"estimating the determinants and effects of participation in the usda's conservation...
TRANSCRIPT
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"Estimating the Determinants and Effects of Participation in the USDA's Conservation Reserve
Program."
Prepared for: Camp Resources XVAugust 7-8, 2008
Jacob N. BrimlowPh.D. Candidate
Agricultural and Resource EconomicsNCSU
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USDA Conservation Reserve Program
(CRP)
• Goals:
• reduce soil erosion, enhance air and water quality, expand and improve wildlife habitat and wetlands
• Retires cropland using 10-15 year contracts
• Compensates landowners using annual rental payments, cost share assistance, and incentive payments
National Enrollment : 36.8 million acres ($1.8b/yr)
Minnesota Enrollment: 1.8 million acres
**
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Enrolling in the CRP ( “general” sign-ups since 1990)
Landowner “Bids”:
The Environmental Benefits Index (EBI) Score
• Landowner chooses land to enroll, conservation practice to adopt, and rental rate/cost share assistance to request
• Seven EBI factors are tallied to compute overall score
- cost factor penalizes higher rental rate bids
• Bids are ranked by EBI score, and EBI cutoff determined by FSA after all bids are received
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Question
Does enrollment in the CRP affect the value of enrolled farmland?
Who cares?
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Hypothesis
Does enrollment in the CRP affect the value of enrolled farmland?
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Hypothesis
Yes
Does enrollment in the CRP affect the value of enrolled farmland?
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Hypothesis
Note: CRP is voluntaryAssume: Landowners are profit
maximizers
landowners will not enroll in CRP unless profits increase
parcels restricted under CRP will be worth at least as much as those that are not
Hypothesis: Ceteris paribus, CRP enrollment will have a non-negative effect on the value of enrolled farmland
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Estimation
• y - vector of explanatory variables (productivity, location, etc)
• A* - CRP enrollment
and I think
• z - vector of explanatory variables (productivity, location, etc)
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Enrollment
Does CRP enrollment depend, empirically, on variables likely to influence land value?
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Estimation: Enrollment
• County Data• Parks and Kramer (1995)• Esseks and Kraft (1988)• Plantinga, et. al. (1990)• Goodwin and Smith (2003)• Isik and Yang (2004)
• County/Farm Data• Roberts and Lubowski (2007) • Jake
• z - vector of explanatory variables (productivity, location, etc)
• A* - CRP enrollment (continuous or binary)
• Key Variables: Land Productivity, Government payments, Erosion, CRP bids/payments
• Results: Mixed
Literature
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Estimation
• y - vector of explanatory variables (productivity, location, etc)
• A* - CRP enrollment
and I’m pretty sure
• z - vector of explanatory variables (productivity, location, etc)
selection bias/correlated errors...
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• Positive Effect• Shoemaker (1989)*
• No Effect (Insignificant)• Vitaliano and Hill (1994)• Nickerson and Lynch (2001)
• Negative Effect• Taff (2004)*• Shultz and Taff (2004)• Anderson and Weinhold (2005)• Taff and Weisberg (2007)*• Goodwin, et. al. (working)
*Estimate the effect of enrollment in the CRP
• Key Variables: Land Productivity, Location
• Estimation Issues: Selection bias, data quality, sample size
Estimation: Farmland Value and
Conservation Programs
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Farmland Value
Township Data• (Log of) Farmland value per acre 2007• Proportion of township enrolled in CRP• Productivity (CPI): Scale 1-100
• weighted average productivity of township by soil• proportion of land in productivity “grade”
• Population• growth 1990-2000, 2000-2007• level 1990, 2000-2007
• Location (county, NASS region)
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Let’s talk about...
- data resolution (county, township, parcel, mixed)
- data type (spatial?)
- look at eligible cropland only
- estimation strategy (two-stage/IV, diff in diff, spatial?)
- option values (option to enroll, option to develop)
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Thank You!
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0
100
A
$/yr
150
0 .5 A
50
A
$/yr
Average Productivity: 100
Average Productivity: 100
$CRP
AA
Parcel 2Parcel 1
Enrollment: 0 Enrollment: .5A
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17
Quick View: Enrollment Data
– Farm-level Data:
• CRP enrollment (tillable acres)
• Average productivity (CER...more to follow...)
• Tillable acreage
• Location (county, NASS region)
–
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Quick View: Enrollment Regression
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Productivity Data
Parcel/Farm AnalysisCrop Equivalency Rating (CER)- county index 0-100- captures erosion, climate, soil permeability- generated in some counties as early as 1972- only available in select counties
Township AnalysisCrop Productivity Index (CPI)- county index 0-100- captures ability of soil grow corn- NO erosion or climate adjustments- generated 2007- available state-wide
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Censored Regression
Acres = A
$/year
A1*, A2* = 0 A3* A5* = A
c
NP1
A4*
NP2
NP3
NP4
NP5
Observation summary: 4240 left-censored 246 uncensored 42 right-censored
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Model Implication
Where A* is the acreage enrolled, c is the per acre payment for retiring land from production, and z is a vector of variables that affect the net productivity of land.
Test using:
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Crop Equivalency Rating (CER)
• Reflects the net economic return per acre of soil when property is managed for the highest net return.
– adjusted for weather
– CER’s are relative to other properties in each county
• But, – limits sample size
– out of date
•
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Enrollment Summary Stats
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A
$/year
0 A
Model
NP1= a0 + a1 z1 + a2 A
NP2= a0 + a1 z2 + a2 A
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A
$/year
0 A1*
s1
s2
b
A
c
Model
NP1= a0 + a1 z1 + a1 A
NP2= a0 + a1 z2 + a1 A
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The Conservation Reserve Program
(CRP)
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Enrollment: Remaining Issues
1. Productivity Data (Crop Equivalency Rating - CER)- covers only some counties, and is out of date
2. Productivity-Acreage Link- confidentiality issues have made finer resolution difficult
3. Government Payments Data- land characteristics or dollars ?