Uses of a Strategic Information System in Agricultural Policy Formulation

Size: px

Start display at page:

Download "Uses of a Strategic Information System in Agricultural Policy Formulation"

Priscilla Parsons
6 years ago
Views:

1 Uses of a Strategic Information System in Agricultural Policy Formulation Bruce L. Gardner, University of Maryland College Park The idea of a Strategic Information System (SIS) (as opposed to other information systems) is its focus on decision-making. With respect to agricultural policy this means an information system to assist in choice of policy options and in policy implementation. And, it is important that the decisions are political decisions, which have to be implemented and evaluated by interest groups as well as policymakers. An SIS is valuable to the extent that it is useful in decision-making. I want first to suggest a criterion for the value of a SIS by considering the value of the information it provides. The idea is that the value of an information system is the increase in the expected value of decisions made after receiving the messages the system delivers, as compared to decisions that would have been made without the messages. Consider a simple 2-state problem, with 2 alternative policies. Suppose the choice has to be made between increasing government stockholding of grain or letting private storage handle the situation (laissez faire). What information is most valuable for this decision? Two different types of information should be distinguished: monitoring and structural. Monitoring information informs us what the situation is and therefore provides the raw material for immediate forward-looking choices. For the stockholding decision a key element of monitoring information is the level of stocks already being held in the private sector, by commercial enterprises and by farmers. If stocks are low (state L) the expected value of government intervention is large. On the other hand, if private stocks are high (state H), government stockpiling is a waste of scarce resources and the expected value is small. But we are uncertain which state prevails. With 2 states, let the two values for the active policy be XL, if private stocks turn out low (L), or XH if stocks are high (H), with XL > XH. For the policy of non-intervention, or laissez faire, whose value is denoted Y, the situation is the opposite. We will regret the policy if private stocks are low: YL < YH. In advance of knowing which state prevails, we choose the policy whose expected value is highest. The expected value of policy Y is: Pr(H) * YH + Pr(L) * YL, and similarly for X, where Pr(H) and Pr(L) are the probabilities of the 2 states. In this context, the information system provides an estimate of private stocks, whose value as a piece of strategic information, depends on 3 things: Prior information of the decision-maker. (If we already knew the stock level, or knew enough that the estimate would be unlikely to change our prior estimate, then the new estimate is of little value.) The likelihood that the information is correct ( the appropriate measure of quality of information. (If the variance of the new estimate is high, i.e., the quality is low, the information is unlikely to improve our decision-making.) The findings, interpretations, and conclusions are the author s own and should not attributed to the World Bank, its Board of Directors, its management, or any member countries.

2 Workshop Proceedings The increase in the value of the decision that acting on the information would generate, if the information were correct. (If the same policy were appropriate whether stocks were high or low, the strategic value of a stock-level estimate is zero. But if stocks are very costly to maintain when not needed, the strategic value of a stock-level estimate is more likely to be high). These are simple points, but it is helpful to keep in mind that these are the only facts about information that give it value in the strategic context. Of course, determining the expected value of the elements of an information system is only the first step of two in deciding on how to proceed. The second step is assessing the costs of the system 1. A related example for which empirical work has been done is the value of information on the U.S. wheat crop, in the context of implementing an already existing public storage program. Early detection of the size of a crop shortfall helps us decide on appropriate levels of commodity storage in a public stocks program (and helps private-sector storage decision, too). Bradford and Kelegian ( The Value of Information for Crop Forecasting in a Market System. Review of Economic Studies, 44, December 1977, pp ) carried out a benefit-cost study of the monthly U.S. wheat production estimates of USDA along these lines. Essentially the idea is that the sooner you know there will be a shortfall, the sooner you can start making consumption adjustments (avoiding using grain in ways that will in retrospect be wasteful). They conclude that the U.S. wheat crop estimates in fact have benefits substantially exceeding their costs. Structural information helps in determining parameters which are crucial in deciding the more fundamental question of whether a public buffer stock should be established. One such parameter is the net additionality of public stocks. If we place a thousand tons of wheat in public stocks, this will reduce the likelihood of future price increases, and this in itself will reduce the incentives of the private sector to hold stocks. But what is the tradeoff? One needs data on the history of private stockholding and its determinants to answer this question. For example, I once estimated that each bushel of wheat placed in USDA stocks in the 1970s caused 0.75 bushel less wheat to be held by private stockholders, so that net additionality was quite low, about 25 percent. So if we want the stabilization afforded by an additional 100,000 tons of wheat in stocks, the stocking authority has to hold an additional 400,000 tons. In the U.S. case, such evidence of the inefficiency of USDA stockholding eventually led both farm and nonfarm interests to agree on ending USDA stockholding in the 1980s. A similar accumulation of evidence about annual acreage-idling programs resulted in the 1996 Farm Act ending such programs, even revoking the Secretary of Agriculture s authority to implement such programs, without significant opposition from either farm or nonfarm interests. The evidence here involved the effects of U.S. production cutbacks on world price, which turns on the elasticity of demand for U.S. commodity exports. As more and more data indicated that expanded production elsewhere was supplanting U.S. exports, farmers support for idling acreage waned. The information system needed for these strategic purposes is one which provides the basic data necessary for parameter estimates as well as a system which provides the economic and statistical skills needed to analyze the data appropriately to reveal the parameters (usually not easy). 1 The approach follows that of Hirsheleifer and Riley (Economics of Uncertainty and Information, Cambridge, 1991, Ch.4) who in turn are using ideas developed by Leonard J. Savage (Foundations of Statistics, 1954). It is an application of Bayesian statistical decision theory. See also paper by Bradford and Kelejan.

3 Mexico Strategic Information System for the Agriculture and Rural Sectors Clinic In the U.S. case, the programs that have proven themselves to be the most valuable elements of a strategic information system for agriculture are: (1) the National Agricultural Statistics Service (NASS) crop and livestock quantity and farm price estimates, provided monthly during the growing season, (2) the periodic (usually every 5 years) Censuses of Agriculture that provide details by county about farm enterprises, and (3) the system of collecting farm input price and quantity data, and food price and margin data through surveys of the Department of Labor s Bureau of Labor Statistics (BLS) and the Economic Research Service (ERS) of USDA. The BLS price data are derived from monthly surveys, but much ERS input data comes from producer-group data and informal industry contacts. Some of these data are arguably of low quality, from a strict statistical sampling viewpoint, but they are also cheap. The main gaps in the U.S. picture at the moment are environmentally related agricultural information. We just don t know enough about the extent to which nitrogen and phosphorus applied as fertilizer gets into streams flowing through farms to make well informed policy choices. Similar but even more passionately debated issues arise about pesticide residues on crops, and possible human health problems attributable to them. Consumers would like to know more, but comprehensive residue testing is very costly, especially when we do not even know what the health risks are associated with various levels of the hundreds of chemicals used. On the other hand, collection of some U.S. data has been increasingly called into question. Information on minor commodities like mink, horses, or specialty crops has been argued to be of value only to the industry and not needed for policy-making; therefore the industry should finance the collection and dissemination of these data. In fact some USDA data collection has been dropped, reduced in frequency, or done by USDA under contract with states or private-sector interests. The approach follows that of Hirshleifer and Riley (Economics of Uncertainty and Information, Cambridge, 1991, Ch. 4) who in turn are using ideas developed by Leonard J. Savage (Foundations of Statistics, 1954). It is an application of Bayesian statistical decision theory.

4 Uses of a Strategic Information System in Agricultural Policy Formulation Bruce L. Gardner University of Maryland Criterion for the value of information: expected return from decisions made after receiving information, minus expected return from decisions that would have been made without the information. 1

5 Example 1. Value of Information about Vulnerability of Livestock Sector to an Epidemic Value of Actions: (S 1 : Not Vulnerable) Value of Actions (S 2 : Vulnerable) Policy A 1 (no regulation) N 2 M 1 G H M 2 N 1 Policy A 2 (restrictive regulation) Example 2. Value of Information about Net Additionality of Public Stocks Value of Actions: (S 1 : Additionality small) Value of Actions (S 2 : Additionality large) Policy A 2 (public stocks with price band) N 2 M 1 Policy A 1 (no puhlic stocks) M 2 N

6 Components That Determine the Value of Information Increase in expected value that acting on the information would generate, if the information were correct The likelihood that the information is correct Prior information of the policymaker AND NOTHING ELSE [Bayesian decision theory provides a method to calculate the value of information using the three components] Examples of Agricultural Policy Decisions under Uncertainty that Depend on Information (1) Deciding whether to have an acreage set-aside program (e.g., U.S. corn in 1994) (2) Deciding whether to implement a commodity price stabilization program (2a) Deciding what quantities to store in (2) (3) Deciding how much to spend on agricultural research (4) Deciding how much to spend on economic studies to evaluate an agricultural research program 3

7 Case Studies Value of USDA Crop Forecasting for Wheat (Bradford and Kelejian) Value of the U.S. Farmer-Owned Reserve Program Elasticities of demand and supply management Pricing of product components and ethanol subsidies Value of tobacco quota Components of the U.S. Data System that Have Been Strategically Valuable NASS crop and livestock quantity and farm price estimates AMS market price information County data of Census of Agriculture BLS and ERS input price and quantity data, as used in price and productivity indexes FAS reports on foreign market conditions 4

2007 U.S CORN PRODUCTION RISKS: WHAT DOES HISTORY TEACH US? Darrel Good and Scott Irwin May 17, 2007 MOBR 07-01

2007 U.S CORN PRODUCTION RISKS: WHAT DOES HISTORY TEACH US? Darrel Good and Scott Irwin May 17, 2007 MOBR 07-01 INTRODUCTION From May to October each year, the corn market typically finds direction from