The Use of Extraneous Information in the Development of a Policy Simulation Model

Daryll E. Ray

Journal of Agricultural and Applied Economics, 1973, vol. 5, issue 1, 167-174

Abstract: A number of highly aggregated policy simulation models have been developed for the U.S. agricultural sector. While these models are useful in providing broad-stroke sketches of the effects of alternative farm policies, they have been criticized for their lack of commodity detail. Individuals, organizations and congressmen from a cattle producing state, as an example, are more interested in the impact of a changed agricultural policy on cattle prices and incomes than its effect on the income of all farmers. The reason most often given for not disaggregating by commodity groups is the researcher's reluctance to quantify opportunities for substitution among commodities in production and consumption. However, there may be more agreement on the relative magnitudes of supply and demand elasticities for individual commodities than the price elasticities of supply and demand for all farm output. Hence, a disaggregated model may distort reality much less than a highly aggregated model and at the same time provide detail on indirect effects of proposed policies that is so often sought by policy makers.

Date: 1973
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:jagaec:v:5:y:1973:i:01:p:167-174_01a

Access Statistics for this article

More articles in Journal of Agricultural and Applied Economics from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().