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
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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)
2
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
Today’s Farm Sector is Truly Diverse
Offutt (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.
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Unique Features of Ag Production
Just & 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
Water Availability
Climate/Weather/Pests
Environmental Sensitivity
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The Problem of Heterogeneity
Just & 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 Allocation
Just (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 Models
Just & 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
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2. Temporal Allocation
Just (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
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The Crop Insurance Example
Just/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)
Crop Revenue Coverage (CRC)
Income Protection (IP)
Catastrophic Risk Protection (CAT)
Revenue Assurance (RA)
Group Risk Protection (GRP)
Why are large subsidies required if SR risk matters?
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The Identification Problem
Just (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).
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Cannot Truly Test New Advances
with 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)
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3. Statistical Distribution
Just & 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
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4. Correlation of Attributes
Just & 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
Environmental
Sensitivity:
PollutionOutput Ratio
(z/y)
Input Intensity: Input-Output Ratio (x/y)
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An Input Intensity Restriction
Environmental
Sensitivity:
PollutionOutput Ratio
(z/y)
Input Intensity: Input-Output Ratio (x/y)
15
Effect of a Target Price
PollutionOutput Ratio
(z/y)
Py PyT
Px Px
Input-Output Ratio (x/y)
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Social Optimality
PollutionOutput Ratio
(z/y)
Slope -Px/Pz
Py = Marginal value of output
Px = Marginal cost of input
Pz = Marginal cost of pollution
Py/Pz
Input-Output Ratio (x/y)
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Social Optimality: Environmental Use Restriction Works Well
PollutionOutput Ratio
(z/y)
Slope -Px/Pz
Py = Marginal value of output
Px = Marginal cost of input
Pz = Marginal cost of pollution
Py/Pz
Input-Output Ratio (x/y)
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Social Optimality: Input Intensity Restriction Works Well
PollutionOutput Ratio
(z/y)
Slope -Px/Pz
Py = Marginal value of output
Px = Marginal cost of input
Pz = Marginal cost of pollution
Input-Output Ratio (x/y)
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5. Capital Stock & LR Behavior
Just & 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
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The Move to Service Flow Data
Just & 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 Activity
Offutt (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 Estimation
Just & 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 Form
Just & 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
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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 data
Delays 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 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
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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
29
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 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
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