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 DCNovember 6, 2002

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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)

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Shortcomings of DataHeterogeneity

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1. Spatial Allocation (of Inputs to Crops)2. Temporal Allocation (Planting, Growing, Harvest) 3. Statistical Distribution (Variation not only Average)4. Correlation of Multifunctional Attributes5. Capital Stock & Long-Term Behavior6. Financial Structure & Off-farm Activity

Identification of Structure vs Reduced Form

Widely Accessible Panel Data is Needed

Nontraditional Markets

Consolidation Issues

Anticipatory Policy Support

Today’s Farm Sector is Truly DiverseOffutt (2002 AAEA Presidential Address; AJAE Dec 2002)

Size

Choice of farming enterprise(s)

Business organization

Environmental performance

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Analysts are obligated to investigate differential response and impact.

Relying on aggregate data has obscured distributional facts about effects of ag policy.

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

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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

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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

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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)

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If policy responses are affected by these, aggregate models that ignore heterogeneity are inappropriate

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 motivationsRisk aversion vs

Simple profit max (or loss minimization)

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To understand behavior, we must study decisions given information available to the farmer at the time

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)

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Why are large subsidies required if SR risk matters?

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?

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Most risk effects are subject to identification problems.Without allocations, discernment is possible only artificially

(by imposing assumptions which in effect determine results).

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 riskFarm-level yield variation 2-10 times greater than aggregate data (Just & Weninger AJAE May 1999)

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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

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The alternative: Assume identical farms & circumstances

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

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An Environmental Use Restriction

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Input Intensity: Input-Output Ratio (x/y)

Environmental Sensitivity:

Pollution- Output Ratio

(z/y)

An Input Intensity Restriction

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Input Intensity: Input-Output Ratio (x/y)

Environmental Sensitivity:

Pollution- Output Ratio

(z/y)

Effect of a Target Price

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Input-Output Ratio (x/y)

Pollution- Output Ratio

(z/y)

Py PyT

Px Px

Social Optimality

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Input-Output Ratio (x/y)

Pollution- Output Ratio

(z/y)

Py = Marginal value of output

Px = Marginal cost of input

Pz = Marginal cost of pollution

Py Y + P

x X – Pz Z > 0

Py Y + P

x X – Pz Z = 0

Py/Pz

Slope -Px/Pz

Social Optimality: Environmental Use Restriction Works Well

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Input-Output Ratio (x/y)

Pollution- Output Ratio

(z/y)

Py = Marginal value of output

Px = Marginal cost of input

Pz = Marginal cost of pollution

PyY + P

xX – PzZ > 0

PyY + P

xX – PzZ = 0

Py/Pz

Slope -Px/Pz

Social Optimality: Input Intensity Restriction Works Well

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Input-Output Ratio (x/y)

Pollution- Output Ratio

(z/y)

Py = Marginal value of output

Px = Marginal cost of input

Pz = Marginal cost of pollution

Py Y

+ Px X

– Pz Z

> 0

Py Y

+ Px X

– Pz Z

= 0

Slope -Px/Pz

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/behaviorFarmers’ willingness to tradeoff annual variability for serial correlation of profits is not understood

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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

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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

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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

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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

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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

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

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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 dataDelays in research & revisions for publication

Three-quarters of research abandoned after 10 years

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Nontraditional Markets:Potential Declining Inclusiveness of Public Data

Decline in central cash markets (Ag Statistics)

Industrial AgriculturePoultry: 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

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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

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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)

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Shortcomings of DataHeterogeneity

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Identification of Structure vs Reduced FormThe Need for Widely Accessible Panel Data

• Nontraditional Markets• Consolidation Issues• Anticipatory Policy Support

1. Spatial Allocation (of Inputs to Crops)2. Temporal Allocation (Planting, Growing, Harvest) 3. Statistical Distribution (Variation not only Average)4. Correlation of Multifunctional Attributes5. Capital Stock & Long-Term Behavior6. Financial Structure & Off-farm Activity