Uchook Duangbootsee

1. The Ranking of Jasmine Rice Farmers’ Preference towards Government Program Alternatives: An Application of a Stochastic Dominance with respect to a Function Uchook Duangbootsee Department of Food, Resources, and Agricultural EconomicsMichigan State University (USA) Funded by Thailand Research Fund (TRF)

2. Introduction • Paddy Pledging Program (PPP) &Deficiency Payment Program (DPP) • PPP: Farmers can sell any amount of rice paddy to the government at a target price which is administratively predetermined by the government. Farmers are given four months from the sale date to redeem the pledged paddy. Otherwise, the government will take possession of the paddy. • DPP: The government sets a target price for which deficiency payments are made to farmers when the target price is below market price. The deficiency payment is a product of the difference between target and market price and average provincial yield multiplied by number of rais of registered land

3. Introduction • Change in Net Farm Income (NFI) under PPP: = NFIPPP– NFIMKT= (PPPP.QTotal) – (PMKT.QTotal)= (PPPP- PMKT).Qtotal(1) • Change in Net Farm Income (NFI) under DPP: = Deficiency Payments (if PDPP > PMKT)= (PDPP- PREF).QREF (2) • Remark: (1) = (2) if i.) PPPP= PDPP and ii.) PREF=PMKT and iii.) QTotal = QREF

4. Introduction • What are the difficulties in comparing farmer’s preference towards PPP and DPP? • Non-simultaneous implementation of PPP and DPP • Choice of utility function • Difference in farmer’s risk attitudes • Lack of statistical software to evaluate farmer’s preferences • Lack of data • Etc.

5. Which asset would you prefer?

6. Which asset would you prefer?

7. Literature Reviews • Von Neumann & Morgenstern (1945) proposed expected utility (EU) for ranking of risky alternatives • Arrow (1965) & Pratt (1964) measured degree of risk aversion in term of utility • Meyer (1977) applied above theories and proposed a stochastic dominance with respect to a function(SDRF) as a method to rank risky alternatives

8. Literature Reviews • Meyer proposed ranking risky alternatives for a class of decision makers, i.e., for decision makers who’s utility function is defined by a lower risk aversion coefficient (LRAC or r1 ) and an upper risk aversion coefficient (URAC or r2 ) which is denoted as U(r1(z), r2(z)) • The condition for F preferred to G under SDRF is :

9. Literature Review • which is often expressed as: • Z = Income or wealth • F(Z) andG(Z) areCDFs of two different risky alternatives • U(Z) = Utility Function • r1 = LRAC = lower risk aversion coefficient • r2 = URAC = upper risk aversion coefficient

10. Literature Review • The SDRF criteria indicates that utility is calculated for each z value and the sum of the weighted utilities is used to rank F and G. • The preferred risky alternative is calculated for the LRAC and for the URAC. • If the same risky alternative is preferred for both RACs, it is considered to be in the “efficient set.” • The SDRF criteria is useful for ranking risky alternatives who’s CDFs cross. • The advantage of SDRF is that it does not assume a specific utility function.

11. Objectives Applying SDRF procedure to rank preferences of jasmine rice farmer based on income distributions under the following scenarios: 1. No participation 2. PPP participation 3. DPP participation

12. Method • General steps for applying SDRF • Simulate multivariate empirical distribution (MVE) of price (P1,P2,P3,…,P500) and yield (Y1,Y2,Y3,…,Y500) of jasmine rice based on its historical data (Richardson et al, 2001) • Compute net farm income (NFI) from the simulated data under each scenario for 500 iterations • Compute expected utility under each scenario using the information on the CDFs of NFI and a specified range of risk aversion • Degrees of risk aversion as defined by Anderson and Dillon (1992): 1-normal, 2-rather risk averse, 3-very risk averse, 4-extremely risk averse • Rank the preferences using SDRF option built in a software called “Simulation & Econometric to Analyze Risks (SEMITAR)”

13. Net Farm Income (NFI) NFI under three scenarios: • Case 1: No Participation NFIMKT = (QTotalx PMKT) – Cost • Case 2:PPP Participation NFIPPP =(QTotalx Max[PPPP- PMKT ]) – Cost • Case 3:DPP Participation NFIDPP = NFIMKT + {Max[(PDPP-PREF),0]}x(QREF) – Cost

14. Data • Market price and yield of jasmine rice in Buriram from 1999-2010 (2542-2554) obtained from Office of Agricultural Economics (OAE), Bangkok, Thailand • Fixed and variable costs (author’s calculation) • Target price and program benefits of PPP and DPP from 2005-2011 collected from Department of Internal Trade (DIT), Bangkok, Thailand

15. Data Price and Yield of Jasmine Rice in Buriram from 2542-2554

16. NFI Inputs and Assumptions

17. Result Summary of Prices and Yield from 500 iterations

18. Result Simulated NFIs when PPPP = PDPP = 20,000baht/ton

19. Market PPP DPP

21. Result Ranking of Farmer’s Preferences when PPPP = PDPP = 20,000 baht/ton

22. Result Would the ranking change if percentages of price differential (PREF – PMKT) was doubled?

24. Result How much can we decrease the DPP target price before the ranking change? Result: At PDPP =19,500 and PPPP =20,000, PPP is preferred over DPP.

25. Discussion and Suggestion • PPP and DPP raised farmers’ rice farm income and reduced fluctuation of the income by at least twice as much compared to no participation in any of government price support program • Farmers at all degrees of risk aversion prefer DPP to PPP • The fact that the government had historically overestimated the DPP reference price leads to a possibility of farmers switching from DPP to PPP as such inaccuracy increases • DPP is considered more economical since the government could lowerthe DPP target price close to 19,500 baht/ton (while hold the PPP target price at 20,000 baht/ton), yet farmers still prefer DPP to PPP)

26. Thank You