1 / 24

Futures Price Information for Farmers

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

lel
Download Presentation

Futures Price Information for Farmers

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related