1906–1999, who won the Nobel Prize in Economics for his work on IO models, see Leontief 1986) that assumes inputs are used in each industry in some measurable fixed proportions by value. Once these proportions are measured, the interactions of the inputs to production across the economic system and along its supply chains can be related to the collected government agency data, which creates an economic snapshot of an economy’s linkages. With the model in hand, changes to underlying values or assumptions in the model produce counterfactual results. Economists recognize that assuming fixed proportions technologies for production functions is a big assumption; however, economic development studies regularly use IO models such as IMPLAN because they allow hypothesizing changes to an industry’s employment or revenue to measure the impacts on the rest of the productive inputs in the economic model.³ For this study, I will use IMPLAN’s Industry Contribution Analysis (ICA) software.⁴ ICA can be used to gauge the aggregate economic importance of a particular industry, in this case, NAICS Industry 11 on the rest of the economy. 

For this analysis, I take a conservative approach whereby I remove the effects along the supply chain for Industry 11 where the industry is interacting with itself, so as to focus only on the impacts of this industry on the rest of the economy. In other words, I remove “buybacks” and other intra-industry sales within Industry 11, such as, when a hog producer buys feeder pigs, or when corn is purchased for livestock production, since both purchases are within Industry 11 and we want to avoid double counting.⁵ Under industry contribution analysis, IMPLAN measures three types of effects as shown in table 4.

Table 5 shows the economic impact of Industry 11 on the rest of the economy where “Jobs” is IMPLAN’s estimate of the number of jobs created in the economy due to Industry 11 (“Jobs” in IMPLAN can be both full and part time and do not directly match full-time employment (FTE) often used in other studies). “Labor Income” is the income transferred to people working in those jobs. Directly, labor income adds $8 billion in Iowa and $146 billion in the United States, but with its indirect and induced impacts throughout the economy, labor income is an additional $4.7 billion and $190 billion in Iowa and the United States, respectively. “Output” is IMPLAN's estimate of revenue—its direct effect impact is the value of the final production from Industry 11. As a check on IMPLAN’s results, we see that IMPLAN’s estimate of the direct impacts of output in table 5 for Industry 11 in Iowa (which has little forestry or fisheries) is comparable to the cash receipts reported by USDA in table 3 for all agriculture (differing mostly by inflation between 2022 and 2024). Likewise, notice the direct effects on value added for Iowa and the United States ($19 billion and $274 billion, respectively) are similar to the values generated by the Bureau of Economic Analysis and shown in table 1 for 2022.

What table 5 shows on the “Output” results is that the direct, indirect, and induced economic impacts of the agricultural, forestry, and fishing industries adds $68 billion to Iowa sales revenue with about 30% of this being from indirect and induced effects. For the United States as a whole, the indirect and induced economic impacts of the agricultural, forestry, and fishing industries is about 52% of the total output generated by this sector ($1.3 trillion). “Value Added” shows a similar story where the direct impacts are only part of the story as the indirect and induced effects multiply through the rest of the economy. Furthermore, table 5 shows that without Industry 11, combined federal and state taxes from Iowa would be about $4 billion less and about $111 billion less nationally.

There are drawbacks of the present study, of course. The first is the base year of the data used. Because all government agencies have lags in their reporting of data, sometimes by a year or two, IMPLAN is guessing as to the 2024 impacts based upon data collected in 2022 and earlier and then adjusting for inflation and other effects determined by IMPLAN’s developers (see IMPLAN 2017a; IMPLAN 2017b). Secondly, IMPLAN is based on very large, aggregated building blocks of data about US industries and, as such, this limits what economists would call a general equilibrium approach to the study of impacts. In short, IMPLAN does not examine linked market changes in supply and demand and does not directly model one market’s linkages through the economy to other markets in production-function, IO analysis. Third, the production function utilized by IMPLAN is restrictive. IMPLAN enforces a Leontief production function on all of its industries whereby it assumes proportions of inputs to production used in the recent past remain the same when changes are made to an industry. While such fixed proportions arguably make sense for some industries like agriculture and forestry (e.g., producers may use roughly fixed proportions of seed, land, labor, and fertilizer to produce a crop in one year, which IMPLAN assumes might be the same the next year), we still must question the usage of fixed proportions for industries like trucking, construction, financial, or service industries that can more easily alter their input combinations when the output from another industry changes. Fourth, I do not study any environmental or other negative costs (externalities) that affect an economy. For example, I do not consider the impact of pollution from Industry 11, although there are methods to try and take such costs into the calculations. 

Nevertheless, as a back-of-the-envelope analysis assuming every input flowed proportionally through an industry as it did during data collection, this analysis does provide a rational guess as to the impacts of a dollar spent in one industry on the spending of another industry. In every economic model, one is always trading off one set of modeling assumptions for another. In this short paper, by restricting the model to large aggregations of many industries in order to examine “agriculture, forestry, and fishing production” I have also aggregated away many of the supply and demand linkages, and, by restricting linkages to a single year of the analysis, have taken a short-run snapshot where fixed proportions make more sense. Likewise, by restricting the analysis to remove indirect and induced linkages within the “agriculture, forestry, and fishing” sector, I make the analysis more conservative in its estimates. Future studies should consider less aggregated sectors such as crops, livestock, and food processing to follow the indirect and induced effects throughout the economies of Iowa and the United States for these sectors. What the analysis is showing is that the production portion of the agricultural, forestry, and fisheries sector has much larger impacts than just its direct ones. The industry contributes to 197,000 jobs in Iowa, 6 million jobs in the United States, $68 billion throughout the Iowa economy, and $1.3 trillion throughout the United States because of the presence of direct, indirect, and induced impacts.

Footnotes:

1. The north-central United States had 117 billion cubic feet of forest growing stock in 2017, which is only 12% of the growing stock of the entire United States. The north-central United States has little fresh, frozen, or canned fishery products relative to the 1.65 billion pounds of fish production for the rest of the United States in 2017 (USDA NASS 2021).
2. IMPLAN Group, LLC. Huntersville, NC.
3. CARD researchers recently used IMPLAN to examine the economic importance of the Iowa beef industry (Schulz et al. 2017), the hog industry (Cook and Schulz 2022), the impact of African Swine Fever in Iowa and the United States (Carriquiry et al. 2020), and the economic impact of Iowa State University’s veterinary diagnostic lab on the Iowa economy (Schulz et al. 2018).
4. For a fuller discussion on contribution analysis, see Lucas (2019) and Miller and Blair (2022, pp. 310–316).
5. As another example where buybacks in sectors are removed, see Cook and Schulz (2022).

References

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Cook, H., and L. Schulz. 2022. “U.S. Pork Industry: Current Structure and Economic Importance.” National Pork Producers Council, Washington, DC. Available at: https://nppc.org/wp-content/uploads/2022/07/2021-NPPC-Economic-Contribution-Report-FINAL.pdf. Last accessed February 24, 2024.

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Leontief, W. 1986. Input-Output Economics, 2nd ed. Oxford: Oxford University Press.

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Schulz, L.L., D.J. Hayes, D.J. Holtkamp, and D.A. Swenson. 2018. "Economic Impact of University Veterinary Diagnostic Laboratories: A Case." Preventive Veterinary Medicine 151:5-12.

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Suggested citation

Crespi, J.M. 2024. “The Contribution of Agricultural, Forestry, and Fisheries, Production to the US and Iowa Economies.” Agricultural Policy Review, Winter 2024. Center for Agricultural and Rural Development, Iowa State University. Available at: https://agpolicyreview.card.iastate.edu/winter-2024/contribution-agricultural-forestry-and-fisheries-production-us-and-iowa-economies.