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An Overview of Public Information and the Agriculture and Food System

An Overview of Public Information and the Agriculture and Food System. Richard E. Just C-FARE Fall Symposium on "Public Information and the Agricultural and Food System“ Washington DC November 6, 2002. 1. Unprecedented Change. Industrialization of Agriculture Role of Off-farm Income

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An Overview of Public Information and the Agriculture and Food System

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  1. An Overview of Public Information and the Agriculture and Food System Richard E. Just C-FARE Fall Symposium on "Public Information and the Agricultural and Food System“ Washington DC November 6, 2002 1

  2. Unprecedented Change • Industrialization of Agriculture • Role of Off-farm Income • Biotechnology – GM Traceability • Environmental Concerns & Sensitivity • Alternative Agriculture – Niche Markets • Nonmarket Activity (Contracting & Internet) • Information Technology & the Internet • Consolidation of Agribusiness (Supply, Mktg) 2

  3. Shortcomings of Data • Heterogeneity • 1. Spatial Allocation (of Inputs to Crops) • 2. Temporal Allocation (Planting, Growing, Harvest) • 3. Statistical Distribution (Variation not only Average) • 4. Correlation of Multifunctional Attributes • 5. Capital Stock & Long-Term Behavior • 6. Financial Structure & Off-farm Activity • Identification of Structure vs Reduced Form • Widely Accessible Panel Data is Needed • Nontraditional Markets • Consolidation Issues • Anticipatory Policy Support 3

  4. Today’s Farm Sector is Truly DiverseOffutt (2002 AAEA Presidential Address; AJAE Dec 2002) • Size • Choice of farming enterprise(s) • Business organization • Environmental performance Analysts are obligated to investigate differential response and impact. Relying on aggregate data has obscured distributional facts about effects of ag policy. 4

  5. Unique Features of Ag ProductionJust & Pope (2002 synthesis of ag production in the Handbook) • Temporal allocation with biological production • Flexible output mix by spatial allocation • Fragmented technology adoption (role of capital) • Uncertainty (weather & pests): biological production • Heterogeneity: • Land/Soil Quality Climate/Weather/Pests • Water Availability Environmental Sensitivity 5

  6. The Problem of HeterogeneityJust & Pope (1999 ASSA Meetings; AJAE Aug 1999) • Standard theory fails at the aggregate level if heterogeneity is not considered • Explains why many models don’t predict • Implied policy/welfare impacts are false • Distributional data is required 6

  7. 1. Spatial AllocationJust (2000 NEARA Meetings; ARER Oct 2000) • Data do not include allocations of fertilizer, pesticides, & labor to crops • Models can study only aggregate production possibilities • Models allow increased fertilizer application on wheat land to increase corn yields • Necessary aggregation conditions are dubious & prevent meaningful policy analysis 7

  8. Adequacy of Aggregate ModelsJust & Pope (1999 ASSA Meetings; AJAE Aug 1999) • Aggregates assumed to obey Adam Smith’s invisible hand (equating marginal conditions) • Ignores realities of farming: • SR fixities & constraints (e.g., financial structure, physical capital, land quality, family labor) • Price/weather/pest variation • Ex post adjustment (responses to states of nature) • Entry/exit/failure (bankruptcy) • If policy responses are affected by these, aggregate models that ignore heterogeneity are inappropriate 8

  9. 2. Temporal AllocationJust (2000 SERA-IEG-31; Ag Systems 2002) • Data do not include timing of input applications • Many risk-reducing inputs are stage-dependent • Pesticide applications: • Pre-emergent - Preventative • Post-emergent - Prescriptive • Models cannot discern motivations • Risk aversion vs • Simple profit max (or loss minimization) To understand behavior, we must study decisions given information available to the farmer at the time 9

  10. The Crop Insurance ExampleJust/Calvin/Quiggin (AJAE Nov 1999) • Risk-based justification (missing risk market) • Nondistortionary correction of market failure • Research shows farmers’ are motivated by subsidies • The risk benefit is only $.65/acre • Federal Costs: • $1.4-1.7 billion per year throughout the 1990s • Efforts to address moral hazard/adverse selection • Multi-Peril Crop Insurance (MPCI) Catastrophic Risk Protection (CAT) • Crop Revenue Coverage (CRC) Revenue Assurance (RA) • Income Protection (IP) Group Risk Protection (GRP) Why are large subsidies required if SR risk matters? 10

  11. The Identification ProblemJust (2000 SERA-IEG-31; Ag Systems 2002) • Is crop diversification due to risk aversion? • Or labor constraints, scheduling of fixed inputs & crop rotation? • Is heavy use of pesticides due to risk aversion? • Or expected profit benefits? • Is irrigation used to reduce risk? • Or increase profits? Most risk effects are subject to identification problems.Without allocations, discernment is possible only artificially(by imposing assumptions which in effect determine results). 11

  12. Cannot Truly Test New Advanceswith Aggregate Data • Methodological advances are “illustrated” in academic journals with token aggregate data • Risk response in supply • Risk effects of inputs • Structure of farmers' risk preferences • Exceptions primarily in less developed ag • Aggregation tends to eliminate and alter risk • Farm-level yield variation 2-10 times greater than aggregate data (Just & Weninger AJAE May 1999) 12

  13. 3. Statistical DistributionJust & Pope (1999 ASSA Meetings; AJAE Aug 1999) • NASS Focus: Averages (prices) and Totals (production & capital) • Economists can’t generate the benefit of data that has been collected because all of its characteristics are not available for research • Failure of aggregate models can be mitigated by data on variation as well as averages • The alternative: Assume identical farms & circumstances 13

  14. 4. Correlation of AttributesJust & Antle (1990 ASSA Meetings; AER May 1990) • Local correlations of multifunctional characteristics are critical for policy impacts • Productivity • Erodability • Environmental sensitivity • Value in preservation • Data collection has tended to be independent • ERS (ARMS) NASS CENSUS NRCS EPA GIS • Public data do not allow linking observations by location 14

  15. An Environmental Use Restriction Environmental Sensitivity: Pollution- Output Ratio (z/y) Input Intensity: Input-Output Ratio (x/y) 16

  16. An Input Intensity Restriction Environmental Sensitivity: Pollution- Output Ratio (z/y) Input Intensity: Input-Output Ratio (x/y) 15

  17. Effect of a Target Price Pollution- Output Ratio (z/y) Py PyT Input-Output Ratio (x/y) Px Px 17

  18. Social Optimality Pollution- Output Ratio (z/y) Py = Marginal value of output Px = Marginal cost of input Pz = Marginal cost of pollution Slope -Px/Pz Py/Pz PyY + PxX – PzZ > 0 PyY + PxX – PzZ = 0 Input-Output Ratio (x/y) 18

  19. Social Optimality: Environmental Use Restriction Works Well Pollution- Output Ratio (z/y) Py = Marginal value of output Px = Marginal cost of input Pz = Marginal cost of pollution Slope -Px/Pz Py/Pz PyY + PxX – PzZ = 0 PyY + PxX – PzZ > 0 Input-Output Ratio (x/y) 19

  20. Social Optimality: Input Intensity Restriction Works Well Pollution- Output Ratio (z/y) Py = Marginal value of output Px = Marginal cost of input Pz = Marginal cost of pollution Slope -Px/Pz PyY + PxX – PzZ = 0 PyY + PxX – PzZ > 0 Input-Output Ratio (x/y) 20

  21. 5. Capital Stock & LR BehaviorJust & Pope (2002 synthesis of ag production in the Handbook) • The ag risk that really matters is risk of farm failure (the long swings) • Lesson of 1970s boom, 1980s debt crisis • Capital investment/replacement is the key • No data on capital vintages, retirement, salvage • Crude, inaccessible data on debt/equity/wealth • Hardly any study of LR preferences/behavior • Farmers’ willingness to tradeoff annual variability for serial correlation of profits is not understood 21

  22. The Move to Service Flow DataJust & Pope (2002 synthesis of ag production in the Handbook) • Lacking better capital data, service flow data has been used for estimating production • Construction relies on marginal assumptions necessary for using aggregate data • Ignores: • SR fixities & constraints • Ex post adjustment (response to state of nature) • Price/weather/pest variation • Entry/exit 22

  23. 6. Financial Structure & Off-farm ActivityOffutt (2002 AAEA Presidential Address; AJAE Dec 2002) • The family farm as a household • Most farmers’ major occupation is not farming • Hobby farming: ¾ have sales < $50K; ½ < $10K • Production behavior is affected by financial constraints • Production behavior may be motivated by consumption preferences (smoothing of risk) • Hobby farming may be a consumption activity 23

  24. Tendency Toward Reduced Form EstimationJust & Pope (2002 synthesis of ag production in the Handbook) • Thinking: Appropriately restricted reduced forms relieve data requirements (Offutt) • Estimation of outcomes w/o underlying “how” • Cannot learn basic properties of technology or preferences with reduced form models • Composition of technologies is key • Estimated parameters of reduced form or aggregate technologies embody policies 24

  25. Structure vs Reduced FormJust & Pope (2002 synthesis of ag production in the Handbook) • Lucas Critique: Models estimated under one policy cannot be used for another because the estimated parameters embody policies under which the data were generated • Solution: Capture “deep structure” • Production Structure (allocation to technologies) • Behavior given Technology & Financial Structure • Structure of Institutions & Markets • Model Change in Internal Structure vs Exogenous Factors • Policy- & Behavior-Relevant Aggregation 25

  26. Need: Widely-Accessible Panel Data • Few data sets reflect individual farms: • ARMS KSU Farm Mgmt Survey ICRISAT • Limited accessibility (access & analysis) • Debate & scientific progress is choked • Labor economics: Current Population Survey, Panel Study of Income Dynamics • Survey exposure vs matching existing data • Confidentiality – broadening the circle 26

  27. My Experience on Crop Insurance • Needed data: • FCRS (now ARMS) data on crop production • FCIC data on participation and yield histories • Piggy-back survey to get farmer perceptions • My grant paid for NASS to conduct the survey • NASS matching problems: • Delays & discarding of data • Limited access to data • Delays in research & revisions for publication • Three-quarters of research abandoned after 10 years 27

  28. Nontraditional Markets:Potential Declining Inclusiveness of Public Data • Decline in central cash markets (Ag Statistics) • Industrial Agriculture • Poultry: 90% contracted since 1950s but vertical integration doubled 1975-94; only 1% cash market • Pork: Contracted share 2% to 56% 1970-99 • Beef: Non-cash marketing 19% to 42% 1994-2000 • Internet marketing (disintermediation) • Niche markets & direct marketing • Information markets 28

  29. Consolidation in Ag Supply & Marketing:Inability to Research Concentrated Industries • The 80 largest pesticide companies have merged into 10 huge conglomerates • Noncompetitive pricing premiums are typically 20-50% and profit margins are higher • Strategic competitive practices maintain monopolies up to 10 yrs beyond patents • Public data give no way to estimate the associated welfare losses 29

  30. Anticipatory Policy Support • Actions are preceded by perceptions • Perceptions depend on information • Models are weakest w.r.t. expectations • Models are weakest when needed the most – • The formative stages of policy making • Examples: GM seeds (Starlink Corn), Risk, • Rapidity of adoption in the age of information • Understanding policy impacts may depend on understanding information markets (Internet) 32

  31. Shortcomings of Data • Heterogeneity • 1. Spatial Allocation (of Inputs to Crops) • 2. Temporal Allocation (Planting, Growing, Harvest) • 3. Statistical Distribution (Variation not only Average) • 4. Correlation of Multifunctional Attributes • 5. Capital Stock & Long-Term Behavior • 6. Financial Structure & Off-farm Activity • Identification of Structure vs Reduced Form • The Need for Widely Accessible Panel Data • Nontraditional Markets • Consolidation Issues • Anticipatory Policy Support 33

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