ous year; ARMS indications from the current year; other data; and knowledge of NASS, ERS, and state economists and statisticians. The data other than ARMS that the board uses in its work include the Census of Agriculture, prices paid indexes from the NASS Agricultural Prices Report, Crop Acreage and Production, Livestock Production, ERS farm income, ERS cost of production for various commodities, and data from the National Pork Producers Council, the Federal Reserve, the producer price index (PPI), and the Association for Equipment Manufacturers (AEM). ARMS III staff prepares recommendations prior to the board.
The boards rely on two major inputs or “summaries.” One is a direct expansion of the summarized data. The other is the final calibrated summary.
Regional Estimates. After the national estimate for each expenditure item is set, board members set regional-level estimates for the five farm production regions. These regional estimates are constrained to sum up to the National Expenditures Estimates.
State Estimates. The board members then set state-level estimates that sum up to the regional expenditures estimates. Thus, in a cascading effect, the board conducts several reviews to ensure that the totals estimated in the ARMS estimation process are consistent with official production estimates, and that regional and state estimates are consistent with the national totals and are additive.
Other Estimates. Other values are estimated directly, including the level of aggregate expenditures by item within each size class of gross farm revenue, and within each major type of farm enterprise and fuel subcomponent expenditures (diesel, gas, liquid propane gas, and other fuels).
In this section, we have outlined the several steps in the development of published estimates from ARMS. Overall, the effects of the various adjustments on statistics estimated using ARMS are not clear. In particular, the interventions in ARMS based on board processes introduce changes that are not replicable in the normal sense expected in scientific research. These interventions may well lead to better estimates, or they may simply impose consistency across various key estimates at the cost of disturbing other relationships in the data.
Recommendation 6.9: NASS and ERS should provide more clarification and transparency of the estimation process, specifically the effect of calibrations on the assignment of weights and the resulting estimates.