Welfare Effects of Herbicide Tolerant Rice Adoption in Southern Brazil

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Welfare Effects of Herbicide Tolerant Rice Adoption in Southern Brazil Fabrizio Galli (BASF) Anwar Naseem (McGill) Rohit Singla (McGill) Presented at the 16 th ICABR Annual Meetings, June 25-27, 2012, Ravello

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Welfare Effects of Herbicide Tolerant Rice Adoption in Southern Brazil. Fabrizio Galli (BASF) Anwar Naseem (McGill) Rohit Singla (McGill) Presented at the 16 th ICABR Annual Meetings, June 25-27, 2012, Ravello. Motivation. - PowerPoint PPT Presentation

Transcript of Welfare Effects of Herbicide Tolerant Rice Adoption in Southern Brazil

Page 1: Welfare Effects of Herbicide Tolerant Rice Adoption in Southern Brazil

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

Page 2: Welfare Effects of Herbicide Tolerant Rice Adoption in Southern Brazil

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?

Page 3: Welfare Effects of Herbicide Tolerant Rice Adoption in Southern Brazil

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

rice area 1 million hectarespaddy rice production 5.3 million tonsarea growth 2%yield growth 1%

Rio Grande do Sul (1990-2010)

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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.

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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

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Economic Surplus ModelSmall Open Economy

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Methodology (cont.) RtttRtt KKQPTSPS 5.01

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- price of rice in Rio Grande do Sul

- quantity of rice produced in RS prior to CR introduction

- rice supply elasticity in Brazil

- expected proportionate yield change per hectare

- proportionate change in input cost per hectare

- probability that CR will achieve the expected yield

- adoption rate of CR

- technology depreciation factor

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Methodology (cont.)Firm profits model - Hareau, Mills and Norton (2006)

tttt LA

- technology fee charge per hectare

- adoption rate of technology

- crop area.

t

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- research and development costs – sunk costs

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Data and parameters YE- estimation of yield change per hectare -

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Variable Categories

1X Nature of rice variety

CR or non-CR (conventional).

2X Age of farmer young, mid-age, senior

3X Level of education of household head

4X Geographic area within RS

south-eastern, south-western, mid-western, mid-eastern or capital area.

5X Farm size small, medium or large farms.

6X Tillage system tillage, no-tillage, semi-tillage, pre-germ. seeds or transp. seeds.

ttR

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Data and parameters (cont.) YE- estimation of yield change per hectare -

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Variable Source

1Z Percentage share of land planted to conventional rice. BASF

2Z Rainfall in RS. Brazil’s Ministry of Environment

3Z Temperature in RS. GISS Temperature Analysis

4Z Time trend for 1994-2010. -

ttR

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Data and parameters (cont.)- estimation of input cost change per hectare - CE

USD/haCost share (%)

USD/haCost share(%)

1. Soil preparation 230.28 13.11 230.28 13.172. Soil drainage 64.16 3.65 64.16 3.673. NPK / Top dressing 229.77 13.09 229.77 13.144. Seeds 94.83 5.40 108.62 6.215. NPK / Top dressing application

and Sowing operations68.33 3.89 68.33 3.91

6. Irrigation 327.48 18.65 327.48 18.737. Weed and pest management 169.71 9.66 148.81 8.518. Harvest 241.26 13.74 241.26 13.809. Inner farm transportation 51.44 2.93 51.44 2.94

10. Freight 132.01 7.52 132.01 7.5511. Rice drying 146.70 8.35 146.70 8.39

12. Total variable cost 1,755.96 100 1,748.85 100

13. Cost change (%) -0.41

Conventional Clearfield

Source: IRGA (2010).

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Data and parameters (cont.)- estimation of input cost change per hectare - CE

Mean Std. Dev. Mean Std. Dev.

1. Seed cost (USD/ha) 75.04 10 86.72 112. Labor3. Land preparation (hrs/ha) 5.11 2 5.23 24. Weeding (hrs/ha) 0.25 0 0.44 05. Herbicide application (hrs/ha) 0.44 0 0.42 0.276. Inseticide / fungicide

application (hrs/ha)0.45 0 0.49 0

7. Total labor 6.25 - 6.58 -

8. NPK fertilization (USD/ha) 190.94 150 188.63 170

9. Top dressing fertilization (USD/ha) 134.85 94 130.44 87

10. Herbicides / Pesticides (USD/ha) 132.60 103 123.18 105

11. Cost change (%) -0.71

Conventional Clearfield

Source: Kleffmann (2010).

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Data and parameters - summary

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Period of analysis 2004-2018 Probability of success 100%

Depreciation rate of technology 2004-2010: 2%. 2011-2018: 4%.

Production quantity (2006-2010) 7,147 thousand tons (IRGA) Price of rice in RS (2006-2010) USD 269.45/ton (CEPEA) Price elasticity of supply 0.440 (Cap et al. 2006)

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Results and discussion - 665544332211)( XXXXXXYLog

(1) (2) (3) (4)1. CR dummy 0.181 0.181 0.178 0.156

(2.96)** (2.95)** (2.90)** (2.48)*Household characteristics

2. Education (years) 0.007 0.007 0.007-1.43 -1.66 -1.54

Age3. up to 30 years old 0.071 0.08 0.074

-0.81 -0.91 -0.854. older than 60 years old -0.022 -0.04 -0.027

-0.35 -0.64 -0.43Regional effects

5. Capital area RS 0.047 0.047 0.028 0.05-0.5 -0.5 -0.3 -0.53

6. Southwest RS -0.186 -0.185 -0.165 -0.192(2.32)* (2.33)* (2.09)* (2.41)*

7. Mid-west RS -0.285 -0.279 -0.278 -0.284(2.18)* (2.14)* (2.13)* (2.18)*

8. Mid-east RS -0.069 -0.066 -0.042 -0.063-0.52 -0.5 -0.32 -0.47

Farm size9. Mid-size farms (200-1000ha) -0.079 -0.074 -0.072 -0.072

-1.21 -1.14 -1.11 -1.1110. Large farms (>1000ha) 0.106 0.116 0.123 0.115

-1.04 -1.14 -1.22 -1.13Sowing operations

11. Semi-tillage -0.078 -0.085 -0.06 -0.079-0.91 -0.99 -0.7 -0.92

12. Conventional tillage -0.303 -0.31 -0.281 -0.547(2.16)* (2.22)* (2.01)* (2.75)**

13. Pre-germinated seeds -0.024 -0.028 -0.006 -0.035-0.18 -0.21 -0.04 -0.25

14. Transplanted seeds -0.239 -0.266 -0.199 -0.214-0.35 -0.4 -0.29 -0.32

15. Conventional tillage x CR 0.412-1.73

16. Observations 597 597 598 597Absolute value of t statistics in parentheses.* significant at 5%; ** significant at 1%.

Yields (ton/ha) in Log

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Results and discussion (cont.) - '44332211)( ZZZZYLog

(1) (2)1. Conventional rice adoption -0.666 -0.359

(5.47)** (2.22)*

2. CR adoptiona 0.486 0.302

3. Rainfall (mm/year) -0.001 -0.001-1.65 (2.37)*

4. Temperature (oC/year) 0.087 0.055-1.71 -1.21

5. Time trend 0.017(2.46)*

6. Adj. R-squared 0.71 0.79Absolute value of t statistics in parentheses* significant at 5%; ** significant at 1%

Yields (ton/ha) in Logs

- 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%

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Results and discussion (cont.)- Baseline: NPV of change in surplus, 2009-2018 (million USD).

- NPV of change in surplus under IPR enforcement, 2009-2018 (million USD).

Producers' surplus ($) 14,412Technology revenue ($) 6,315Total surplus ($) 20,727Producers (%) 69.5BASF (%) 30.5

Producers' surplus ($) 26,398Technology revenue ($) 12,631Total surplus ($) 39,028Producers (%) 67.6BASF (%) 32.4

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Sensitivity results for yield change Baseline (ΔY=15%) Sensitivity (ΔY=20%)Producers' surplus ($) 14,412 18,712Technology revenue ($) 6,315 6,315Total surplus ($) 20,727 25,027Producers (%) 69.5 74.8BASF (%) 30.5 25.2

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Million USD20% yield shift Baseline

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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 4.27Source: IRGA, BASF.

Certified CR

Farm-savedFarm-saved, bred

for sale

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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.

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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

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),,,,,()( TillageFarmSizeGeoAreaEducAgeCRfyieldLn

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).

Back up slide