ดา - agri.eco.ku.ac.th

ดา - agri.eco.ku.ac.th

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 Economics Michigan State University (USA) Funded by Thailand Research Fund (TRF) 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 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 Introduction What are the difficulties in comparing farmers preference towards PPP and DPP? Non-simultaneous implementation of PPP and DPP Choice of utility function Difference in farmers risk attitudes Lack of statistical software to evaluate farmers preferences Lack of data Etc. Which asset would you prefer? Which asset would you prefer? 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 Literature Reviews Meyer proposed ranking risky alternatives for a class of decision makers, i.e., for decision makers whos 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 : Literature Review which is often expressed as: Z = Income or wealth F(Z) and G(Z) are CDFs of two different risky alternatives

U(Z) = Utility Function r1 = LRAC = lower risk aversion coefficient r2 = URAC = upper risk aversion coefficient 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 whos CDFs cross. The advantage of SDRF is that it does not assume a specific utility function. 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 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) Net Farm Income (NFI) NFI under three scenarios: Case 1: No Participation NFIMKT = (QTotal x PMKT) Cost Case 2: PPP Participation NFIPPP = (QTotal x Max[PPPP- PMKT ]) Cost Case 3: DPP Participation NFIDPP = NFIMKT + {Max[(PDPP- PREF),0]}x(QREF) Cost

Data 1. Market price and yield of jasmine rice in Buriram from 1999-2010 (2542-2554) obtained from Office of Agricultural Economics (OAE), Bangkok, Thailand 2. Fixed and variable costs (authors calculation) 3. Target price and program benefits of PPP and DPP from 20052011 collected from Department of Internal Trade (DIT), Bangkok, Thailand Data Price and Yield of Jasmine Rice in BuriramYearfrom 2542-2554

Yield (kg/rai) Price (baht/ton) 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 291 301 314 313

315 302 328 324 322 321 323 364 365 7403.50 6852.00 4765.00 5786.00 7675.00 7830.00 7831.00 8047.50 9385.50 13282.50

14691.50 14147.50 16060.00 NFI Inputs and Assumptions Planted Areas 25 Utilization of Jasmine Rice Seed 15 Consumption 1000 Other 300 Production Cost 3000 Sales and Marketing PricePPP

20,000 PriceDPP 20,000 YieldPPP 365 rai Kg./rai Kg/HH Kg/HH Baht/rai Baht/ton Baht/ton Kg/rai Result Summary of Prices and Yield from 500 iterations Market Reference Variable

Yield Price Price Mean 354.67 14,846.36 15,522.01 StDev 10.58 2,588.18 2,696.89 CV 2.98 17.43 17.37 Min 336.45 11,289.29 11,262.28 Max 373.88 21,348.28 22,889.35 Result Simulated NFIs when PPPP = PDPP = 20,000 baht/ton Market

Mean Standard Deviation CV Min Max PPP DPP 69,105.15 73,443.71 31,659.41 (2,764.21 (2,937.75 (1,266.38) ) ) 18,314.97 5,359.07 7,289.18 57.85 7.75 9.92 2,421.6559,722.9952,721.21

85,638.2485,638.2493,123.91 Probabilities of observing NFI above 75,000 and below 37,500 100% 0.02 0.16 90% 80% 0.34 0.44 70% 60% 50% 0.84

40% 30% 0.64 0.56 20% 10% 0% 0.00 Market 0.00 PPP DPP

probability of NFI > 75,000 baht probability of NFI between 37,500 and 75,000 baht CDFs of NFI under 3 Scenarios 1 0.9 0.8 0.7 Prob 0.6 0.5 0.4 0.3 0.2 0.1 0

0 Marke PPP DP 20000 40000 60000 80000 t P 100000

Result Ranking of Farmers Preferences when PPPP = PDPP = 20,000 baht/ton Case DPP PPP Market Preference Ranking at Risk Aversion of : RRAC=1 RRAC=4 (normal) (extreme) 1 1 2 2 3

3 Result Would the ranking change if percentages of price differential (PREF PMKT) was doubled? Preference Ranking at Risk Aversion of : RRAC=1 RRAC=4 Case (normal) (extreme) DPP1 1 2 PPP 2 1 DPP2

(Less accuracy of PREF) 3 3 Market 4 4 Prob CDFs of NIF when % of price differential (P REF PMKT) doubled. 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

0.1 0 0 20000 40000 60000 Market PPP 1 80000 100000 120000

DPP1 DPP2 2 Result How much can we decrease the DPP target price before the ranking change? Result: At PDPP =19,500 and PPPP =20,000, PPP Preference Ranking at is preferred over DPP. Risk Aversion of : RRAC=1 RRAC=4 Case (normal) (extreme) DPP @ 20,000 1 (most 1 (most

baht/ton preferred) preferred) DPP @ 19,800 baht/ton 2 2 DPP @ 19,600 baht/ton 3 3 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 lower the 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) Thank You

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