Designing a Counter-Cyclical Support Program

Price vs. Revenue Triggers

Published: February 27, 2002
Updated: March 30, 2017

Price vs. Revenue Triggers

The FAIR Act contained no mechanism to provide assistance to producers during protracted periods of low prices. This deficiency has led to support for some type of counter-cyclical (CC) support program for production agriculture. Most CC proposals advanced to date rely on either a price or revenue trigger. The U.S. cotton industry supports a price-triggered counter-cyclical support program as included in H.R. 2646, the Farm Security Act of 2001, as reported by the House Agriculture Committee.

The price and yield experiences of the last three years for many U.S. agricultural commodities will limit the effectiveness of any historically derived income or revenue-based CC program. As an example, consider that the market revenue (inclusive of marketing loan gains) generated by upland cotton production has averaged about $4.8 billion since 1996 due to weak prices and production losses, with 1998, 1999 and 2000 providing some of the most difficult growing conditions ever experienced by broad areas of the U.S. Cotton Belt. This income average is far below the $6.2 billion averaged during the mid-1990's.

Adding market loss assistance payments from 1996 onward raises average revenue to only $5.3 billion, and cotton prices are currently at historical lows for the 2001 crop. Thus, a historically based CC program would provide only nominal support for cotton producers. Current projections indicate a similar situation would exist in 2002. Soybean growers could face an analogous quandary as the prolonged period of low prices has reduced soybean revenues that would be used in calculating a national target income.

Another difficulty with many revenue-based commodity proposals is that they would require early season projections of both price and production. The plan advanced by the National Corn Growers Association (NCGA), for example, would require USDA to base expected market income on price and production estimates for just the first three months of the marketing year. Yet, historically USDA has required a longer time frame to develop stable estimates of prices and production. For example, USDA's projected 1999 U.S. cotton crop declined 1.9 million bales between its August 1999 and October 1999 crop reports. The cotton crop subsequently increased by 600,000 bales by January 2000. For the 2000 cotton crop, USDA's crop projection was lowered almost 1.7 million bales between August and October of 2000. By January 2001, USDA had lowered the crop estimate another 300,000 bales. The appropriate price discovery period is also likely to vary across crops. The bulk of the U.S. cotton crop is historically marketed in the months of October through February. Other crops display different patterns.

Revenue-based plans often "lock-in" baseline projection errors. The NCGA proposal, for example, derives specific future target crop incomes by adjusting average production during a defined base period by year-to-year changes in the Congressional Budget Office (CBO) baseline production forecasts. Although CBO does an admirable job under difficult circumstances, CBO's projections are often quite inaccurate, as with any forecasting body, both in terms of magnitude and direction. CBO's December 2000 baseline projected 2001 U.S. upland cotton planted acreage at 15.37 million acres. In actuality, 2001 planted acreage exceeds 16 million acres. Likewise, the 2001 U.S. upland cotton crop is at least 1.2 million bales higher than CBO's baseline projection. And the accuracy of CBO projections degrades even more dramatically beyond more than one year out as the estimates are essentially an extension of recent trends. As such, the point estimates required of CBO can not reflect the self-correcting actions of the marketplace. Hence, there is a built-in bias, either toward growth or shrinkage, in CBO's out-year projections. The use of baseline projections for the derivation of future target incomes is highly questionable, especially given the considerable uncertainty that must be attached to any such projections.