The impact of rice research expenditure policy in Thailand

Jaijit, Sasarose; Paoprasert, Naraphorn; Pichitlamken, Juta

doi:10.1016/j.jpolmod.2018.06.002

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Empirically, agricultural insurance in developed countries, such as the US, Japan, and several European countries, is growing rapidly and effectively protecting farmers. Government policies significantly affect the stability of agricultural commodity prices in many countries [3], such as Canada, Australia [4], Germany [5], Turkey [6], China [7], Japan, and Thailand [8]. Implementing agricultural insurance has become one of the strategies to adapt to price instability.…”

Section: Introductionmentioning

confidence: 99%

Insurance Premium Formulation for Agricultural Commodity Prices

Wendra¹

2022

InPrime:Ind.Jour.Pure.Applied.Math

View full text Add to dashboard Cite

This research develops the appropriate formula to determine insurance premiums on agricultural commodity prices that provide coverage to policyholders for losses caused by falling prices. The price component is assumed to follow the Brownian Geometric motion in determining the insurance premiums for agricultural commodity prices. So, through the Ito process, a target price can be selected and is used as a reference to determine whether a claim can be made or not at harvest time. The approach of the Black-Scholes model is used to construct an appropriate model to determine insurance premiums of agricultural commodity prices due to a decrease in prices from the expected price. A simulation study is carried out using daily price data for red chili commodities in the Jambi province in 2020 with several assumptions made based on literature studies and farmers’ common habits in Jambi. The simulation results indicate that the average return is -0.001069649, and the standard deviation is 0.07297269. Thus, the expected estimated value of the profit rate is -0.001069649, and the estimated volatility value is 0.07297269. Furthermore, using the target price value, the red chili price insurance premium for a one-planting period with an area of one hectare is Rp. 1,527,088.Keywords: agriculture insurance; Black-Scholes model; European option; Ito process. AbstrakPenelitian ini mengembangkan formula yang sesuai untuk menetapkan harga premi asuransi harga komoditas hasil pertanian yang memberikan pertanggungan kepada nasabah atas kerugian yang disebabkan oleh turunnya harga. Dalam menentukan premi asuransi harga komoditas hasil pertanian, komponen harga diasumsikan bergerak mengikuti Gerak Brownian Geometrik. Sehingga melalui proses Ito dapat ditentukan target harga yang akan dijadikan acuan untuk menentukan klaim atau tidaknya nasabah pada saat panen. Pendekatan model Black-Scholes digunakan untuk mengkonstruksi model yang sesuai untuk menentukan premi asuransi harga komoditas hasil pertanian yang disebabkan karena turunnya harga komoditas dari harga yang diharapkan. Simulasi dilakukan menggunakan data harga harian komoditas cabe merah di Provinsi Jambi tahun 2020 dengan beberapa asumsi yang dibangun berdasarkan kajian literatur dan kebiasaan umum petani di Provinsi Jambi. Hasil simulasi menunjukkan bahwa rata-rata return adalah -0,001069649 dan standar deviasi 0,07297269. Sehingga diperoleh nilai estimasi tingkat keuntungan yang diinginkan sebesar -0,001069649, dan nilai estimasi volatilitas sebesar 0,07297269 . Selanjutnya, menggunakan nilai target harga diperoleh premi asuransi harga cabe merah untuk 1 periode tanam dengan luas lahan 1 hektar sebesar Rp. 1.527.088.Kata Kunci: asuransi pertanian; model Black-Scholes; opsi eropa; proses Ito. 2020MSC: 62P05

show abstract

Section: Introductionmentioning

confidence: 99%

Insurance Premium Formulation for Agricultural Commodity Prices

Wendra¹

2022

InPrime:Ind.Jour.Pure.Applied.Math

View full text Add to dashboard Cite

show abstract

“…l,l − F * ,s ⩽ (1 − P B, * ,l )c B,l − (1 − P B, * ,s )c B,s + (e B,l − e B,s ) P G, * ,l c G,l − P B, * ,l c B,l ⩾ P G, * ,s c G,s − P B, * ,s c B,s (1 − P B,l,l )c B,l − (1 − P B, * ,s )c B,s + (e B,l − e B,s ) ⩾ (1 − P G,l,l )c G,l − (1 − P G, * ,s )c G,s + (e G,l − e G,s ) (1 − P B,l,l )c B,l − (1 − P B, * ,s )c B,s ⩾ (1 − P G,l,l )c G,l − (1 − P G, * ,s )c G,s P G,l,l c G,l − P B,l,l c B,l ⩾ P G, * ,s c G,s − P B, * ,s c B,l − F * ,s ⩽ (1 − P G,l,l )c G,l − (1 − P G, * ,s )c G,s + (e G,l − e G,s ) F l,l − F * ,s ⩾ (1 − P B,l,l )c B,l − (1 − P B, * ,s )c B,s + (e B,l − e B,s ) P G, * ,l c G,l − P B, * ,l c B,l ⩽ P G, * ,s c G,s − P B, * ,s c B,s (1 − P B,l,l )c B,l − (1 − P B, * ,s )c B,s + (e B,l − e B,s ) ⩾ (1 − P G,l,l )c G,l − (1 − P G, * ,s )c G,s + (e G,l − e G,s )This section describes a case study of a typical research funding scenario in Thailand. We used the case study of rice studies in Thailand as explained inJaijit et al (2017) andJaijit et al (2019) Jaijit et al (2017). attempted to evaluate the research impact of three selected rice studies in the areas of breeding, production, and processing, respectively, whereas Jaijit et al (2019) investigated the relationships between research funding and research outcomes (e.g., crop productivity, farmers' income, and plantation cost).…”

mentioning

confidence: 99%

Signaling Game for Research Fund Allocation in Thailand

Duangsong

Paoprasert

Witchakul

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

This study proposed a signaling game for a research grant allocation situation. A funding agency and a researcher whose type was kept secret from the funding agency were the players in the game. The agency decided the grant amount to fund the researcher. The results show that a pooling equilibrium existed when the difference between a large and small fund was sufficiently large, and the expected costs of failing the large-fund project for both types were small, whereas the expected costs of failing the small-fund project for both types were large. When the difference between the effort of putting up a project proposal between a large-fund project and a small-fund project was relatively small, pooling equilibrium occurred. Nevertheless, a separating equilibrium occurred in which the good researcher requested a large fund and the bad researcher requested a small fund when the expected cost of failing the large-fund project for the bad researcher was sufficiently large, whereas the expected cost of failing the large-fund project for the good researcher was sufficiently small. On the basis of the research impact assessment of previous studies, a case study was examined. According to the results, we were still in a pooling equilibrium. However, if some model parameters changed (e.g., when the estimated cost of a penalty to the bad researcher was increased), a separating equilibrium began to show.

show abstract