Crop Insurance and Pesticide Use · 2016-11-02 · Insurance uptake Influence? 2. Pesticide use. 1. System of land use equations (acreage of four field crop groups) and insurance

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Robert Finger*, Niklas Möhring*, Tobias Dalhaus*, Geoffroy Enjolras ǂ

*ETH ZürichǂUniversité Grenoble-Alpes [email protected]

Oct 3rd, 2016Niklas Möhring 1

The Effects of Crop Insurance on Pesticide Use


Pesticide use on the top of agricultural policy in Europe (Lefebvre et al. 2015; Chabé-Ferret and Subervie2013)

National Action Plans introduced in the EU

Pesticide taxation schemes introduced in four EU countries (Böcker and Finger, 2016)

Risk management tools like insurances increasingly subsidized in Europe (Meuwissen et al.,2013; El Benni et al., 2015; Bardajíet al. 2016)

New, innovative tools are developed (Norton et al., 2016)


Pesticide Use and Insurance Systems on the Top of European AgPolicy

Dependencies in policies?


1. Insurances claimed to be substitutes for pesticides (example revenue insurance) intensive margin effect (Mishra et al. 2005)

2. Insurance uptake might influence land use decisions and therefore pesticide use extensive margin effect (Wu, 1999)

3. Decisions of pesticide use and insurance uptake both determined by risk preferences and perceptions (example hail insurance) (Waterfield and Zilberman, 2012; Menapace et al. 2016)


Pesticide use and Insurance uptake potentially linked through three channels

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Literature: Ambiguous effects of insuranceuptake on pesticide use found

Effect Pesticide type Case study Paper

Yield Insurance:

Insecticide Maize in Iowa Feinerman (1992)

Herbicide, Insecticide Maize in ten states oft he USA

Horowitz and Lichtenberg (1993)

Chemical Inputs Field crops in Kansas Smith and Goodwin (1996)

Chemical Inputs Field crops in Nebraska Wu (1999)

Chemical Inputs Maize, soy bean in the Corn Belt; Wheat and rye in the Great Plains

Goodwin et al. (2004)

n.s. PPP, Chemical Inputs Wine in France Aubert and Enjolras (2014)

Revenue Insurance

n.s. PPP Wheat in the USA Mishra et al. (2005)

Hail Insurance

PPP Rape seed in France Chakir and Hardelin (2014)

Legend: , , n.s. indicate significant increasing, decreasing or no significant effect of insurance on pesticide use. Goodwin et al. (2004) find mixed evidence depending on the case study.

Accounts for landuse decisions


Does crop insurance influence pesticide use?

a. Do extensive margin effects play a role?

b. Do results differ w.r.t. different insurance systems (case studiesfor France and Switzerland)?

c. Does the specification of pesticide use (monetary units, physicalunits, pesticide types) matter?


Research questions


Conceptual and econometricframework

1. Land use decision/Insurance uptake

Influence?

2. Pesticide use

1. System of land use equations(acreage of four field crop groups) and insurance use equation (binary)

Method: Simulated Maximum Likelikood Estimation (Roodman, 2009)

Predicted valuesas instruments(Schoengold et al.,2006)

2. Effect of predicted land use/ insurance uptake on pesticide use(3 different specifications tested)

*Source of symbols used: OCHA


Explanatory variables 1st step:lagged land use, lagged insurance uptake, farm- and farmers’ characteristics, climate conditions (long-term), climatic risks


Data overview

*Data from central evaluation of agri-environmental indicators.

Swiss dataset French datasetData(year 2010)

field-level data*, bookkeeping data andweather data

merged FADN data and weatherdata

Sample size 154 farms 2704 farms

Insurance type hail insurance (not subsidized) multiperil insurance (max. subsidization of 65%)

Pesticidemeasures

expenditures, physical units (overall andin types)

expenditures

Group of crops grassland, intensive cereals, extensive cereals, root crops

Explanatory variables 2nd step:predicted land use, predicted insurance uptake, farm- and farmers’ characteristics, weather (current year), fertilizer use


Feedback effects of lagged land use and insurancedecisions (both samples)

Linkages between crops (rotation)

Effect of climatic conditions on land use (both samples) and insurance uptake (only French sample)

Extensive margin effect of insurance (only Swiss sample) – seems to depend on insurance system


Key results 1st step:

land use and insurance uptake decisions


Positive significant effect of insurance on pesticideexpenditures (French sample)

Positive significant effect of insurance on fungicidequantities (Swiss sample – strong subjective componentin fungicide application: Ramseier et al.,2016)

Positive correlation between herbicide, fungicide andinsecticide use (Swiss sample)

Positive interactions between pesticide and fertilizer use(both samples)


Key results 2nd step:

pesticide use decisions (different specifications)


Conclusions

Allignement of agricultural policies needed:

Interdependency of pesticide use and insurance uptake (extensive margin effect)

Interaction of fertilizer and pesticide use

Analysis of interrelation between insurance and pesticideuse is sensitive to the specification of pesticides

Evidence that risk preferences and attitudes matter forpesticide use (and insurance uptake)

Thank you for your attention!

Contact: [email protected]

Literature I• Aubert, M., & Enjolras, G. (2014). The Determinants of Chemical Input Use in Agriculture: A Dynamic

Analysis of the Wine Grape-Growing Sector in France. Journal of Wine Economics, 9(1), 75.• Bardají, I., Garrido, A., Blanco, I., Felis, A., Sumpsi, J.-M., García-Azcárate, T., Enjolras, G., and Capitanio,

F. (2016), "State of Play of Risk Management Tools Implemented by Member States During the Period 2014-2020: National and European Frameworks", European Parliament, 146 pages.

• Böcker, T., & Finger, R. (2016). European Pesticide Tax Schemes in Comparison: An Analysis of Experiences and Developments. Sustainability, 8(4), 378.

• Chabé-Ferret, S., & Subervie, J. (2013). How much green for the buck? Estimating additional and windfall effects of French agro-environmental schemes by DID-matching. Journal of Environmental Economics and Management, 65(1), 12-27.

• Chakir, R. & Hardelin, J. (2014). Crop Insurance and pesticide use in French agriculture: an empirical analysis. Review of Agricultural and Environmental Studies, 95(1), 25-50.

• El Benni, N., Finger, R., & Meuwissen, M. P. (2015). Potential effects of the income stabilisation tool (IST) in Swiss agriculture. European Review of Agricultural Economics, jbv023.

• Feinerman, E., Herriges, J. A., & Holtkamp, D. (1992). Crop insurance as a mechanism for reducing pesticide usage: a representative farm analysis. Review of agricultural economics, 14(2), 169-186.

• Goodwin, B. K., Vandeveer, M. L., & Deal, J. L. (2004). An empirical analysis of acreage effects of participation in the federal crop insurance program. American Journal of Agricultural Economics, 86(4), 1058-1077.

• Horowitz, J. K., & Lichtenberg, E. (1993). Insurance, moral hazard, and chemical use in agriculture. American journal of agricultural economics, 75(4), 926-935.

• Lefebvre, M., Langrell, S. R., & Gomez-y-Paloma, S. (2015). Incentives and policies for integrated pest management in Europe: a review. Agronomy for Sustainable Development, 35(1), 27-45.

Literature II• Menapace, L., Colson, G., & Raffaelli, R. (2016). A comparison of hypothetical risk attitude elicitation

instruments for explaining farmer crop insurance purchases. European Review of Agricultural Economics, 43(1), 113-135.

• Meuwissen, M. P., Assefa, T. T., & Asseldonk, M. A. (2013). Supporting insurance in European agriculture: Experience of mutuals in the Netherlands. EuroChoices, 12(3), 10-16.

• Mishra, A. K., Nimon, R. W., & El-Osta, H. S. (2005). Is moral hazard good for the environment? Revenue insurance and chemical input use. Journal of environmental management, 74(1), 11-20.

• Norton, M., van Sprundel, G. J., Turvey, C. G., & Meuwissen, M. P. (2016). Applying weather index insurance to agricultural pest and disease risks. International Journal of Pest Management, 1-10.

• Roodman, D. (2009). Estimating fully observed recursive mixed-process models with cmp. Available at SSRN 1392466.

• Schoengold, K., Sunding, D. L., & Moreno, G. (2006). Price elasticity reconsidered: Panel estimation of an agricultural water demand function. Water Resources Research, 42(9).

• Smith, V. H., & Goodwin, B. K. (1996). Crop insurance, moral hazard, and agricultural chemical use. American Journal of Agricultural Economics, 78(2), 428-438.

• Waterfield, G., & Zilberman, D. (2012). Pest management in food systems: an economic perspective. Annual Review of Environment and Resources, 37, 223-245.

• Wu, J. (1999). Crop insurance, acreage decisions, and nonpoint-source pollution. American Journal of Agricultural Economics, 81(2), 305-320.


Classification of land use categories

Major crops Group Switzerland France Comment

I (Grassland) Both permanent and artificial meadows

Both permanent and artificial meadows

Grassland in total covers more than 70% of agricultural area in Switzerland. Artificial meadows cover more than 12% of total acreage. (SBV 2014). For France, the total grassland covers 42% of agricultural area and artificial meadows play a minor role (3%).1

II (Intensive cereals)

Wheats, barley, other cereals

Wheats, barley, other cereals

III (Extensive cereals)

Extensively produced wheat and barley, maize

Maize In Switzerland, farmers receive direct payments to produce cereals without use of all pesticides, except herbicides, which is used by about 50% of all cereal producing farms (Finger and El Benni, 2013).

IV (Root crops)

Potatoes, sugar beet

Sugar beet, sunflower, rapeseed, fodder beet, potatoes

1 Source: http://www.developpement-durable.gouv.fr/


Coefficient estimates 1st stage: French sample

Grassland (1) Wheat Intensive (2)

Wheat Extensive (3)

Foliage crops (4)

Insurance uptake (5)

Lag Grassland

1.06 *** (0.04)

0.13 (0.08)

0.06 (0.05)

0.10 (0.06)

X

Lag Wheat Intensive

0.08 * (0.04)

1.00*** (0.09)

0.10* (0.05)

0.24*** (0.07)

X

Lag Wheat Extensive

0.06 (0.04)

0.18** (0.08)

1.06*** (0.06)

0.09 (0.07)

X

Lag Root crops

0.01 (0.04)

0.33*** (0.09)

0.01 (0.05)

0.94*** (0.07)

X

Lag Ha Total

-0.06 (0.04)

-0.11 (0.08)

-0.06 (0.05)

-0.10* (0.06)

0.00*** (0.00)

Share Animals

5.09*** (0.75)

-1.53* (0.88)

2.34*** (0.63)

-11.14*** (1.16)

-0.57*** (0.11)

Age

-0.05 ** (0.02)

-0.05** (0.03)

-0.01 (0.02)

-0.05 (0.03)

-0.00 (0.00)

Education

-0.33 (0.25)

-0.36 (0.25)

0.16 (0.17)

0.07 (0.26)

-0.05 (0.04)

5-year temp. avg.

-0.13 (0.15)

-0.61*** (0.18)

0.13 (0.14)

0.48* (0.26)

X

5-year prec. Avg.

0.00 (0.00)

-0.00 (0.00)

-0.00 (0.00)

-0.00 (0.00)

X

Lag Insurance

X X X X 2.50*** (0.07)

Weather related hazards

X X X X 0.01** (0.00)

Log Asset- Debt Ratio

X X X X 0.075** (0.03)

Constant

-3.48 (2.55)

12.24*** (12.87)

-3.49 (2.57)

-4.34 (3.86)

-1.12*** (0.30)

Numbers in parentheses are robust standard errors. *,**,*** indicate significance level of 10%, 5% and 1%, respectively.


Coefficient estimates 1st stage: Swiss sample

Grassland (1) Wheat Intensive (2)

Wheat Extensive (3)

Foliage crops (4)

Insurance uptake (5)

Lag Grassland

0.42 (0.39)

0.05 (0.29)

0.65 (0.29)

0.03 (0.17)

X

Lag Wheat Intensive

0.10 (0.24)

0.45 (0.32)

0.01 (0.27)

-0.09 (0.18)

X

Lag Wheat Extensive

0.31 (0.41)

0.30 (0.32)

0.20 (0.31)

0.20 (0.19)

X

Lag Root crops

0.51 (0.52)

0.36 (0.44)

0.34 (0.38)

0.47** (0.24)

X

Lag Ha Total

-0.38 (0.37)

-0.09 (0.26)

0.00 (0.27)

0.04 (0.17)

0.03** (0.01)

Share Animals

5.81 (4.24)

0.45 (3.09)

1.37 (1.97)

-9.00*** (1.79)

-0.18 (0.51)

Age

-0.03 (0.09)

0.01 (0.08)

0.04 (0.04)

-0.07* (0.04)

0.00 (0.01)

Education

-5.56*** (1.77)

0.63 (1.46)

-0.17 (0.89)

2.46*** (0.78)

0.20 (0.25)

5-year temp. avg.

-3.04** (1.28)

0.53 (0.76)

1.36** (0.61)

1.33*** (0.51)

X

5-year prec. Avg.

-0.00 (0.01)

-0.01* (0.01)

-0.00 (0.00)

-0.01*** (0.00)

X

Lag Insurance

X X X X 1.14*** (0.24)

Years Hail Events

X X X X -0.00 (0.01)

Log Asset- Debt Ratio

X X X X -0.04** (0.02)

Constant

27.6 (19.2)

2.57 (12.87)

-10.48 (9.64)

0.98 (7.17)

-0.39 (0.86)

Numbers in parentheses are robust standard errors. *,**,*** indicate significance level of 10%, 5% and 1%, respectively.


Correlations of first stage regressionsSwiss Sample French Sample

Atanrho 1_2 -0.15 (0.10)

-0.00 (0.03)

Atanrho 1_3 -0.31*** (0.12)

-0.02 (0.03)

Atanrho 1_4 -0.05 (0.13)

0.04 (0.04)

Atanrho 1_5 0.00 (0.13)

-0.16 (0.04)

Atanrho 2_3 -0.24** (0.10)

-0.17*** (0.05)

Atanrho 2_4 0.53*** (0.12)

-0.18** (0.08)

Atanrho 2_5 0.15 (0.16)

0.02 (0.04)

Atanrho 3_4 0.27*** (0.09)

-0.09** (0.04)

Atanrho 3_5 0.52*** (0.17)

0.02 (0.04)

Atanrho 4_5 0.38** (0.18)

-0.01 (0.05)

Atanrho represents the transformed (arc-hyperbolic tangent), unbounded correlation coefficient of a pair of equations (see Roodman, 2007).*,**,*** indicate significance level of 10%, 5% and 1%, respectively.


Coefficient estimates 2nd stage: Log pesticideexpenditures as dependent variable

Swiss Sample French Sample Grassland pred.

-0.15 (0.13)

-0.03*** (0.00)

Wheat Intensive pred.

-0.01 (0.10)

0.01*** (0.00)

Wheat Extensive pred.

0.13 (0.13)

0.01*** (0.00)

Root crops pred.

0.10 (0.06)

-0.00 (0.00)

Insurance uptake pred.

0.69 (0.47)

0.04*** (0.04)

Age

0.00 (0.01)

0.00 (0.00)

Education

-0.22 (0.30)

0.06*** (0.01)

Log Fert. Expend.

0.07 (0.06)

0.62*** (0.03)

Temperature year

-0.12 (0.23)

0.01 (0.01)

Precipitation year

-0.00 (0.00)

-0.00*** (0.00)

Constant

7.77*** (2.34)

3.03*** (0.32)

Numbers in parentheses are standard errors, based on 2000 bootstrap repetitions. *,**,*** indicate significance level of 10%, 5% and 1%, respectively.


Coefficient estimates 2nd stage: Pesticidequantities as dependent variable

Numbers in parentheses are standard errors, based on 2000 bootstrap repetitions. *,**,*** indicate significance level of 10%, 5% and 1%, respectively.

Quantity of total pesticide use (kg AI)

Quantity of pesticide use by type of pesticide (kg AI)

Herbicides

Fungicides Insecticides

Grassland pred. 113.37 (255.68)

-15.81 (90.40)

94.81 (134.54)

-113.92 (134.23)

Wheat Intensive pred.

-144.40 (224.64)

-74.49 (85.51)

-3.62 (119.00)

-45.35 (74.24)

Wheat Extensive pred.

-90.08 (298.85)

-20.92 (102.50)

-78.50 (147.65)

-93.41 (131.07)

Foliage crops pred. 391.82** (158.46)

149.63 *** (53.06)

140.42* (80.38)

121.01* (67.53)

Insurance uptake pred.

733.88 (700.59)

113.68 (228.10)

1182.10** (480.46)

273.64 (530.46)

Age

6.30 (18.80)

-2.91 (5.74)

2.82 (9.93)

-4.12 (11.38)

Education

440.64 (700.80)

-71.60 (244.84)

675.00 (412.31)

66.71 (354.34)

Log Fert. Expend. 44.17 (100.00)

20.70 * (10.86)

354.04** (156.57)

308.89** (149.20)

Temperature year 47.41 (472.48)

-20.15 (165.45)

183.76 (251.10)

-158.10 (255.77)

Precipitation year -1.74 (1.10)

-0.59 (0.39)

-0.81 (0.63)

-1.45** (0.65)

Constant

1113.31 (4653.40)

1080.28 (1523.1)

-4844.14 (3184.16)

-331.52 (3031.79)


Correlations of second stage regressions

Atanrho represents the transformed (arc-hyperbolic tangent), unbounded correlation coefficient of a pair of equations (see Roodman, 2007).*,**,*** indicate significance level of 10%, 5% and 1%, respectively.

Atanrho 1_2 0.68*** (0.10)

Atanrho 1_3 0.32*** (0.09)

Atanrho 2_3 0.51*** (0.16)


Econometric framework

1st stage: System estimation of four Tobit (eq. 1,2) and one Probit regression (eq. 3,4)

(1) 𝑆𝑆𝑖𝑖𝑖𝑖∗ = 𝛽𝛽𝑆𝑆𝑋𝑋𝑆𝑆 + 𝜖𝜖𝑖𝑖𝑖𝑖

(2) 𝑆𝑆𝑖𝑖𝑖𝑖 = �𝑆𝑆𝑖𝑖𝑖𝑖∗ if 𝑆𝑆𝑖𝑖𝑖𝑖∗ ≥ 0

0 otherwise

(3) 𝐼𝐼𝑖𝑖∗ = 𝛽𝛽𝐼𝐼𝑋𝑋𝐼𝐼 + 𝑣𝑣

(4) 𝐼𝐼𝑖𝑖 = � 𝐼𝐼𝑖𝑖∗ if 𝐼𝐼𝑖𝑖∗ ≥ 0

0 otherwise

Use simulated maximumlikelihood techniques forestimation, using Stata`s cmppackage (Roodman, 2007)

2nd stage: OLS estimation with instruments and bootstrapped SE’s/ Sytemestimation with instruments for herbicide/fungicide/insecticide quantities

(5) 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑃𝑃𝑖𝑖𝑃𝑃𝑃𝑃𝑘𝑘𝑖𝑖 = 𝛽𝛽𝑃𝑃𝑋𝑋𝑃𝑃 + 𝛽𝛽𝑃𝑃𝐼𝐼𝐼𝐼𝑖𝑖� + 𝛽𝛽𝑃𝑃𝑆𝑆𝑆𝑆𝑖𝑖� + 𝜔𝜔

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Crop Insurance and Pesticide Use · 2016-11-02 · Insurance uptake Influence? 2. Pesticide use. 1. System of land use equations (acreage of four field crop groups) and insurance