2014 Farm Bill: Historical Likelihood of County ARC and SCO Payments for Soybeans in Illinois
The farmdoc daily posts on May 8th and May 15th analyzed the County Ag Risk Coverage (ARC) and Supplemental Coverage Option (SCO) programs created in the 2014 Farm Bill. County ARC is one of the optional commodity programs available to producers of eligible program crops, while SCO is an optional insurance program which supplements the producer’s underlying individual plan of insurance. Both programs have “shallow loss” coverage designs: payments are triggered when actual revenues or yields fall below a specific guarantee, and are capped at a percentage of the value of the guarantee (details on the new commodity program options can be found in farmdoc daily February 20, 2014; details on SCO can be found in farmdoc daily February 27, 2014 and April 24, 2014; all posts in our continuing series on the 2014 Farm Bill can be accessed here). Today’s post extends the historical analysis of County ARC and SCO to soybeans in Illinois. Since the decision to use the County ARC (or Individual ARC) program limits the producer’s eligibility to use SCO, analysis comparing these programs should be useful in making program decisions.
Coverage Levels on Crop Insurance and the SCO Alternative
Over time, crop insurance coverage levels chosen by farmers have increased, and there are regional patterns in coverage elections. Coverage level choices tend to be highest for corn and soybeans in the heart of the Corn Belt. These regional distributions will impact the economic attractiveness of the Supplemental Coverage Option (SCO), a new crop insurance product specified in the 2014 Farm Bill (farmdoc daily February 27, 2014). Because SCO will be more attractive where lower coverage levels predominate, SCO use likely will be more attractive outside of the Corn Belt.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.