Do Contractual Relations Incentivize Farmers’ Adoption of...

Do Contractual Relations Incentivize Farmers’ Adoption of Multiple Innovations?: Evidence from the Indonesian Dairy Sector

Risti Permani, Ph.D. Professor Wendy Umberger, Ph.D.Global Food Studies, University of Adelaide

The 60th AARES Conference, 2-5 February 2016

Background

University of Adelaide 2

Technology adoption

• Adoption of a single type of technology

• Farmers may adopt in a stepwise pattern

Contract farming

• An important platform to allow smallholders to gain access to both markets and technologies

• Literature on contract farming and technology adoption is extensive

• Little has been done to assess the effects of farmers’ involvement in contract farming on the sequential adoption of multiple innovations.

– Imposing quality-based bonus payment to contracts between farmers and processors to address information asymmetries would improve input use and therefore milk quality (Saenger et al. 2013; Saenger et al. 2014)

• It remains unclear whether and how this vertical coordination has impacted farmers’ stepwise adoption of multiple innovations

Objective


• To investigate whether farmers who engage in contract farming would be more incentivized to (sequentially) trial multiple innovations

Methodology


Data collection (December 2014-January 2015)o >200 dairy households.o 6 cooperatives o Stratified random samplingo 20-page structured questionnaireo 10 moduleso Collaborative effort between Global Food Studies at

University of Adelaide and Bogor Agricultural Universityo Team: Four key researchers, 3 fieldwork coordinators, 16

enumerators, one data entry programmer (and her staff).

Have you used/done since 2010?


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Automatic milking machines

Milk processing (make yogurt)

Cooling milk in water tanks

Conserving forages for the dry seasons (hay, silage)

Milk pasteurisation

Biogas units

High protein concentrates (16% or higher)

Teat dipping after milking

Nutrient feed blocks

Record keeping

Feed legume forages (e.g. Leucaena)

Mastitis test

Grow new improved grasses (high yield)

Rubber/Plastic floor for the barn/cage

Stainless steel milking equipment

Improved milking hygiene to reduce TPC

Improving drinking water availability 24/7

Use of any fertilisers for the grass

Using detergents for milking equipment

Artificial Insemination (AI)

Adoption, 0 to 1

Innovations – adoption and timeline

Type of innovation Are you familiar with or have you heard of […]?

Have you used/done […] since 2010

Are you still using/doing [...]?

What year did you used/do [...] for the first time?

Artificial Insemination (AI) 0.99 0.98 0.97 2000

Using detergents for milking equipment 0.94 0.86 0.80 2001

Rubber/Plastic floor for the barn/cage 0.92 0.68 0.62 2008

Stainless steel milking equipment 0.88 0.68 0.58 2003

Use of any fertilisers for the grass 0.80 0.79 0.62 2002

Biogas units 0.80 0.25 0.09 2010

Mastitis test 0.79 0.58 0.33 2003

Record keeping 0.70 0.41 0.27 2003

Milk processing (make yogurt) 0.66 0.06 0.02 2010

Improving drinking water availability 24/7 0.66 0.71 0.46 2001

Automatic milking machines 0.65 0.03 0.02 2011

Cooling milk in water tanks 0.61 0.12 0.06 2009

Feed legume forages (e.g. Leucaena) 0.54 0.49 0.22 2000

Conserving forages for the dry seasons (hay, silage) 0.54 0.15 0.04 2011

Grow new improved grasses (high yield) 0.51 0.58 0.29 2004

Teat dipping after milking 0.49 0.32 0.10 2006

Improved milking hygiene to reduce TPC 0.44 0.69 0.30 2002

Milk pasteurisation 0.43 0.17 0.05 2006

High protein concentrates (16% or higher) 0.37 0.29 0.03 2005

Nutrient feed blocks 0.28 0.33 0.02 2002


Mastitis and record keeping

• Mastitits – requiring milk not being sold

• Record keeping is negatively associated with the incidence rate of clinical mastitis (Kivaria et al. 2007)

• First-time mastitis cases can be compared against farmer’s record book to see what changes preceded the development of mastitis.

• Mastitis adversely affected the quality of pasteurized fluid milk (Ma et al.2000). – It is recommended that the fluid milk industry

consider implementation of premium quality payment programs for low Somatic Cell Count (SCC) milks.


Empirical model

To look at the impacts of contract farming on farmer 𝑖′s decision to adopt multiple innovations• First stage: 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑖 = 𝛼1 +

𝛼2𝑋𝑖 + 𝛼3𝑍𝑖 + 𝑢𝑖• Second stage: 𝐼𝑛𝑛𝑜𝑣𝑖 = 𝛽1 +

𝛽2𝑋𝑖 + 𝛽3𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑖 + 𝑣𝑖

Where 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡 is contract farming participation (Yes/No); 𝐼𝑛𝑛𝑜𝑣 is the innovation index.

Two innovations:• Mastitis test• Record keeping

Method:1) Logit – contract farming2) Multinomial logit - Innovation

index3) Multinomial logit with an

endogeneous regressor


Initial node

Mastitis test

Record keeping (3)

– 9.62%

No record keeping (1) – 32.69%

Adopt both (5)

Adopt none (0) – 38.46%

Record keeping

Mastitis test (4) – 4.81%

No mastitis test (2) –14.42%

Endogeneity

• 𝐶𝑜𝑟𝑟 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑖 , 𝑣𝑖 ≠ 0

• Similarly, contract farming participation could be affected by adoption decisions where more ‘technology savvy’ farmers might be more likely to meet standards and participate in contract farming.

• As summarised by Bolwig et al. (2009): to deal with endogenous selection, three approaches can be used:

1. Matching estimators which require that selection into program is based only on the observed variables. However, if the assumption of participation on observed variables is doubted, then matching methods will be biased;

2. Instrumental variable (IV);

3. Heckman selection models

Title and Author Method Instrument

The Economics of Smallholder

Organic Contract Farming in Tropical

Africa (Bolwig, et al., 2009)

OLS, Two-stage IV, LIML and

FIML Heckman Selection models

Treatment: Participation in the

organic scheme

Outcome: Gross crop revenue

(log.)

(i) A dummy constructed from the ratio of

non-farm revenue to total revenue, taking

the value of one for those falling in the top

tercile and zero otherwise; (ii) A dummy to

proxy welfare, one if the walls of the

household are made of brick and zero

otherwise.

Impact of Contract Farming on

Income: Linking Small Farmers,

Packers, and Supermarkets in China

(Miyata, et al., 2009)

Heckman selection model

Treatment: Participation in

contract farming

Outcome:

Household income

The distance between the farm of a

household and the farm of the village

leader.

As You Sow, So Shall You Reap: The

Welfare Impacts of Contract Farming

(Bellemare, 2012)

IV model

Treatment: Household

participation in contract farming

Outcome:

Household income

A variable derived by interacting: (i) Dummy 1=’Yes’ to a hypothetical contract

farming question i.e. (randomly generated)

contingent valuation question ( Arrow et

al., 1993 and Mitchell and Carson, 1989);

and

(ii) The initial (hypothetical) value of

investment to enter a contract farming as

part of the same question (i.e. “Would you

be willing to enter a contract farming

agreement that would necessitate an initial

investment of [___]?”; the value ranges

from 25,000 - 150,000 Ariary or US$ 12.5 -

75).

An analysis of contract farming in

East Java, Bali, and Lombok,

Indonesia (Simmons, et al., 2005)

OLS and 2SLS

Treatment:

Contract participation

Outcome: gross margins and

labour use

N/A

Production Contracts and Farm

Business Growth and Survival (Key,

2013)

OLS and IV-2SLS

Treatment: Participation in

production contract

Outcome: Farm size growth

The local (county-level) availability of

production contracts.


Instrument

• The ‘availability of contracts’ (Key, 2013) as proxied by the average number of local buyers as an instrument.

• In our survey, farmers reported that two most important clauses in their contract are price certainty and supply exclusivity.


Descriptive statistics


Variable Contract farming DifferenceNo

(59.05%)Yes

(40.95%)HH gender (Male=1; Female=0) 0.984 0.988 -0.005

HH age (years) 45.984 44.267 1.716

Education (years of schooling) 2231.855 2128.337 103.518

Household size (age>10) 6.976 6.942 0.034

Car ownership (Yes=0) 62.895 57.919 4.977

Truck ownership (Yes=1) 4.411 4.744 -0.333

Motorbike ownership (Number) 0.339 0.116 0.222*

Mobile phone ownership (Yes=1) 0.032 0.023 0.009

Dairy as the main business (Yes=1) 1.355 1.395 -0.041

Herd size (number of cows) 0.944 0.942 0.002

Experience in dairy (years) 0.903 0.837 0.066

Distance to the nearest processor (km) 9.226 6.105 3.121**

Number of buyers 14.780 14.791 -0.011

Quality-based pricing (Yes=1) 6.018 4.647 1.371

Total plate count testing (Yes=1) 3.753 8.032 -4.278**

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

#1 - Multinomial logit: Adoption of innovations(contract farming assumed to exogenous)


Dependent variable: Innovation index (between 0 and 4)

Innovation index=1Mastitis test

Innovation index=2Record keeping

Innovation index=3Mastitis testthen record keeping

Innovation index=4Record keeping then mastitis test

Ordered logit (0=none; 1=1innov; 2=2innov)

HH gender (Male=1; Female=0) -1.126*** 1.490*** 0.507** 0.253 -0.297

HH a -0.022 -0.004 0.017 0.002 0.008

HH age, square 0.000* 0 0 0 0

Education (years of schooling) -0.055 -0.02 0.008 0.062 -0.055

Education, square 0.004 0.002 0 -0.003 0.007Household size (age>10) 0.022 0.041* -0.032* -0.007 0.014Car ownership (Yes=0) 0.103 -0.076 -0.127 0.049 -0.201Truck ownership (Yes=1) 0.073 0.106 0.042 -0.417** 0.161Motorbike ownership (Yes=1) -0.037 -0.025 0.001 0.02 -0.078Mobile phone ownership (Yes=1) -0.214 -0.111 0.932*** -0.108 0.625Dairy as the main business (Yes=1) 0.103 -0.098 0.029 -0.026 0.06

Herd size (number of cows) -0.01 0.002 0.009** -0.006 0.03

Experience in dairy (years) 0.004 0.002 0.002 0.001 0.029

Distance to processor (km) 0.001 -0.008 0.004 0.003 0.019

Participation in contract farming (Yes=1) -0.051 0.012 0.006 0.062* 0.386

Quality-based pricing (Yes=1) 0.005 -0.002 -0.099 -0.032 -0.943**

Total plate count testing (Yes=1) 0.056 -0.007 0.114* 0.049 1.195*

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1; Base category is innovation index=0. All coefficients are

marginal effects.

#2 Probit: Participation in contract farming


Dependent variable:

Participation in contract farming

Probit

(1)

Probit

(2)HH gender (Male=1; Female=0) -0.165 -0.0410HH age (years) -0.103** -0.113**HH age (years), square 0.000998** 0.00109**Education (years of schooling) 0.168* 0.210**Education (years of schooling), square -0.00729 -0.00888*Household size (age>10) 0.174*** 0.164**Car ownership (Yes=0) -0.205 -0.198Truck ownership (Yes=1) 0.468 0.469Motorbike ownership (Yes=1) 0.0372 0.0385Mobile phone ownership (Yes=1) -0.125 -0.0777Dairy as the main business (Yes=1) -0.323 -0.267Herd size (number of cows) -0.0614** -0.0659**Experience in dairy (years) 0.00894 0.00838Distance to processor (km) -0.0226 -0.0427**Quality-based pricing (Yes=1) 0.591***Total Plate Count testing (Yes=1) -0.211Number of buyers 0.0288*** 0.0289***Constant 1.575 1.402Observations 210 210

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1. All coefficients are

marginal effects.

#3 - Multinomial logit: Adoption of innovations(contract farming assumed to endogenous)








HH gender (Male=1; Female=0) 0.424 -14.62 -10.43 -9.681 -0.0623

HH age -0.0909 -0.0497 0.229 0.0303 0.0109

HH age, square 0.00106 0.000183 -0.00231 -0.000224 -0.000127

Education (years of schooling) -0.249 -0.207 0.0415 1.549 -0.0143

Education, square 0.0189* 0.0215 0.000948 -0.0805 0.00294Household size (age>10) 0.140 0.389** -0.359 -0.105 0.0178Car ownership (Yes=0) 0.254 -0.694 -1.899* 1.109* -0.0435Truck ownership (Yes=1) -0.203 0.317 0.0246 -10.92 0.121Motorbike ownership (Yes=1) -0.249 -0.322 -0.130 0.401 -0.0284Mobile phone ownership (Yes=1) 0.737 0.585 14.36 -1.216 0.239Dairy as the main business (Yes=1) 0.404 -0.703 0.479 -0.640 -0.0963

Herd size (number of cows) -0.0581 -0.00380 0.107* -0.166 0.00803

Experience in dairy (years) 0.0315 0.0346 0.0426 0.0348 0.0150


Participation in contract farming (Yes=1) -0.115 0.161 0.170 1.674* 0.281*

Quality-based pricing (Yes=1) -0.428 -0.440 -1.834** -1.235 -0.550***

Total plate count testing (Yes=1) 1.608 1.325 3.008** 2.548 0.689**

Constant 0.452 14.21 -11.26 0.736 1.402

Number of observations 210 210 210 210 210

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1; Base category is innovation index=0. All

coefficients are marginal effects. STATA command gsem is used to run multinomial logit with an endogenous regressor

#3 - Multinomial logit: Adoption of innovations(contract farming assumed to endogenous)








HH gender (Male=1; Female=0) -0.588 14.76 10.42 9.797 -0.00370

HH age -0.0969 -0.0546 0.217 0.0265 0.00875

HH age, square 0.00109 0.000211 -0.00221 -0.000204 -0.000114

Education (years of schooling) -0.208 -0.177 0.0988 1.588 0.000429

Education, square 0.0173 0.0203 -0.00170 -0.0823 0.00233Household size (age>10) 0.142 0.388** -0.379 -0.109 0.0205Car ownership (Yes=0) 0.198 -0.732 -1.932* 1.080* -0.0578Truck ownership (Yes=1) -0.258 0.285 -0.00347 -11.22 0.134Motorbike ownership (Yes=1) -0.241 -0.314 -0.0915 0.408 -0.0232Mobile phone ownership (Yes=1) 0.681 0.558 14.57 -1.233 0.220Dairy as the main business (Yes=1) 0.362 -0.738 0.425 -0.661 -0.116

Herd size (number of cows) -0.0481 0.00339 0.117* -0.161 0.00955

Experience in dairy (years) 0.0353 0.0372 0.0471 0.0367 0.0167*


Participation in contract farming (Yes=1) -0.0941 0.174 0.181 1.679* 0.289*

Quality-based pricing (Yes=1) -0.335 -0.377 -1.763** -1.180 -0.523***

Total plate count testing (Yes=1) 0.772 0.546 2.207** 1.854 0.553*

Constant 1.406 -15.19 -32.29 -18.81

Number of observations 210 210 210 210 210

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1; Base category is innovation index=0. All

coefficients are marginal effects. STATA command gsem is used to run multinomial logit with an endogenous regressor

Concluding remarks


• There is a need to better understand the determinants of adoption of innovations given the declining rates of adoption.

• Endogeneity of contract farming

– Younger; more educated; smaller farms located close to a processor; quality-based pricing – more likely to engage in contract farming

– ‘Availability of contracts’ as an instrument

• Determinants of adoption of innovations vary between different innovation modes taking into account the sequence of adoption.

• Policy recommendations on the design and timing of programs to introduce new innovations to farmers and future ‘packaging of technologies’ by emphasizing ‘stepwise dissemination’.

References


• Barrett, C. B., Bachke, M. E., Bellemare, M. F., Michelson, H. C., Narayanan, S., Walker, T. F., 2012. Smallholder Participation in Contract Farming: Comparative Evidence from Five Countries, World Development. 40, 715-730.

• Bellemare, M. F., 2012. As You Sow, So Shall You Reap: The Welfare Impacts of Contract Farming, World Development. 40, 1418-1434.

• Bolwig, S., Gibbon, P., Jones, S., 2009. The Economics of Smallholder Organic Contract Farming in Tropical Africa, World Development. 37, 1094-1104.

• Ersado, L., Amacher, G., Alwang, J., 2004. Productivity and Land Enhancing Technologies in Northern Ethiopia: Health, Public Investments, and Sequential Adoption, American Journal of Agricultural Economics. 86, 321-331.

• Key, N., 2013. Production Contracts and Farm Business Growth and Survival, Journal of Agricultural and Applied Economics. 45, 277-293.

• Miyata, S., Minot, N., Hu, D., 2009. Impact of Contract Farming on Income: Linking Small Farmers, Packers, and Supermarkets in China, World Development. 37, 1781-1790.

• Saenger, C., Qaim, M., Torero, M., Viceisza, A., 2013. Contract farming and smallholder incentives to produce high quality: experimental evidence from the Vietnamese dairy sector, Agricultural Economics. 44, 297-308.

• Saenger, C., Torero, M., Qaim, M., 2014. Impact of Third-party Contract Enforcement in Agricultural Markets—A Field Experiment in Vietnam, American Journal of Agricultural Economics.

• Simmons, P., Winters, P., Patrick, I., 2005. An analysis of contract farming in East Java, Bali, and Lombok, Indonesia, Agricultural Economics. 33, 513-525.

• Wang, H. H., Wang, Y., Delgado, M. S., 2014. The Transition to Modern Agriculture: Contract Farming in Developing Economies, American Journal of Agricultural Economics. 96, 1257-1271.

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