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Farm Data Procedures

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BEA Benchmark and Census of Agriculture

The BEA Benchmark I-O tables provide us with Output, Employee Compensation (EC), and Gross Operating Surplus (GOS) by IMPLAN Sector. They also provide us with production functions for each IMPLAN Sector. The data is available for all 14 farm Sectors. 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 Sector: The BEA Benchmark provides EC and Output by farm Sector.
  2. EC-per-W&S Worker by farm Sector: The BEA Benchmark does not provide any employment estimates. Thus, to get ‘Benchmark’ W&S Employment by farm Sector, we distribute benchmark-year BEA REA “All Farm” W&S Employment based on benchmark-year BLS CEW W&S Employment by farm sector and then calculate ratios from these estimates. While CEW has more sector 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 sectors. 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 Benchmark EC data to calculate EC-per-W&S Worker ratios by farm Sector.
  3. PI-per-Output by farm Sector: The BEA Benchmark does not directly give us PI by farm Sector, but rather gives us GOS by farm Sector. We use an agriculture-wide average ratio between PI and GOS (using PI from the 2007 BEA REA data, which has just one “All Farm” Sector) to estimate Benchmark PI by farm Sector. These ratios are then used to distribute the latest BEA REA “All Farm” PI data amongst the 14 farm Sectors. We then combine these PI estimates with the BEA Benchmark Output data to calculate PI-per-Output ratios by farm Sector.
  4. Output-per-Proprietor by farm Sector: Again, the BEA Benchmark does not provide any employment estimates. We estimate Benchmark Proprietor Employment by distributing benchmark-year BEA REA farm proprietor employment based on the farm count per farm sector 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 Sector.

Annual Output

  1. Each year, we obtain estimates of agricultural Output by commodity by state from USDA’s Economic Research Service (ERS) and National Agricultural Statistics Service (NASS). 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 Sector. 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 Sector 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 Sector; 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 acreages 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.1

1 ERS 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.

Annual Employment & Income

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 Sector applying the Benchmark ratios to annual Output by commodity (both described above).
  2. We control those estimates of employment and income to annual BEA 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.

Annual Value-Added

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” Sector. 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 Sectors 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.

Updates for 2015 IMPLAN Data

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

  • 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 REA data, to maintain consistency with REA definitions and concepts.
  • 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.
  • 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:

  • ARMS data report sub-components of value added components 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 Sector 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 sectors), 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 sector), wheat and corn (collectively over 90% of IMPLAN’s Grains sector), 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 sector and rice in the Grains sector. We do not use ARMS data in cases where the commodity classifications do not align well with IMPLAN Sectors.
  • 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 Sectors.
    • 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.
  • 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 Sectoring.
  • 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.

 

Notes

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 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 when building their IMPLAN models. However, in the absence of such data, we encourage people to use IMPLAN’s estimates.

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