Welfare Effects of Herbicide Tolerant
Rice Adoption in Southern Brazil
Fabrizio Galli (BASF)
Anwar Naseem (McGill)
Rohit Singla (McGill)
Presented at the 16th ICABR Annual Meetings, June 25-27, 2012, Ravello
Motivation
• Protection of intellectual property rights (IPRs)
are meant to provide an incentive for private
R&D.
– Benefit: New innovations; growth (dynamic
efficiency)
– Cost: Monopoly rents for innovator (static
inefficiency)
• What is the evidence for these claims?
Specific Context
• Clearfield rice in Brazil
–
–
–
–
Herbicide tolerant rice
Effective in red rice control
Non genetically modified
Introduced by BASF in 2004; 55% in 2010; half illegally
grown
– Majority of rice in Brazil grown in Rio Grande do Sul
Rio Grande do Sul (1990-2010)
rice area
1 million hectares
paddy rice production
5.3 million tons
area growth
2%
yield growth
1%
Objectives
• Evaluate the farm level impacts resulting from
Clearfield
• Estimate the change in social welfare from
introduction of Clearfield
• Quantify the economic benefits captured by
the technology provider
• Examine surplus changes from introducing
stronger IPR system.
Methodology
• To estimate the economic impact on
producers, use economic surplus model of
Alston, Norton and Pardey (1995)
• To estimate benefits to technology supplier
use firm profits model of Moschini and Lapan
(1997)
• We assume a small open economy
Economic Surplus Model
Small Open Economy
Price
S0
S1
a
Pw
b
PSt  TS t  PR Qt K t 1 0.5K t  R 
 E Y  E C  
Kt 

 At 1 t 
  R 1 E Y  
I0
D
QT0
QT1
I1
C0
Q0
Q1
Quantity
Price
S0
Methodology (cont.)
S1
a
Pw
b
PSt  TS t  PR Qt K t 1 0.5K t  R 
I0
D
PR - price of rice in Rio Grande do Sul
Qt - quantity of rice produced in RS prior to CR introduction
 R - rice supply elasticity in Brazil
QT0
QT1
I1
 E Y  E C  
Kt 

 At 1 t 



1

E
Y
 R

EY  - expected proportionate yield change per hectare
EC  - proportionate change in input cost per hectare

- probability that CR will achieve the expected yield
At
- adoption rate of CR
 t - technology depreciation factor
C0
Q0
Q1
Quantity
Methodology (cont.)
Firm profits model - Hareau, Mills and Norton (2006)
 t   t At Lt
 t - technology fee charge per hectare
At - adoption rate of technology
Lt - crop area.
- research and development costs – sunk costs
Data and parameters
- estimation of yield change per hectare - E Y 
 E Y  E C  

Kt 
 At 1 t 
  R 1 E Y 
Log (Y )  1 X 1   2 X 2   3 X 3   4 X 4   5 X 5   6 X 6  
Variable
X3
Nature of rice
variety
Age of farmer
Level of education
of household head
X4
Geographic area
within RS
X5
Farm size
X6
Tillage system
X1
X2
Categories
CR or non-CR
(conventional).
young, mid-age, senior
south-eastern, southwestern, mid-western, mideastern or capital area.
small, medium or large
farms.
tillage, no-tillage, semitillage, pre-germ. seeds or
transp. seeds.
 E Y  E C  

Kt  
 At 1 t
 R 1 E Y  
Data and parameters (cont.)
- estimation of yield change per hectare - E Y 
Log (Y )   1 Z 1   2 Z 2   3 Z 3   4 Z 4   '
Variable
Z1
Z2
Z3
Z4
Percentage share of land
planted to conventional rice.
Rainfall in RS.
Temperature in RS.
Time trend for 1994-2010.
Source
BASF
Brazil’s Ministry of
Environment
GISS Temperature Analysis
-

Data and parameters (cont.)
- estimation of input cost change per hectare - EC 
Conventional
230.28
64.16
229.77
94.83
68.33
Cost
share
(%)
13.11
3.65
13.09
5.40
3.89
327.48
169.71
241.26
51.44
132.01
146.70
1,755.96
USD/ha
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Soil preparation
Soil drainage
NPK / Top dressing
Seeds
NPK / Top dressing application
and Sowing operations
Irrigation
Weed and pest management
Harvest
Inner farm transportation
Freight
Rice drying
12. Total variable cost
13. Cost change (%)
Source: IRGA (2010).
Clearfield
230.28
64.16
229.77
108.62
68.33
Cost
share
(%)
13.17
3.67
13.14
6.21
3.91
18.65
9.66
13.74
2.93
7.52
8.35
327.48
148.81
241.26
51.44
132.01
146.70
18.73
8.51
13.80
2.94
7.55
8.39
100
1,748.85
100
USD/ha
-0.41
Data and parameters (cont.)
- estimation of input cost change per hectare - EC 
Conventional
1.
Seed cost (USD/ha)
2.
Labor
Clearfield
Mean
75.04
Std. Dev.
10
Mean
86.72
Std. Dev.
11
3.
Land preparation (hrs/ha)
5.11
2
5.23
2
4.
Weeding (hrs/ha)
0.25
0
0.44
0
5.
6.
Herbicide application (hrs/ha)
Inseticide / fungicide
application (hrs/ha)
0.44
0.45
0
0
0.42
0.49
0.27
0
6.25
-
6.58
-
7.
Total labor
8.
NPK fertilization (USD/ha)
190.94
150
188.63
170
9.
Top dressing fertilization (USD/ha)
134.85
94
130.44
87
132.60
103
123.18
105
10. Herbicides / Pesticides (USD/ha)
11. Cost change (%)
Source: Kleffmann (2010).
-0.71
Data and parameters - summary
PSt  TS t  PR Qt K t 1 0.5K t  R 
 E Y  E C  
Kt 

 At 1 t 
  R 1 E Y  
Period of analysis
Probability of success
Depreciation rate of technology
Production quantity (2006-2010)
Price of rice in RS (2006-2010)
Price elasticity of supply
2004-2018
100%
2004-2010: 2%.
2011-2018: 4%.
7,147 thousand tons (IRGA)
USD 269.45/ton (CEPEA)
0.440 (Cap et al. 2006)
Results and discussion - Log(Y )   X
1
1. CR dummy
2.
Household characteristics
Education (years)
3.
Age
up to 30 years old
4.
older than 60 years old
5.
Regional effects
Capital area RS
6.
Southwest RS
7.
Mid-west RS
8.
Mid-east RS
9.
10.
Farm size
Mid-size farms (200-1000ha)
Large farms (>1000ha)
Sowing operations
11. Semi-tillage
12.
Conventional tillage
13.
Pre-germinated seeds
14.
Transplanted seeds
(1)
0.181
(2.96)**
0.007
-1.43
1
  2 X 2  3 X 3   4 X 4  5 X 5  6 X 6  
Yields (ton/ha) in Log
(2)
(3)
0.181
0.178
(2.95)**
(2.90)**
0.007
-1.66
0.071
-0.81
-0.022
-0.35
0.007
-1.54
0.08
-0.91
-0.04
-0.64
0.074
-0.85
-0.027
-0.43
0.047
-0.5
-0.186
(2.32)*
-0.285
(2.18)*
-0.069
-0.52
0.047
-0.5
-0.185
(2.33)*
-0.279
(2.14)*
-0.066
-0.5
0.028
-0.3
-0.165
(2.09)*
-0.278
(2.13)*
-0.042
-0.32
0.05
-0.53
-0.192
(2.41)*
-0.284
(2.18)*
-0.063
-0.47
-0.079
-1.21
0.106
-1.04
-0.074
-1.14
0.116
-1.14
-0.072
-1.11
0.123
-1.22
-0.072
-1.11
0.115
-1.13
-0.078
-0.91
-0.303
(2.16)*
-0.024
-0.18
-0.239
-0.35
-0.085
-0.99
-0.31
(2.22)*
-0.028
-0.21
-0.266
-0.4
-0.06
-0.7
-0.281
(2.01)*
-0.006
-0.04
-0.199
-0.29
597
597
598
-0.079
-0.92
-0.547
(2.75)**
-0.035
-0.25
-0.214
-0.32
0.412
-1.73
597
15. Conventional tillage x CR
16. Observations
Absolute value of t statistics in parentheses.
* significant at 5%; ** significant at 1%.
(4)
0.156
(2.48)*
Results and discussion (cont.) - Log (Y )   Z
1
1
  2Z2  3Z3   4Z4   '
Yields (ton/ha) in Logs
(1)
-0.666
(5.47)**
(2)
-0.359
(2.22)*
2. CR adoption
0.486
0.302
3. Rainfall (mm/year)
-0.001
-1.65
0.087
-1.71
-0.001
(2.37)*
0.055
-1.21
0.017
(2.46)*
0.79
1. Conventional rice adoption
a
o
4. Temperature ( C/year)
5. Time trend
6. Adj. R-squared
Absolute value of t statistics in parentheses
* significant at 5%; ** significant at 1%
0.71
- specification (1): impact of CR adoption on yield = + 50% (overstated).
- specification (2): impact of CR adoption on yield = + 30% (acceptable).
Certified CR
Yield increase: 15%
Cost reduction: 1%
Results and discussion (cont.)
- Baseline: NPV of change in surplus, 2009-2018 (million USD).
Producers' surplus ($)
Technology revenue ($)
Total surplus ($)
Producers (%)
BASF (%)
14,412
6,315
20,727
69.5
30.5
- NPV of change in surplus under IPR enforcement, 2009-2018 (million USD).
Producers' surplus ($)
Technology revenue ($)
Total surplus ($)
Producers (%)
BASF (%)
26,398
12,631
39,028
67.6
32.4
Sensitivity results for yield change
Producers' surplus ($)
Technology revenue ($)
Total surplus ($)
Producers (%)
BASF (%)
Million USD
400
Baseline (ΔY=15%)
14,412
6,315
20,727
69.5
30.5
20% yield shift
Sensitivity (ΔY=20%)
18,712
6,315
25,027
74.8
25.2
Baseline
350
300
250
200
150
100
50
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Cost sensitivity analysis
Seed cost
(USD/bag)
Seed cost share in
total variable cost (%)
Certified CR
108.62
6.21
Farm-saved
59.10
3.48
Farm-saved marketed
73.10
4.27
Source: IRGA, BASF.
NPV of producer surplus (million USD)
3,500
Certified CR
3,000
2,500
2,000
1,500
1,000
Farm-saved
500
Farm-saved, bred
for sale
0
0
-500
20
40
60
80
100
120
Seed cost (USD/ha)
140
Conclusion
- innovators do not extract monopoly rents, corroborating with Falck-Zepeda,
Traxler, and Nelson 2000; Falck-Zepeda, Traxler, and Nelson 2000; Pray et
al. 2001; Qaim and Traxler 2005; Hareau, Mills, and Norton 2006.
- complete IPR enforcement  economic agents (producers and innovators)
would gain considerably and order of beneficiaries not reversed.
- favourable economic environment under strict IPRs
- official CR more efficient than illegal and conventional rice  dissemination
of information.
Limitations and future research
- detailed farm level data to assess E(Y) and E(C)
- probability distribution to certain model parameters
- relationship between adoption and resulting cost reduction
- contingent valuation  willingness to pay for Clearfield Rice
Back up slide
Objectives slide:
- examine whether the public goods nature of invention is managed
by IPR exclusion mechanisms and whether the technology supplier
earns economic rents.
- favourable economic environment for firms to invest in research
(Pray, Govindasamy and Courtmanche 2003).
Ln( yield )  f (CR, Age, Educ, GeoArea, FarmSize, Tillage )