Impact of Fuel Prices on Paddy Farmer’s Expenditure ...

International Journal of Business Management and Economic Studies

Volume No 2, Issue 1, 2015

© 2014. IJBMES. All rights reserved 1

www.ijbmes.com

Impact of Fuel Prices on Paddy Farmer’s Expenditure Allocation in

Malaysia

Siti Aisyah Baharudina, Fatimah Mohamed Arshad

b, Muhammad Tasrif

c

andAbdulla Ibragimovc

a PhD student, Faculty of Economics and Management, Universiti Putra Malaysia, Selangor,

Malaysia2

E-mail address : [email protected] bLecturer, Faculty of Economics and Management, Universiti Putra Malaysia, Selangor,

Malaysia

E-mail address : [email protected] cLecturer, Institut Teknologi Bandung, Indonesia

E-mail address : [email protected]

dPhD student, Institute of Agricultural and Food Policy Studies, Universiti Putra Malaysia,

Selangor, Malaysia

E-mail address : [email protected]

Received: September 2015; | September 2015

ABSTRACT

The international price increase in fuel and food has direct impact on production cost and

domestic prices. Fuel and fertilizers are agricultural inputs in paddy production. Fuel is needed

for operating paddy machineries such as tractors and harvesters, while fertilizers are important for

keeping the soil for paddy crop fertile. Increment in fuel prices will affect paddy productivity and

farmer’s expenditure. Farmer’s expenditure includes fuel (petrol and diesel), input (fertilizer,

pesticides and seed) and labour cost. (i) What is the current behaviour of paddy expenditure in

1990 to 2015? (ii) What is the future trend of farmer’s expenditure under the fuel price change in

2016 to 2030? (iii) How the farmer’s will decide to allocate their expenditure on inputs under the

different world fuel price rate? The situation requires one to seek a methodology that could

explain the complexity of the system and to establish an insight on the interrelation of the key

variables. The system dynamics model provides a framework for understanding the impact of fuel

price change on farmer’s expenditure. Simulation result show domestic fuel price will follow

world’s crude oil price trend with the increasing by 40% in 2030 with the average of 3.8% per

year. Meanwhile, expenditure of paddy increase by 3.8. However, the share of fuel and fertilizer

is about 45% of the total expenditure as fuel price could indirectly affect the price of fertilizer.

Indirect effect is greater than direct effect on farmer’s expenditure.

Keywords: ICARBSS, Farmer’s expenditure, Paddy production, Fuel price, System dynamics,

Production cost.

mailto:[email protected]




www.ijbmes.com

1. INTRODUCTION

The farmer’s expenditures directly influenced by the price variation and usage level. A

change in expenditure pattern is based on income behaviour, while the expenditures

influenced by the subsidies by the government. Fuel and fertilizers are agricultural inputs

in paddy production. Government decisions are treated as exogenous policy and

government is not permitted to engage in any deficit spending except as an explicit

policy. Desired expenditure is a function of expenditure for production which consists of

input, machinery and other expenditure. Other expenditure consist labour cost and tax.

While, non-production expenditure is a function of economic and living cost such as

education, food, festivals and others.Expenditure for non paddy production is 30% of

total income(Rashid & Dainuri, 2013). The general objective of the study is to develop a

system dynamics model of the impact of fuel prices on farmer’s expenditure allocation in

Malaysia.

Specific objectives are:

(i) Create a system dynamics model that is able to capture the relationship between

key elements in the system; and

(ii) Simulate a number of scenarios under the world crude oil price change.

2. PRODUCTION COST

Analysis on production cost in the 1970sshowed an increase during the years due to

higher input costs of fertilizer, urea and compound. Fertilizer subsidy scheme was

introduced in 1979 where farmers have to pay only about17to21% ofthe input material

and 79 -83% ofthe labor cost. After the implementation of the fertilizer subsidy scheme

and direct seeding practices, the use of inputs such as fertilizer, urea, compound and

amorphous showed a decrease from 20 to 4%. The use of pesticides and seeds also

increased by 55% and 15% respectively since 1970, together with increment in the seeds

cost (Robiah, 2003). Labor utilizationis high as most plantingmethods are done manually,

especially in landpreparationandplanting. If planters are tenants, they incur an additional

33 to 37% for land rental cost. Nevertheless, the percentage of labor and land rent costs

continued to rise. In the 1990s, farmers had to adopt to direct seeding because it was

deemed as time and cost saving. Furthermore, local labor was becoming scarce while the

number of laborers from Thailand was decreasing. (Robiah, 2003).

Table 1. Production cost (RM/ha) between granary area in Malaysia (2006)

Granary Area Items (RM/ha) Production Cost (RM/ha)

Input cost Operation

cost

Land

tax

Land

rental

Owner Tenant

MADA 820.59 910.84 37.98 1,106.35 1,769.41 2,837.78

KADA 529.40 1,269.40 10.00 295.45 1,808.80 2,094.25

IADA KSM 838.00 876.00 15.00 378.00 1,729.00 2,092.00

IADA PBLS 1,429.88 1,061.91 9.00 1,250.00 2,500.79 3,741.79

IADA P.P 1,635.00 1,400.00 20.00 700.00 3,055.00 3,735.00

IADA S.P 750.00 840.00 - 800.00 1,590.00 2,390.00




www.ijbmes.com

IADA

KETARA

983.00 1,140.00 - 350.00 2,123.00 2,473.00

IADA K.S 1,079.50 1,114.38 5.00 150.00 2,198.88 2,343.88

Source : MoA (2012a)

Table 1 shows a comparison between the production costs of paddy per ha by granary

area for the season 2/2006. There are two production costs categories; namely paddy

fields ownership and paddy fields tenancy. Production costs in IADA P.P and IADA BLS

were at RM 3,055 and RM 2,500 per ha for land owners and RM 3,735 and RM3741 per

ha for land tenants. Production costs in the MADA areas are among the lowest compared

to other granary areas with RM 1,769 per ha for owners and RM 2,837 per ha for tenants.

The average production cost in the owner category was RM 1,681 per ha while RM 3,075

per ha was recorded for the rental category.

Land rental represent the highest production cost components,ie46.5%, followed by

machinery costs(32.7%), input cost (13.7%) and labor cost (7%). Meanwhile, for owner

category, machinery cost represent highest cost, ie 60.4% compared to input cost (25.3%)

and labot cost (13%). In 2012, the average production cost in the owner category was RM

3,442 per ha while RM 6,322 per ha was recorded for the rental category in MADA

compared to production cost in non-granary area at RM 4,259 per ha for owner category

(MADA, 2012a).

The ranges of production cost to produce one kilogram of paddy is between RM0.28 and

RM0.40. The share of expenditure component of paddy are; input cost (42.2%); operation

and labour cost (57.8%) of total cost. The operation cost including land preparation

(11.8%), seed preparation (1.9%), manuring (6.5%), pest and disease control (7.8%) and

harvesting and transportation (29.5%) (Nordiana & Low, 2010). Machinery cost are

devided by two cost, namely fuel cost for machinery and transportation and non-fuel cost

for machine rental and machine driver which accounted 60% of total expenditure.

Increase in world crude oil prices affected petroleum based products such as diesel and

petrol in the country. Prices of oil and petroleum products were determined by the world

market and were beyond Malaysia control. OPEC played a crucial role in determining the

world oil production level and affected the market price. Although Malaysia produced

and exported oil, it is neither a member of OPEC nor a large oil producing country

because Malaysia is a marginal net exporter (total imports almost equal to the total

exports). In the case of high oil prices in the world market, the high price could affect the

price of petroleum products such as diesel and petrol in the country (ECM, 2011).




www.ijbmes.com

Fig. 1: Price of crude oil (USD/barrel), fertilizer and rice (USD/MT) in world market

(1990-2012)

Source: www.InflationData.com, (2013) and FAO, (2012)

Fuel cost is influenced by fuel price and hectare age usage level. Fuel cost consists 15%

of machinery cost (own survey, 2012). A change in fuel price is based on world crude oil

price behaviour and fuel subsidy. Fraction of fuel subsidy is equal to 0 to 1. If zero means

no subsidy and 1 is full subsidy. A fraction of petrol is 28% while diesel at 32% with the

average of fuel subsidy is 30% of total fuel use (APERC, 2012).Increase in world fuel

and food price, has direct impact on production cost and domestic prices (Fig. 1).

Table 2. Fuel price (RM/L) in ASEAN (2005-2013)

Country Petrol RON95 (RM/L) Diesel (RM/L)

2005 2012 2013 2005 2012 2013

Thailand 2.29 4.84 4.74 1.82 3 3.06

Singapore 3.76 5.26 5.56 2.36 3.97 4.32

Indonesia 1.7 2.91 3.05 0.86 2.77 3.43

Philippines 2.07 - 5.44 1.78 - 4.32

Malaysia 1.6 1.9 2.1 1.28 1.8 2.0

Source: EPU (2005) and EPU (2013)

Based on the comparison in Table 2, the oil price in Malaysia was cheaper than most of

the ASEAN countries. In fact, it was among the lowest in the world. However, the diesel

price differences between the countries were very tangible. As a comparison, RON 95

petrol and diesel in Malaysia were priced at RM2.10 and RM2.00 respectively, whereas

the petrol and diesel in Thailand were RM4.74 and RM3.06, Singapore (RM5.56 and

RM4.32), Indonesia (RM3.05 and RM3.43) and the Philippines (RM5.44 and RM4.32)

for every liter petrol and diesel. Malaysia was ranked as the 26th world's oil producers

(EIA 2013). Meanwhile, the fuel price in Malaysia was the eighth lowest in the world

with RM23.7 billion in fuel subsidy in 2012. The government subsidized RM0.83 for




www.ijbmes.com

every liter of RON95 petrol and RM1.00 for every liter of diesel, which would cost the

government RM24.8 billion in 2013 (“Malaysian fuel price 8th lowest in world”, 2013).

Table 3. Retail price (RM/L) of petrol RON95 in Malaysia (2005-2013)

Cost (RM/L) 2005 2009 2013

(+) Production cost 1.64 1.24 2.83

(+) Alpha parameter (buffer) 0.05 0.05 0.05

(+) Operation cost 0.09 0.09 0.09

(+) Margins for oil producers 0.04 0.05 0.05

(+) Margins for oil dealers 0.08 0.12 0.12

Actual price 2.48 2.14 2.93

(-) Sales taxes 0.58 0.58 0.58

(-) Subsidy 0.24 0 0.25

Retail price 1.62 1.56 2.1

Source: EPU (2013)

Table 3 shows the calculation of retail prices for RON95 petrol in Malaysia. Diesel and

petrol are subject to a sales tax of 19.64 cents/L for diesel and 58.62 cents/L for petrol.

The price setting mechanism for petroleum products was established in 1983. The

formula used to calculate the fuel price is called the Automatic Pricing Mechanism

(APM) and its function is to stabilize the price of petrol and diesel in the country via a

variable amount sales tax and subsidy. As a result, the retail price only has to be changed

if the price difference exceeds the threshold of the tax and subsidy at the discretion of the

government.

The Government ensured low prices for petroleum products by providing sales tax

exemption and subsidies for all petroleum products. Sales tax was introduced in 1983 is

one of the sources for government‘s revenue as well as a price stabilizer for petroleum

products. Therefore, to reduce the retail price of petroleum products, the first step taken

by the government was to provide a sales tax exemption on the product that caused a loss

of revenue to the government. In recent years, the amount of tax exemption had

increased. For example, in 1993, revenue loss was RM190.8 million but increased to

RM2.6 billion in 1999 and further increased to RM7.9 billion in 2005. Sales tax

exemptions on diesel began in October 1999 following the increasing price of diesel and

petrol since June of 2004 (EPU, 2005).

3. METHODOLOGY

We propose to apply system dynamics methodology developed by Forrester (1950)

starting from problem articulation over the design of a dynamic hypothesis, formulation,

and testing to evaluation essentially applying the best practices of system dynamics

modelling (Martinez-Moyano and Richardson, 2013). System dynamics is a computer-

aided approach to policy analysis and design. It applies to dynamic problems arising in

complex social, managerial, economic, literally any dynamic systems characterized by

interdependence, mutual interaction, information feedback, and circular causality.




www.ijbmes.com

In system dynamics, a system is represented by feedback loops which simulate the

dynamic behaviour of the system. The problem or system is first represented as a stock

and flow diagram in this study. The system dynamics modelling is conducted in two

phases: model building and model testing. The best way to undertake the system

dynamics modelling is considered and consists of four stages: (1) problem identification

and definition, (2) system conceptualization, (3) model formulation, (4) model testing and

evaluation.

Dynamic systems consist of interconnected feedback loops and the feedback loops

simulate dynamic behavior of the systems. There two fundamental types of variable

elements within each loop which are the building blocks of a system dynamics model.

These building blocks are stock and flow. The stock is a state variable and it represents

the state or condition of the system at any time t. The flow shows how the stock changes

with time. The flow diagram shows how stocks and flows are interconnected to produce

the feedback loops and how the feedback loops interlink to create the system. Figure 2

shows the stock-flow diagram of the farmer’s expenditure in Malaysia. The relationships

represented in the flow diagram are expressed in terms of integral and algebraic equations

and these equations are solved numerically to simulate the dynamic behavior.

These processes are usually done by using specific software like a Stella, Vensim and

others. Once stock and flow is finished, equations among variables are set. A stock is a

term refers to variables that accumulates or depletes over time. Inflow or outflow is the

indicator of change in a stock. The simulation of the model begins as the equations are

written, and comparison of the simulation results of the model to the real behavior of the

system are done. The model and parameters are summarized in Fig. 2 and Table 4.

Table 4. List of parameters for farmer’s expenditure

No Variables Unit Value Source

1 Fuel subsidy fraction Dmnl 0.3 IISD (2013)

2 Initial input cost RM/ha/Year 429.9 MADA (2012a)

3 Initial other cost RM/ha/Year 9.36.2 MADA (2012a)

4 Initial world crude oil price RM/liter 0.46 Indexmundi

(2014)

5 Input cost growth rate Dmnl 0.032 Estimated

6 Non energy machinery cost growth

fraction

Dmnl 0.032 Estimated

7 Other cost growth rate Dmnl 0.032 Estimated

8 World crude oil growth rate 1/Year 0.06 Estimated

Fig. 2: Stock and flow diagram of farmer’s expenditure




www.ijbmes.com

Desired expenditure

for paddy

Input cost

Initial other cost

Desired

expenditure for fuel

Initial world crude

oil price World crude oil

growth rate

World crude oil

price Change in world

crude oil price

Domestic fuel

price

Input cost growth

rate

Initial input cost Other cost

Other cost

growth rate

Non fuel growth

fraction

Non fuel

Non fuel growth

rate

Desired exp for

machinery

Fuel subsidy

fraction

Desired exp for

input

<Paddy area>

Desired exp for

others

Desired non fuel

Total expenditure

Allocation input Allocation

machineryAllocation other

<Desired exp for

machinery>

Cash adequacy

for input

Allocation for

non fuel

Allocation for fuel

<Desired

expenditure for fuel> Cash adequacy

for fuel

<Allocation for non

paddy production>

<Allocation for paddy

production>

<Desired energy for

paddy production>

4. MODEL VALIDATION

Initial values and the parameters were estimated from the primary and secondary data

collected from different research reports, statistical year books of Malaysia and field

visits. Tests were also conducted to build up confidence in the model. To build up

confidence in the predictions of the model, various ways of validating a model such as

model structures, comparing the model predictions with historic data, checking whether

the model generates plausible behaviour and checking the quality of the parameter values

were considered. In the behaviour validity tests, emphasis should be on the behavioural

patterns rather than on point prediction (Barlas, 1996).

Fig. 3: Simulated and historical patterns of paddy productivity in Malaysia (1990-2012)




www.ijbmes.com

Fig. 4: Simulated and historical patterns of domestic fuel price in Malaysia (1990-2012)

Figure 3 and 4 shows the comparison of simulated behaviours of paddy productivity and

domestic fuel price with the historical data. Simulated behaviours are numerically

sensitive to parameters and shapes of the table functions. However, the basic patterns of

the historical and simulated behaviours agree adequately and model predictions represent

reality.

5. POLICY ANALYSIS

Given the rising global energy demand and economic growth, contribution from the oil

and gas industry is expected to increase further by approximately 20% over the next 5

years (DOS, 2014). Naturally, the world crude oil price is expected to increase by 50% in

2016 until 2030.




www.ijbmes.com

Table 5. Simulation results for domestic fuel price, allocation for fuel price and paddy

expenditure under the crude oil price increase

Domestic fuel price Allocation for fuel Paddy expenditure

Time BAU S2 % BAU S2 % BAU S2 %

2015 1.44 1.38 -3.70 222346320 223581264 0.56 4849715200 4854893056 0.11

2016 1.52 1.46 -3.84 234558112 235917280 0.58 4981175808 4986827776 0.11

2017 1.62 1.60 -1.27 247421216 256494784 3.67 5115979776 5129707520 0.27

2018 1.72 1.74 1.37 260971664 278847552 6.85 5254259200 5277161472 0.44

2019 1.82 1.90 4.08 275247232 303142272 10.13 5396143104 5429466112 0.62

2020 1.93 2.07 6.87 290287904 329521504 13.52 5541767168 5586827264 0.81

2021 2.05 2.25 9.73 306135616 358193952 17.00 5691267072 5749580288 1.02

2022 2.18 2.46 12.66 322834656 389327648 20.60 5844783104 5917957632 1.25

2023 2.31 2.68 15.68 340431616 423169248 24.30 6002458624 6092335616 1.50

2024 2.46 2.92 18.77 358975648 459917120 28.12 6164436992 6272974848 1.76

2025 2.61 3.18 21.95 378518368 499862112 32.06 6330869760 6460299776 2.04

2026 2.77 3.47 25.21 399114336 543238528 36.11 6501909504 6654607872 2.35

2027 2.94 3.78 28.56 420821024 590389888 40.29 6677715456 6856382464 2.68

2028 3.12 4.12 32.00 443699040 641592768 44.60 6858449408 7065963008 3.03

2029 3.31 4.49 35.53 467812160 697252864 49.05 7044278272 7283899392 3.40

2030 3.52 4.90 39.16 493227456 757696448 53.62 7235376128 7510582272 3.80

Fig. 5: Simulated domestic fuel price, allocation for fuel and expenditure on paddy

(Increasing crude oil price by 50%)




www.ijbmes.com

Since no policy is implemented under the extreme tests, domestic fuel price will follow

worlds crude oil price trend which increasing by 40% in 2030 with an average of 3.8%

per year (Figure 5 and Table 5). Meanwhile, expenditure of paddy increases by 3.8%.

Fuelpricesalsoindirectlyaffectthe price offertilizer. 1% increaseinworldcrude oil

pricewillincrease the fertilizerprice by 3.4%. Indirect effect is greater than direct effect on

productivity. Therefore, rising fuelpriceswillincrease the price offertilizerand ultimately

affectfarmers'expenses by 45%.

6. CONCLUSION

In this paper, impact of fuel price change on paddy farmers’ expenditure allocation in

Malaysia is studied using a system dynamics approach. Attempt has been made to

develop a generic model, representing the essence of the dynamic theory that emerged

from the survey. Model analysis reveals that an increase in fuelpriceswillincrease the

price offertilizer, eventually it leads to an increase in farmers’expenditure. It is also




www.ijbmes.com

observed that, the range of production costs to produce one kilogram of paddy is between

RM 0.28 and RM 0.40.

7. REFERENCES APERC. (2012). APEC energy overview. Asia Pacific Energy Research Centre (APERC).

Retrieved from http://aperc.ieej.or.jp/file/2013/6/28/APEC_Energy_Overview_2012.pdf.

Barlas, Y. Formal aspects of model validity and validation in system dynamics.System

Dynamics Review. 12 (1996) 183-210.

Economic Planning Unit (EPU) (1957).First Malaya Plan, 1957-1960. Kuala Lumpur:

Government Printers.

Energy Commission Malaysia (ECM) (2011). National energy balance. Issue No. 0128-6323.

Putrajaya: Suruhanjaya Tenaga Malaysia.

Forrester, J. W. 2007. System dynamics – the next fifty years. System Dynamics Review, 23(2-

3): 359-370.

InflationData.com (2013). Historical crude oil prices. Retrieved

from http://inflationdata.com/Inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp

MADA (2011). Statistics of MADA 2011. Kedah: Lembaga Kemajuan Pertanian Muda

___(2012a). Penyiasatan pengeluaran padi.Retrieved

fromhttp://www.mada.gov.my/penyiasatan-pengeluaran-padi

___(2012b). Statistics of MADA 2012. Kedah: Lembaga Kemajuan Pertanian Muda.

___(2012c). Irrigation and Drainage in the MADA area. Kedah: Divison of Water management

Lembaga Kemajuan Pertanian Muda.

___(2012d). Nota Panduan: Projekestetpadipengurusanberpusat program NKEA MADA

[Brochure]. Kedah: Lembaga Kemajuan Pertanian Muda.

___(2013). Statistics of MADA 2013. Kedah: Lembaga Kemajuan Pertanian Muda.

MADA website (2014). Pengenalan PPK. Retrieved from http://www.mada.gov.my/pengenalan-

ppk)

Malaysian fuel price 8th lowest in world (2013. September 3). Bernama, p.1.

Martinez-Moyano, I. J. and Richardson, G. P. 2013. Best practices in system dynamics

modeling. System Dynamics Review, 29(2): 102-123.

http://aperc.ieej.or.jp/file/2013/6/28/APEC_Energy_Overview_2012.pdf

http://inflationdata.com/Inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp

http://www.mada.gov.my/penyiasatan-pengeluaran-padi

http://www.mada.gov.my/pengenalan-ppk

http://www.mada.gov.my/pengenalan-ppk




www.ijbmes.com

Ministry of Agriculture and Agro-based Industries (MoA)

(2008).DasarSekuritiMakananSektorPertaniandanIndustriAsasTani.Putrajaya: Ministry

of Agriculture and Agro-based Industries.

Nordiana, I., & Low, S.M. (2010). Factors affecting paddy production under Integrated

Agriculture Development Area of North Terengganu (IADA KETARA): A case study.

Paper presented at The Rice Proceeding, July 25 – 29, Washington, DC, USA.

Rashid, M.R., & Dainuri M.S. (2013). Food and livehood security of the Malaysian paddy

farmers. Economic and Technology Management Review, 8, 59-69.

Robiah Lazim (2003). Analisa ringkas trend kos pengeluaran di era 70an, 80an, 90an dan awal

abad 21 di dalam kawasan Muda. MADA publications.

Impact of Fuel Prices on Paddy Farmer’s Expenditure ...

Documents

Transcript of Impact of Fuel Prices on Paddy Farmer’s Expenditure ...