Futures Price Information for Farmers

Shawn Cole, Harvard Business SchoolNilesh Fernando, Centre for Micro Finance

Stefan Hunt, Harvard University

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CMF-SEWA Seminar on Risk Mitigation in AgricultureAugust 11th, 2009

Outline• Project Overview

– Motivations– Study Design– Respondent Characteristics– Intervention Characteristics– Timeline– Survey Tool

• Results– Awareness– Understanding – Usage– Price Expectations– Sowing Decisions

• Academic Findings• Practical Lessons Learned• Next Steps

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Project Overview

• Centre for Micro Finance (CMF), Harvard, Self-Employed Women’s Association (SEWA) and the National Commodities and Derivatives Exchange (NCDEX) partnered in 2007

• A randomized controlled trial (RCT) to look at the impact of providing commodity futures prices to farmers in Gujarat and its impact on price expectations and sowing decisions.

• What is a futures contract?

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Motivation: Price Risk

• Farmers face fluctuations in the prices of agricultural commodities (e.g. recent rises in food prices in South-East Asia)

• Sowing decisions are often based on either current spot prices, weather information or last year’s harvest price with can lead to suboptimal sowing decisions

• Commodity futures prices can be used to make a more accurate estimation of the harvest time prices for agricultural commodities (French 1986, Fama & French 1987, preliminary analysis with Indian data follows suit)

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Study Design• 108 villages in 4 districts of Gujarat

– 54 villages get “treated” with commodity futures prices– 10 farmers who grow either cotton, castor or guar seed are

surveyed and trained

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Respondent Characteristics• Income

– Average income from own cultivation ~ Rs. 4800 per month, median Rs. 2500– Average total income ~ Rs. 6000 per month, median ~ Rs. 3700

• Landholdings– Small Farmers (< = 1 hectare ) ~ 20%– Marginal Farmer (> 1 ha., <= 5 ha.) ~ 55%– Large (Other) Farmers ( > 5 ha.) ~ 25%

• Literacy– 85% report they are able to read and write in Gujarati

• Harvest Characteristics– 85% of cotton cultivators grow < 100 maund (2000 kg) of cotton, mean ~ 60

maund (1200 kg)– 95% of castor cultivators grow < 100 maund (2000 kg) report , mean ~ 45

maund (900 kg)6

Cropping Patterns

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Intervention Characteristics• Price boards displayed in each village, often on the side of walls by

frequented spots: milk cooperatives etc..

• Futures prices for crops are obtained from the NCDEX

• SMS sent out weekly to ‘price poster’ in each village who then updates the board with the latest prices

• ‘Price checker’ contacted by phone to ensure that boards have been updated correctly

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Operations

• Provide repeated training to farmers, with support of NCDEX and SEWA– Explain how farmers can interpret futures prices– Develop video and futures manual to facilitate replication

• Provide futures prices through sowing and harvest– Board in every treatment village

• Survey 1,080 respondents twice per year

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Timeline

Survey and training developed

Prices delivered

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Detailed Survey Tool

• We measure many levels of effects of futures price information on farmers

1. Awareness of futures prices2. Understanding of futures prices3. Belief in usefulness of futures prices4. Price expectations: level and spread5. Cultivation decisions

• Data collected also includes information to pinpoint which farmers are most affected – Attitudes to risk, wealth– Financial literacy, education, cognitive ability

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Measuring Price Expectations

• ‘Bean game’ to measure harvest-time price expectations– Subjective expectations are increasingly being measured in field

research in developing countries (Delavande, Gine, McKenzie 2009)– Farmers asked to place 20 beans in to different price ranges to

indicate where they think the harvest price will fall – Each bean represents 5%, so bean game yields a subjective probability

distribution of the price expectedat harvest time

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Awareness and BeliefsTreatment Control Difference Significance

Have you ever heard of futures prices?

0.89 0.58 0.31 ***

Since we last surveyed you, have you seen any futures prices?

0.81 0.50 0.31 ***

How well do you feel you can trust futures prices (1 to 10)

6.51

How good a predictor of harvest time prices do you believe futures are (1 to 10)

6.26

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Understanding

• Short test administered during Round IV testing knowledge of futures contracts / prices

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Group Treatment Control Difference Significance

Percentage Correct

59.8% 46.7% 13.1% ***

Usage

Survey Baseline Round II Round III Round IV

Treatment 13.6% 35.0% 35.2% 39.8%Control 12.6% 15.7% 15.9% 14.5%Difference 1% 19.3% 19.3% 25.3%Signficance *** *** ***

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- Percentage using futures prices to decide which crops to plant

- Percentage for whom futures prices affected decision whether to cultivate crop (Treatment group, Round IV, Kharif 2008)

Crop Yes (All Plots) Yes (Some Plots)

No Sample Size

Cotton 53.14% 2.5% 44.34% 318Castor 55.24% 1.4% 43.36% 286Guar 27.27% - 72.73% 22

Price ExpectationsTreatment Control Difference Significance

Castor

Expected price (Rs./20 kg) 527.95 513.87 14.09

Std dev, expected price 181.35 181.58 -0.23

Cotton

Expected price (Rs./20 kg) 562.74 525.65 37.09 **

Std dev, expected price 195.43 181.15 14.28 **

Guarseed

Expected price (Rs./100 kg) 1781.94 1859.44 -77.50

Std dev, expected price 604.55 663.07 -58.52

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Futures Prices at 2008 Sowing: Castor

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Futures Prices at 2008 Sowing: Cotton

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Information Used to Make Decisions

• What information do you use to decide which crops to plant? (Round III)

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Type of Information Treatment Control

Futures prices 35.2% 15.9%

Recent spot prices 65.9% 63.0%

Last year’s harvest prices 62.9% 62.2%

Harvest prices from before last year 28.5% 26.3%

Cultivation DecisionsTreatment Control Difference Significance

Castor

Did you plant? 0.56 0.48 0.08

Area (acres) 1.61 1.84 -0.22

Cotton

Did you plant? 0.60 0.61 -0.01

Area (acres) 4.34 4.42 -0.08

Guar

Did you plant? 0.09 0.08 0.01

Area (acres) 0.16 0.13 0.03

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Results Summary1. Training and price provision affect awareness, usage and

understanding of futures prices– Awareness in the control group surprisingly high

2. The “treatment” also impacts farmers’ expectations of prices for some crops– The pattern of the treatment effects merits further analysis

3. We do not yet see significant differences in crop choices or areas cultivated– Some suggestive results in 2007 for “monoculture” farmers . 2008 results to

come

4. No significant differences when results looked at conditional on education, risk aversion etc…

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Lessons Learned1. Getting prices right

– Low-cost distribution of prices significant challenge in rural setting– New verification procedures ensure right prices at right times– Harvest-date contracts to ensure farmers use most helpful prices

2. Improving training– Training video– Focus groups helped identify gaps in farmer understanding

3. Developing understanding of cultivation decisions– Do not see price expectation impact leading to cultivation impact– Potential reasons:

• Other factors influence cultivation decisions• Takes time for new information to change farmers’ behaviors

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Future Plans• Allow farmers to manage risk directly

– Minimum lot size regulations mean farmers cannot sign up for futures contracts– Plan underway to offer put options

• Finish full results for Rounds III and IV surveys– Much more detailed results to come

• Develop training and survey further– Documentary video on farmers’ experiences– Develop interactive games to teach futures prices– More questions on farmers cultivation decisions

• Deliver price information via SMS to individual farmers– Information could include weather forecasts or local spot prices

• Examine how information sharing between farmers influences treatment effects– Hypothesize that knowing futures prices is necessary but not sufficient to change

cultivation decisions23

Thank you!

Feedback : nilesh.fernando@ifmr.ac.in

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