Farm Industries are unique from other Industries in IMPLAN as they often have different data sources and data processing techniques to capture the full economic activity in that Industry group. For example, while Total Industry Output (TIO) by Industry is only available at the national level for most Industries, state-level raw data sources exist for Farm Industry TIO. On the other hand, the primary data source for most industries’ Wage and Salary Employment and Income only has about 90% coverage for Farm Industries; thus, other data sources are used for these data elements. 


The primary data sources for state-level Output by farm Industry are the National Agricultural Statistics Service (NASS) Value of Production and Economic Research Service (ERS) Annual Cash Receipts data sets, both from the USDA. The county-level data from these sources are not consistent enough for our use; thus, we use data from the latest Census of Agriculture to estimate county-level farm industry Output. Census of Agriculture data is also used to estimate state-level values not disclosed by NASS or ERS.

The BEA's Regional Economic Accounts (REA) program estimates county-level Wage and Salary Employment, Proprietor Employment, Employee Compensation, and Proprietor’s Income. In contrast to the BLS’ CEW data, the BEA’s REA data provide full coverage of the farm Industries; however, the figures are totals for all farm industries combined, not broken down by type of farm commodity.1 Therefore, we have developed procedures to generate first estimates of employment and income by commodity and county based on annual Output values (described above) and various ratios from the Census of Agriculture and state-level data. These first estimates are controlled to the total farm employment value given by the REA data, as well as to higher geographic levels.

Due to their often relatively high level of subsidization, we also incorporate the ERS Agriculture Resource Management Survey (ARMS) data when processing TOPI for farm Industries. Read more about this process in Taxes on Production and Imports Net of Subsidies Data.

Some of the state and county farm Industries are subject to large adjustments when controlled to the national totals. This is a result of inconsistencies between sources. Because the agriculture data is, to a large extent, derived, analysts with local agriculture data are encouraged to use it.



The BEA Benchmark I-O tables provide us with Output, Employee Compensation (EC), and Gross Operating Surplus (GOS) by IMPLAN Industry. They also provide us with production functions for each IMPLAN Industry. The data is available for all 14 farm Industries. However, the data does not separate GOS into its component parts (Proprietor Income (PI) and Other Property Income (OPI)), nor do they provide Employment estimates. We use the latest BEA Benchmark data with other data sources from the same year to create ratios that are used in the annual data creation process:

  1. Output-per-EC by farm Industry: The BEA Benchmark provides EC and Output by farm Industry.
  2. EC-per-W&S Worker by farm Industry: The BEA Benchmark does not provide any employment estimates. Thus, to get ‘Benchmark’ W&S Employment by farm Industry, we distribute benchmark-year BEA’s REA “All Farm” W&S Employment based on benchmark-year BLS’ CEW W&S Employment by farm Industry and then calculate ratios from these estimates. While CEW has more industry detail than REA, we use the REA total W&S Employment figure since the CEW data do not provide 100% coverage of W&S Employment for the farm Industries. We only use the CEW data for distribution of the REA W&S Employment value. We then combine these W&S Employment estimates with the BEA’s Benchmark EC data to calculate EC-per-W&S Worker ratios by farm Industry.
  3. PI-per-Output by farm Industry: The BEA Benchmark does not directly give us PI by farm Industry, but rather gives us GOS by farm Industry. We use an agriculture-wide average ratio between PI and GOS (using PI from the 2007 BEA’s REA data, which has just one “All Farm” Industry) to estimate Benchmark PI by farm Industry. These ratios are then used to distribute the latest BEA’s REA “All Farm” PI data amongst the 14 farm Industries. We then combine these PI estimates with the BEA Benchmark Output data to calculate PI-per-Output ratios by farm Industry.
  4. Output-per-Proprietor by farm Industry: Again, the BEA Benchmark does not provide any employment estimates. We estimate Benchmark Proprietor Employment by distributing benchmark-year BEA’s REA farm Proprietor Employment based on the farm count per farm Industry from the latest Census of Agriculture. We then combine these Benchmark Proprietor Employment estimates with the BEA Benchmark Output data to calculate output-per-proprietor ratios by farm Industry.


  1. Each year, we obtain estimates of agricultural Output by Commodity by state from USDA’s ERS and NASS data. The data generally are current and empirical (i.e. based on observation and survey, rather than trend extrapolations), so we begin with Output data.
  2. ERS and NASS do not cover some states with low production, and can omit certain crops with low production. ERS often will report some of those low-production crops in its “miscellaneous” category. We attempt to reallocate those low-production crops into the appropriate Industry. We also use the Census of Agriculture to estimate crop production for states whose production is not reported by ERS or NASS. For both of these reasons, our estimates for a particular crop Industry often are somewhat larger than those reported by ERS or NASS.
  3. As of IMPLAN’s 2015 data set, we no longer control agricultural estimates to BEA for several reasons. The BEA release of farm cash receipts was released after we produced agricultural estimates. Additional discussions with BEA revealed that they primarily use ERS data, which is one of IMPLAN’s primary sources, so controlling to BEA estimates adds relatively little value. Furthermore, BEA’s commodity-level estimates are for cash receipts, which excludes crops put into inventory and home consumption, both of which drive intermediate expenditures. However, one benefit of controlling to BEA that we wanted to keep is that it theoretically corrects for ERS’ tendency to overestimate the Output of the Miscellaneous Crops Industry; therefore, we implemented an adjustment factor based on the ratio of BEA Benchmark Output to benchmark-year ERS Output. We do, however, compare our Output estimates to estimates from NIPA, BEA REA, ERS, and the BEA Industry Series to ensure that they are reasonable.
  4. These state values are distributed to the counties by using the ratio of county physical production to state physical production from the latest Census of Agriculture (which comes out every 5 years). For example, if County A has 10% of the state’s corn sales (or acres if sales is not available, and farms if acreage is not available), then it gets 10% of the state’s Annual Cash Receipts value for corn.
    • For non-disclosed counties in the Census of Agriculture, we multiply the average value of a particular commodity per unit of volume (or per farm in the absence of volume data) in the state that produces that commodity by the county-level data that produce that commodity (number of farms is always disclosed), giving us an estimate of total value of that commodity at the county level. We adjust estimates based on these ratios to control totals when possible.
    • Annual Cash Receipts data are not available for all crops at the state level. In these cases, the average ratio of state production to national production from the Census of Agriculture is used, and controlled to the state total for a higher-level aggregation, if that aggregation accounts for non-disclosed crop values.2


There is no data source for employment by agricultural commodity, even at the U.S. level.

  1. We make our first estimates of employment and income by IMPLAN Industry applying the Benchmark ratios to annual Output by commodity (both described above).
  2. We control those estimates of employment and income to annual BEA’s REA estimates of “All Farm” employment and income (both for W&S workers and Proprietors).
  3. Each state’s output, employment, and income are forced to sum to the U.S. totals, after which the counties’ values are forced to sum to the state values.


The BEA releases state-level GDP data, at approximately the 3-digit NAICS level, that includes the break-out of GDP into EC, TOPI, and GOS. These data collapse NAICS 111 (Crop Production) and 112 ( Animal Production) into a single “Farms” Industry. GOS consists of Proprietor Income (PI) and Other Property Income (OPI), so OPI for 3-digit NAICS is derived by subtracting our estimates of PI (described above) from GOS. These 3-digit control values are distributed to the IMPLAN Industries based on the latest BEA Benchmark’s characteristics for GOS and TOPI and by using data from the ERS Agriculture Resource Management Survey (ARMS) data as described below.


Updated source data and method for forecasting lagged state GDP data for farm Industries (IMPLAN Industries 1-14):

  1. At the national level, we now use growth rates from NIPA table 7.3.5, which reports national components of value added in the current Implan reference year. We apply the growth rates to BEA’s REA data, to maintain consistency with REA definitions and concepts.
  2. At the state level, we use growth in total farm output rates to project value added growth. We have empirical sources for current-year agricultural output by state. This has the result of better approximating future BEA estimates of farm value added. Previously, we used only EC, which was extrapolated from REA total farm EC and current-year output estimates.
  3. These state-level projections are then controlled to the national projections.

In Data Year 2015, we also incorporated USDA ERS Agriculture Resource Management Survey (ARMS) data to estimate components of Value Added by commodity at the national and state levels:

  1. ARMS data report sub-components of Value Added for certain commodities and certain states.
    • Only major agricultural states are included in ARMS. For non-covered states, we form initial estimates of OPI and TOPI by farm Industry according to national ratios of OPI and TOPI to state-level farm data. The national ratios are based on ARMS data, as described below.
    • ARMS reports government payments, i.e., subsidies, real estate and other property taxes (a large component of gross TOPI for farm Industries), interest (a component of OPI), and depreciation (another component of OPI).
    • Certain ARMS commodities line up well with IMPLAN commodities, including soybeans (over 90% of IMPLAN’s Oilseeds Industry), wheat and corn (collectively over 90% of IMPLAN’s Grains Industry), dairy, cattle, poultry, and other livestock. We make minor adjustments to oilseeds and grains to scale the ARMS values to account for non-covered commodities, e.g., canola in the Oilseeds Industry and rice in the Grains Industry. We do not use ARMS data in cases where the commodity classifications do not align well with IMPLAN Industries.
  2. Since ARMS data covers only certain parts of Value Added components, we don’t use the ARMS data outright but rather to distribute REA “Farm Total” Value Added components to IMPLAN farm Industries. For example:
    • First, we estimate, according to ARMS, the share of all real estate taxes in a state that go to dairy. Let’s say it is 5%.
    • If the ARMS data is lagged with respect to the IMPLAN reference year, we then use state-level output by commodity data from the lagged year and the current year to estimate the change in dairy’s share of the state’s total farm Output. If dairy increased from 10% to 20% of the state’s agricultural Output, we multiply the 5% share of real estate taxes by 2, for a 10% share. We check for and downwardly adjust abnormally high or low changes in shares.
    • We then take 10% of our projected REA Gross TOPI value as the Gross TOPI that belongs to dairy. We perform an analogous calculation for subsidies, and then calculate net TOPI from that.
  3. After using ARMS data where available, we use our pre-existing method of making initial estimates of Value Added components based on 5-year BEA benchmark data for farm commodities where ARMS coverage does not align with IMPLAN Industries.
  4. The final estimate for farm GDP for a state is controlled to our projection of REA farm Value Added, which is consistent with past practices.


Generally, we prefer our own estimates of Employment to QCEW, BEA, and the Census of Agriculture for a variety of reasons. Among those reasons: QCEW does not cover proprietors, which compose a significant share of farm employment; QCEW misses some W&S employment; BEA’s REA has employment data only at the “farm” level of detail, though it includes proprietor employment; the Census of Agriculture releases employment data only every 5 years and measures employment differently than our other data sources: it measures the number of unique human beings who worked on a farm as opposed to the "jobs" those humans filled. For example, if a farm had 6 humans who worked 2 months each, sequentially in a year, the Census of Agriculture would report that as 6 jobs, whereas in other data sources (and in IMPLAN), this is considered just one job – one job that happens to be filled by 6 different temporary workers. Our data attempts to correct for these omissions and inconsistencies. Since the agriculture data are, to a large extent, derived, analysts who have local agriculture data that also correct for these omissions and inconsistencies (e.g., from a survey) are encouraged to use their data. However, in the absence of such data, we encourage people to use IMPLAN’s estimates.


Special Industries in IMPLAN: Farm, Construction, Railroad, and Government


1In regards to Wage and Salary Employment, the BLS’ CEW does have estimates of wage and salary employment for farms with about 90% coverage, but the CEW data for farm Industries is particularly difficult to integrate because BLS’ CEW data establishments are not reclassified year to year, while farmer commodity production is. For example, a given farmer will plant either corn or soybeans (2 separate IMPLAN Industries) based on that year's prices and/or how late in the year they are able to plant their crops. Also, the CEW data does not include Proprietors.

2ERS and NASS occasionally change their reporting for aggregate categories between a) including the sum of non-disclosed values for subcategories in their aggregate category values and b) making the aggregate value simply the sum of disclosed children. We adjust our processes accordingly.


Written April 18, 2024