Futures Price Information for Farmers

1. Futures Price Information for Farmers CMF-SEWA Seminar on Risk Mitigation in Agriculture August 11th, 2009 Shawn Cole, Harvard Business School Nilesh Fernando, Centre for Micro Finance Stefan Hunt, Harvard University

2. 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

3. 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?

4. 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)

5. 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

6. 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)

7. Cropping Patterns

8. 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

9. 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

10. Timeline • Survey • 74 villages • Survey • Futures training • Add 34 villages • Survey • Futures training • Survey • Survey • Futures training • Pilot with phones • Phone survey • Prices by SMS Survey and training developed Prices delivered

11. Detailed Survey Tool • We measure many levels of effects of futures price information on farmers • Awareness of futures prices • Understanding of futures prices • Belief in usefulness of futures prices • Price expectations: level and spread • Cultivation decisions • Data collected also includes information to pinpoint which farmers are most affected • Attitudes to risk, wealth • Financial literacy, education, cognitive ability

12. 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 expected at harvest time

13. Awareness and Beliefs

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

15. Usage - 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)

16. Price Expectations

17. Futures Prices at 2008 Sowing: Castor

18. Futures Prices at 2008 Sowing: Cotton

19. Information Used to Make Decisions • What information do you use to decide which crops to plant? (Round III)

20. Cultivation Decisions

21. Results Summary • Training and price provision affect awareness, usage and understanding of futures prices • Awareness in the control group surprisingly high • The “treatment” also impacts farmers’ expectations of prices for some crops • The pattern of the treatment effects merits further analysis • 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 • No significant differences when results looked at conditional on education, risk aversion etc…

22. Lessons Learned • 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 • Improving training • Training video • Focus groups helped identify gaps in farmer understanding • 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

23. 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 decisions

24. Thank you!Feedback : nilesh.fernando@ifmr.ac.in