Survey Probability and Factors affecting Farmers Participation in Future and Option Markets Case Study: Cotton product in Gonbad kavos city

مجله اقتصاد و توسعه کشاورزی

Article 3, Volume 29, Issue 3, October 2015, Pages 242-257   

Document Type: Research Article

DOI: 10.22067/jead2.v0i0.43215

Authors

F. Sakhi^{* 1}; H. Mohammadi²; M. Sabuhi²

¹Ferdowsi University of Mashhad

²University Ferdowsi of Mashhad

Abstract

Introduction: Farmers are facing with a variety of natural and unnatural risks in agricultural activities, and thus their income is unstable. A wide range of risks such as risks of production, price risk, financial and human risks, influence the income of agricultural products. One of the major risks that farmers faced is the risk of price volatility of agricultural products. Cotton is one of the agricultural products with high real price volatility. Numerous tools for marketing and risk management for agricultural products in the face of price risks are available. Futures and options contracts may be the most important available tools (to reduce price volatility) in agricultural products. The purpose of the current study was to look at the possibility of farmers participations in the future and option markets that presented as a means to reduce the cotton prices volatility. The dependent variable for this purpose had four categories and these included: participate in both the market, participation in the future market, participation in the option market and participation in both future and option markets.
Materials and Methods: data gathered with interview and completing 200 questionnaires of cotton growers using simple random sampling. Multinomial Logit Regression Model was used for data analysis.
Results and Discussion: To measure content validity of the preliminary study the validity of confirmatory factor analysis were used. For calculating reliability, the pre-test done with 30 questionnaires and reliability, coefficient Cronbach alpha was 0.79. The independence of dependent variables categories was confirmed by Hausman test results. The Likelihood ratio and Wald showed these categories are not combinable. Results indicated into period 2014 -2015 and the sample under study, 35% of cotton growers unwilling to participate in future and option markets. Farmers willingness to participate in future and option market was 19% and %21.5 respectively. Multinomial Logit model estimation results for the probability of participation in the future and option markets showed that variables of the level of education, farm ownership, cotton acreage, and non-farm income, work experience in agriculture, the index of willing to use new technologies, the index of risk perception cotton market and risk aversion index are statistically significant. The variables of farm ownership, non-farm income and work experience in agriculture, showed negative effects and the other variables showed positive effects on the probability of participation in these markets. The results are in line with previous studies.
Conclusion: The purpose of the current study was to look at the possibility of farmers participations in the future and option markets that presented as a means to reduce the cotton prices volatility. The dependent variable for this purpose, have four categories: participation in both market, and future market, participation in option market and participation in both future and option markets. Multinomial Legit Regression Model was used for data analysis. Results indicated that during the period of 2014 -2015 and the sample under study 35% of cotton growers unwilling to participate in the future and option markets. Farmers willingness to participate in the future and option market was 19% and %21.5, respectively. Multinomial Legit model estimation results for the probability of participation in the future and option markets showed that the variables of the level of education, farm ownership, cotton acreage, and non-farm income, work experience in agriculture, the index of willing to use new technologies, the index of risk perception cotton market and risk aversion index were statistically significant. The variables of farm ownership, non-farm income and work experience in agriculture, showed negative effects and the other variables positive effects on the probability of participation in these markets. The results are in line with previous studies. Given the positive relationship between level of education and participation of farmers in the future and option markets can be suggested that the training seminars would be provided. The content of the seminars could be about how these markets as a means of reducing the risk of price and performance, and informing farmers of the role of research, education and extension services. Given the positive relationship between risk aversion and risk perceptions which tend to use the new technology on the market, cotton farmers are likely to participate in these markets. Therefore it is proposed to develop a more farmers markets.

Keywords

Cotton, Future market; Multinomial logit; Option Market; Participation

References

1- Abdollahi Ezatabadi M., and Najafi B. 2005. Estimatesofthe supplyof agricultural productsinfutures and optionsmarketsandthe factors which affectit, pistachiocase study. Journal of Scienceand Technology of AgricultureandNatural Resources, 10(2):1-15. (In Persian)

2- Abdollahi Ezatabadi M., and Najafi B. 2006. The possibility ofthe participation offarmersandtradersin thefutures and optionsmarketsfor agricultural products inIran, pistachiocase study. Journal ofAgricultural EconomicsandDevelopment, 15(57):105-130. (In Persian)

3- Adanacioglu H. 2011. The futures market in agricultural products and an evaluation of the attitude of farmers: A case study of cotton producers in Aydin Province in Turkey. Journal of Agricultural Economics,1: 58-64.

4- Anderson T. W.1958. Introduction to multivariate statistical analysis, John Wiley and Sons, New York.

5- Anderson J.A.1984. Regression and ordered categorical variables. Journal of the Royal Statistical Society, Series B,46:1-30.

6- Anderson J.R. 2003. Impacts of climate variability in Australian agriculture. Review of Marketing and Agricultural Economics,49 (31):21-37.

7- DehghanianM., Ghorbani, M., and Dinqzly, F.(Translation). 2010. Economicsof agricultural markets. RonaldArthurShrympr. Publication ofFerdowsi University of Mashhad.

8- Ferdowsi R., Qahreman Zade, M., Pishbahar, A., and Rahely, H .2013. Identify factors to improve the collection of the Agricultural Bank city of Maragheh. Journal of Research and Economic Policy, 21 (67): 49-68. (In Persian)

9- Franken J.R.V., Pennings J.M.E., and Garcia Philip.2012. Crop production contracts and marketing Sstrategies:What Drives Their Use?. Journal of Agribusiness, 28(3): 324–340.

10- GhorbaniM., and Kulshreshtha S.2013. An environmental and economic perspective on integrated weed management in Iran. Weed Science Society of America, 27(2):352-361.

11- Goodwin B.K., and Schroeder T.C.1994. Human capital, producer education programs, and adoption of forward pricing methods. American Journal of Agricultural Economics, 76: 936-947.

12- Greene W.H., and Hensher D.A. 2003. A latent class model for discrete choice 10-analysis: contrasts with mixed logit.Transportation Research Part B, 37:681-698.

13- Hausman J., and McFadden D.1984. Specification tests for the multinomial logit model. Econometrica, 52:1219–1240.

14- Isengildina O., and Hudson M.D. 2001. Factors affecting hedging decisions using evidence from the cotton industry. Paper presented at the NCR-134 Conference on applied commodity price analysis, forecasting, and market risk management, April 23-24. 2001. St. Louis, Missouri.

15- Johnston R. J.1986. Multivariate statistical analysis in Ggeography: a primer on the general linear model, Longman, New York.

16- Jordan H., and Grove B.2007. Factors affecting maize producers adoption of forward pricing in price risk management: The Case of Vaalharts, Agrekon, 46(4):548-565.

17- Jordan H., and Grove B.2008. Factors affecting the use of forward pricing methods in price risk management with special reference to the influence of risk aversion. Agrekon, 46(1):102-115.

18- Kalantary KH.2003. Processing and analysis of data on socio-economic researches. Publish Sharif, Tehran.

19- Long J.S.1997. Regression models for categorical and limited dependent variables. SAGE Publications, Inc. London EC2A 4PU, United Kingdom.

20- Makus L.D., Lin B.H., Carlson J. and Krebill-Prather R. 1990. Factors influencing farm level use of futures and options in commodity marketing. Journal of Agribusiness, 6:621-631.

21- McFadden D.1973. Conditional logit analysis of qualitative choice behavior, in Zarembka, Frontiers in Econometrics. Academic Press, New York.

22- McFadden D.1984. Conditional logit analysis of qualitative choice behavior, in Zarembka, Frontiers in Econometrics. Academic Press, New York.

23- Mishra A.K., and El-Osta H.S.2002. Managing risk in agriculture through hedging and crop insurance: What Does a National Survey Reveal? Agricultural Finance Review,136-148.

24- Mofokeng M., and Vink N.2013. Factors affecting the hedging decision of maize farmers in Gauteng Province. The 4th International Conference of the African Association of Agricultural Economists, September 22-25, 2013, Hammamet, Tunisia.

25- Mojaverian S.M., Rasuli F., and Hossieni Yekani S.A. 2013. Factorsaffecting the selection ofsaleschannelsinMazandarancitrus growers. Journal ofAgricultural Economics and Development, 27(2),133-123. (In Farsi)

26- Musser W.N., Patrick G.F., and Eckman D.T. 1996. Risk and grain marketing behavior of large-scale farmers. Review of Agricultural Economics, 18: 65–77.

27- Nicoee A.R., and Torkemani J. 2002. Look atissuesof moral hazardandadverseselectionwheatInsuranceCase Study inFars Province. IranianJournal of Agricultural Sciences, 33(1): 157-169. (In Farsi)

28- Pai C.W., and Saleh W.2008. Modeling motorcyclist injury severity by various crash types at T-junctions in the uk.Satety science, 46:1234-1247.

29- Pedhazur E.J. 1982. Multiple regression in behavioral research: explanation and prediction. New York.

30- Pennings J.M.E., Isengildina-Massa O., Irwin S.H., Garcia P., and Good D.L. 2008. Producers’ complex risk management choices. Journal of Agribusiness, 24: 31–54.

31- Pennings J.M.E., and Garcia P. 2001. Measuring producers’ risk preferences:Aglobal risk attitude construct. American Journal of Agricultural Economics, 83:993–1009.

32- Pennings J.M.E., and Leuthold R.M. 2000. The role of farmers’ behavioral attitudes and heterogeneity in futures contracts usage. American Journal of Agricultural Economics, 82: 908–919.

33- Penning J.M.E., and Smidts A. 2000. Assessing the construct validity of risk attitude. Manage,46: 13-48.

34- Qadiri Moqadam A., and Nemati A. 2011. Factorsaffectingtheparticipation offarmers in the futures markettomatoes.Journal ofAgricultural Economics and Development(Agricultural Science and Technology), 25 (3):25-53. (In Farsi)

35- Ray P.K.1967. Agricultural insurance, principle and organization and application to developing countries, FAO, Rome, Peramon Prees, P-P. 12.3.

36- Reynaud A., and Ricome A. 2010. An empirical analysis of the determinants of marketing contract choices in France. 9-10 decembre. RENNES, France.

37- SabbaghKermaniD.,and AziziF. 2005.AgriculturalcommoditiesexchangeinIran. Two- Quarterlyof Economic Essays, (3),9-34. (In Farsi)

38- Sartwelle J., O’Brien D., Tieney W., and Eggers T. 2000. The effect of personal and farm characteristics upon grain marketing practices. Journal of Agricultural and applied Economics, 32(1):95-111.

39- Shapiro B.I., and Brorsen B.W.1988. Factors affecting farmers’ hedging decisions. North Central Journal of Agricultural Economics, 10:145-153.

40- Talebi H., and Zangi Abadi Q. 2001. Analysis indicators and determining factors impact in human development methodologies major cities. Geographical Research Quarterly, (60). (In Farsi)

41- Turvey C.G., and Baker T.G.1990. A farm-level financial analysis of farmers use of futures and options under alternative farm programs. American Journal of Agricultural Economics, 72:946-957.

42- Velandia M., Rejesus R.M., Knight T.O., and Sherrick B.J. 2009. Factors affecting farmers’ utilization of agricultural risk management tools: The Case of crop insurance, Forward Contracting, and Spreading Sales. Journal of Agricultural and Applied Economics, 41:107–123.

43- White B., and Dawson P.J. 2005. Measuring price risk on UK arable farms. Journal of Agricultural Economics, 56: 239–252.