Reducing fuel subsidies and the implication on fiscal balance and poverty in Indonesia: A simulation...

Energy Policy 58 (2013) 117–134

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Reducing fuel subsidies and the implication on fiscal balance andpoverty in Indonesia: A simulation analysis

Teguh Dartanto n

Institute for Economic and Social Research (LPEM), Faculty of Economics, University of Indonesia, Jl. Salemba Raya No. 4, Jakarta 10430, Indonesia

H I G H L I G H T S

c Massive fuel subsidies reduce fiscal spaces used to alleviate poverty in Indonesia.c Indonesia can avoid a budget deficit by 78% cutting of fuel subsidies.c A CGE-microsimulation is applied to analyse the impacts of fuel subsidy reallocation.c The 50% of reallocation fuel subsidies decreases the poverty by 0.277 percentage points.c Mark-up pricing done by economic agents reduces the effectiveness of reallocation.

a r t i c l e i n f o

Article history:

Received 27 August 2011

Accepted 21 February 2013Available online 10 April 2013

Keywords:

Removing fuel subsidies

Fiscal balance

Poverty

15/$ - see front matter & 2013 Elsevier Ltd. A

x.doi.org/10.1016/j.enpol.2013.02.040

þ62 21 314 3177; fax:þ62 21 334 310.

ail address: [email protected]

il revenue consists of tax and non-tax rev

jority of Indonesia’s oil output has been ext

investors. Private contractors share their rev

revenue-sharing agreements. The revenu

g income (the amount of oil production rev

ot including any production related-governme

rges include the corporate income tax, inte

na’s retention fee and local taxes.

a b s t r a c t

There is an urgent need for phasing out fuel subsidies in Indonesia due to a severe budget deficit and a

worsening of income distribution. Fuel subsidies, of which almost 72% are enjoyed by the 30% of the

richest income groups, have consumed on average 63.8% of the total subsidies between 1998 and 2013.

This paper aims at evaluating the relationship between existing fuel subsidies and fiscal balance and at

analysing the poverty impact of phasing out fuel subsidies. Applying a CGE-microsimulation, this study

found that removing 25% of fuel subsidies increases the incidence of poverty by 0.259 percentage

points. If this money were fully allocated to government spending, the poverty incidence would

decrease by 0.27 percentage points. Moreover, the 100% removal of fuel subsidies and the reallocation

of 50% of them to government spending, transfers and other subsidies could decrease the incidence of

poverty by 0.277 percentage points. However, these reallocation policies might not be effective in

compensating for the adverse impacts of the 100% removal of fuel subsidies if economic agents try to

seek profit through mark-up pricing over the increase of production costs.

& 2013 Elsevier Ltd. All rights reserved.

2 Fuel subsidies are distributed to five fuel products: gasoline (known as

Bensin/Premium), kerosene (known as Minyak Tanah), automotive diesel oil (ADO)

1. Introduction

Indonesia has not been a net oil-exporting country since 2003and has had decreasing oil production and increasing consump-tion. Its crude oil production has decreased by roughly 3% peryear while overall fuel consumption has increased by roughly 4%per year during last 15 years (OPEC, 2008, 2012). Indonesia issuffering fiscal pressures due to the decrease in oil revenues in theterms of tax and non-tax revenues1 and rapid increase in fuel

ll rights reserved.

enue (oil revenue sharing).

racted under contracts with

enues with the government

e shares are based on net

enue minus costs of produc-

nt taxes and charges). Taxes

rest dividend tax, royalties,

subsidies.2 This is because fuel prices in Indonesia are notdetermined by market mechanisms but administratively by thegovernment. These prices are frequently set lower than theinternational market prices; thus, the government has to fill theprice gap with subsidies.3 Oil revenues and fuel subsidies,

(known as Solar), industrial diesel oil (IDO) (known as Solar Industry) and fuel oil

(known as Minyak Bakar—mainly for industries). Since 2005 the government has

only subsidised the three products of gasoline, kerosene and automotive diesel

oil (ADO).3 The fuel subsidy is essentially subsidising the price gap between the

domestic retail price (administered price) and the economic price. The govern-

ment pays the subsidy to Pertamina—the state owned oil enterprise that has a

mandate to provide and distribute subsidised fuels in Indonesia. The economic

price consists of four components: (1) the border wholesale price of refined

products referring to Mid Oil Platt’s Singapore (MOPS); (2) costs of transportation,

storage and distribution; (3) value added tax and fuel tax; and (4) profit margin of

retail stations.

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Table 1Share of Fuel Subsidies Received by Households in 2008.

Source: Author’s calculation based on SUSENAS 2008.

HouseholdGroup byconsumptiondeciles

Share of fuel subsidies (%) Householdexpenditure(IDR/month/capita)

Fuelsubsidiesreceived byhouseholds(IDR/month/capita)

Kerosene Gasoline Dieselfuel

1 3.70 0.55 0.05 123,256 10,787

2 5.28 1.32 0.49 164,925 16,410

3 7.00 2.19 0.84 196,632 22,573

4 8.15 3.39 1.24 229,225 27,802

5 9.73 4.70 1.93 265,084 34,436

6 11.59 6.78 2.17 308,761 43,114

7 13.56 9.10 2.35 363,421 52,581

8 15.03 12.56 5.02 440,198 62,975

9 14.60 17.63 16.95 571,048 72,031

10 11.36 41.77 68.95 1,090,754 111,533

Note: Fuel subsidies received by households¼(market fuel prices�subsidised fuel

prices)n quantity of fuel consumptions.

T. Dartanto / Energy Policy 58 (2013) 117–134118

therefore, always dominate the nation’s economic policy agendawhen the international oil prices sharply fluctuate.

The international oil prices have been unpredictable duringthe last 10 years. Fig. 1 shows the fluctuation of the 2005 priceindex of crude oil. The price was 25.95 USD/Barrel (January 2001),42.89 USD/Barrel (January 2005), 131.52 USD/Barrel (June 2008),64.65 USD/Barrel (July 2009) and 101.17 USD/Barrel (November2012). Son (2008) remarked that Indonesia spent 5% of its grossdomestic product (GDP) on energy subsidies. In 2008, Agustinaet al. (2008) confirmed that the Indonesian government wasforced to spend around 27.93% of its total budget on energysubsidies and 80% of this was allocated for fuel subsidies. Thegovernment currently plans to allocate nearly 17% of budget tofuel subsidies in 2013.

Other developing and emerging economies, where govern-ments have significant influence over domestic prices, haveincreased fiscal costs, responding to the large increase in inter-national fuel prices during 2003–2006. Baig et al. (2007) observedthat, in 2005, fuel subsidies (as a percentage of GDP) cost around5.8% in Jordan, 9.2% in Yemen, 13.9% in Azerbaijan and 4.1% inEgypt. This condition forced governments to fully pass-throughthe international fuel prices to the domestic retail prices to reducefiscal costs. The average price pass-through during 2003–2006 inthe net oil importer countries was 1.09 (gasoline), 0.91 (kerosene)and 1.15 (diesel oil) (Baig et al., 2007).

Massive fuel subsidies reduce fiscal space so governments havefewer sources to promote economic growth through investment ininfrastructure and human capital and also to provide better socialprotections for low income groups through better targeted sub-sidies and other social expenditures. Fuel subsidies also worsenincome distribution in Indonesia because most of the fuel subsidiesare enjoyed by the non-poor groups, rather than by poor groups.Table 1 shows that, in 2008, more than 41% of gasoline subsidiesbenefitted the top richest income groups in Indonesia. The top 30%of the richest income groups enjoyed almost 72% of gasolinesubsidies. On the other hand, kerosene subsidies were distributedmore equally to all households compared to gasoline subsidies.Thirty per cent of the lowest income groups consumed 16% ofkerosene subsidies and only 4% of gasoline subsidies. This isbecause most of those in this group rarely own a motor vehicle,so their gasoline consumption is very low. Generally, the richest

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Price index of Crude Oil (2005 = 100), ThWest Texas Intermediate, and the Dubai

Fig. 1. The monthly international crude oil price (1980–2012) (constant 2005 prices).

Source: Plot based on the IMF primary commodity statistics.

income group received fuel subsidies of approximately IDR111,533/month/capita while those for the lowest income groupwere approximately IDR 10,787/month/capita.

In 2001, the government carried out the first initiative onderegulating the domestic fuel prices to reduce fiscal costs, toimprove the allocation of appropriate budgetary targets for thepoor, and to promote industrial competitiveness. It was enactedby the Presidential Decree No.45/2001 that principally deter-mined retail prices depending on the type of consumers. Whileprices for households’ consumption, land and water transporta-tions, and small enterprises (henceforth the retail fuel prices)were regulated by the government, the retail fuel prices forindustries and fisheries were set at 50% of international prices.Moreover, mining sectors under the Kontrak Karya, oil-gas indus-tries under the revenue sharing contract, foreign-flagged vesselsand vessels with overseas destinations had to pay 100% ofinternational market prices.

In 2003, the government fully deregulated fuel prices forindustries, fisheries, mining sectors, foreign-flagged vessels andvessels with overseas destinations (henceforth the industrial fuel

e simple average of three spot prices: Dated Brent,Fateh

T. Dartanto / Energy Policy 58 (2013) 117–134 119

prices) in which the domestic fuel prices are delivered to themarket in accordance with the international prices. This policywas regulated with No. 31 K/20/MEM/2003 and 31/KMK.01/2003.Nevertheless, the government still regulated the retail fuel pricesfor households’ consumption, land and water transportation,small enterprises, and, as of 2005 included small fisheries andpublic and social services. These are regulated under the pre-sidential regulation No. 55/2005, later revised as the presidentialregulation No. 9/2006.

A high fuel demand as a consequence of a rapid growing ofmiddle class in Indonesia has forced the government to spend asignificant amount of money to subsidise the disparity betweeninternational and domestic prices when there is a big gapbetween the two. During 2000–2009, the government thenirregularly adjusted the retail fuel prices following the fluctuationof international oil prices to reduce the fiscal burden of fuelsubsidies. Until now, the fuel subsidies have remained an unre-solved problem.

While Indonesia had on many occasions found it necessary toadjust domestic fuel prices through reducing fuel subsidies, thedrastic reduction of fuel subsidies in 2005 resulted in misery forthe poor. In addition to increasing the cost of energy, it alsoindirectly increased non-fuel prices (e.g. increasing the cost ofliving, food, transportation, etc.). Reduction of fuel subsidiesaffected households’ welfare as well as poverty depending onthe importance of energy and private transport costs in totalhousehold consumption and the fuel intensity on the productionof goods and services. Since the poor as well as low incomegroups rarely have an enough saving for consumption smoothingto response the increase of price levels, they will easily fall intopoverty (a deeper poverty). According to the Central StatisticalAgency (henceforth BPS), the number of poor people increasedfrom 16% to 17.8%, the equivalent of 3.95 million people (1.8% ofpopulation) during 2005–2006.4 To mitigate the negative impactof phasing out of fuel subsidies, the government implemented theProgram Kompensasi Pengurangan Subsidi-BBM (compensationprogramme for fuel subsidy reduction) in 2005 and 2008. Thisprogramme included cash transfer, health insurance, educationsubsidies and also rural infrastructure development.

Removing fuel subsidies affects low income groups as itdecreases their purchasing power, while reallocating fuel sub-sidies to infrastructure spending can remove infrastructure bot-tlenecks and create job opportunities. Moreover, reallocating fuelsubsidy to education and health spending can equip the poor tobe more competitive and creative. Many studies such as Jung andThorbecke (2003), Fan et al. (2000), Davis et al. (2001) andRoberts (2003) have confirmed that spending on education,health and infrastructure effectively reduces poverty all over theworld. Clements et al. (2007) found that the 2005 Indonesianreduction in fuel subsidies, in the short run, increased price levelsand reduced household consumption, particularly for the poor.However, in the long-term, given the contribution of subsidyreduction to fiscal sustainability (a precondition for durableeconomic growth and poverty reduction), the subsidy reductionwas beneficial to the poor.

Although reallocating fuel subsidies either into infrastructuredevelopments or human capital investments or social expendi-tures might increase poverty in the short run, it may decrease it inthe long run, due to improvements in infrastructure, increasesin human capital and a better provision of social protections.

4 The official poverty rate in Indonesia is calculated based on the expenditure

based poverty. Those with monthly expenditures for both foods and non-foods

less than the 2005 BPS poverty line are classified as poor. In 2005, the average

monthly money metric of the national poverty line was IDR 117,259 (USD 11.7) in

rural areas and IDR 150,799 (USD 15) in urban areas.

This paper then aims at addressing the following three mainquestions: first, what is the relationship between existing fuelsubsidies and fiscal balance? Second, how large is the impact onpoverty when fuel subsidies are removed? Third, how effectiveare reallocation policies in protecting low income groups from theadverse impacts of removing fuel subsidies?

This paper contributes to the debate on the fiscal and povertyimpact of phasing out fuel subsidies in Indonesia by offering acomprehensive picture of the oil industry and development of thefuel price system and also by providing an overview of thelinkages between the existing retail fuel price systems and thefiscal costs, and the poverty impact of phasing out fuel subsidies.The results of analysis will thus be of benefit to Indonesia’s policymakers in designing a better method of removing fuel subsidiesthat can guarantee both the long run fiscal sustainability and havethe minimum adverse impact on the low income groups.

In the following section, this paper briefly reviews the currentcondition of oil production and fuel consumption in Indonesiaand subsequently introduces the history of fuel price regimes inIndonesia for 1970–2012. Section 4 discusses the existing fuelsubsidies and fiscal balance. Section 5 reviews the need forderegulation of retail fuel prices and the literature review ofthe poverty impact of phasing out fuel subsidies. A review of theresearch methodology of a CGE microsimulation analysis and thesimulation scenarios is given in Section 6. Section 7 analysesthe poverty impact of removing fuel subsidies and reallocatingthe saved money to protect the poor from the adverse impacts.Finally, the concluding section of the paper will summarise thekey findings and discuss policy suggestions for possible realloca-tion policies to reduce adverse impacts.

2. Oil production and fuel consumption

Since 2003, Indonesia has become a net fuel and oil importercountry, as production and refinery capacity have stagnated whileconsumption has grown rapidly. The change to becoming a netimporter led Indonesia to withdraw from OPEC in 2008. The ratiobetween crude oil production and fuel consumption has beencontinuously decreasing; it fell from 205% in 1990 to 128% in2000 and then to 58% in 2011. This is mainly due to lack ofinvestment in exploring new oilfields, the declining productionfrom maturing fields and the increasing fuel demands from thegrowing middle class population. These figures imply that even ifall the domestic crude oil were refined in Indonesia, it would notbe enough to fulfil the domestic demand.

Fig. 2 shows that Indonesia’s crude oil production decreasedfrom 1575.7 thousand barrels per day (bpd) in 1980 to 1272.5thousand bpd in 2000 and continuously decreased to 794.4thousand bpd in 2011, while fuel consumption grew rapidly from114.1 thousand bpd in 1970 to 1370.6 thousand bpd in 2011. In thepast three decades (1980–2011), the demand for fuel productsincreased more than three-fold as a result of the three-fold increasein per-capita GDP. The export trend of crude oil has continuouslydeclined by almost 5% per year, while the import trend of crude oilgrew rapidly at 1.5% per year during 2000–2011.

The ratio between refinery capacity and total consumptionsubstantially decreased from 106% in 2000 to 74% in 2011. Since2002, domestic refineries have not been able to satisfy domesticfuel demands. Meanwhile, for technical reasons, the domesticrefineries have been unable to process the domestic crude oil.Therefore, Indonesia has to import both petroleum products andcrude oil products to fill the gap. On the other hand, proven oilreserves decreased from 11,603 million barrels in 1980 to 5123million barrels in 2000 to 3990 million barrels in 2011, mainly

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1,00

0 ba

rrel

per

day

(bpd

)

Fig. 2. Oil and petroleum products in Indonesia, 1970–2011.

Source: The oil data are drawn from OPEC Annual Statistical Bulletin (OPEC, 2005, 2008, 2012), while the GDP per-capita is drawn from the World Bank database.


due to the level of exploitation without any significant invest-ment into exploring new oilfields.

3. History of fuel price regimes in Indonesia

3.1. Trend of administered domestic fuel prices during 1970–2012

The Government of Indonesia had significant influence overdomestic fuel prices for many decades. While the retail prices ofindustrial diesel oil (IDO) and fuel oil have been followed theinternational market since October 2005, the government stillcontrol the retail prices of gasoline, automotive diesel oil (ADO)and kerosene. The government therefore have carried out irregu-lar and ad hoc adjustments to the domestic fuel prices to achievesome development agenda. Fig. 3 shows the trend of adminis-tered domestic fuel prices during 1970–2012.

In terms of real price, the gasoline price remained relativelystable while the price of diesel oil and kerosene decreased byalmost 31% and 39%, respectively, during 1970–1979. The govern-ment had adjusted 10 times of domestic fuel prices during thisperiod and the adjustments were frequently done in everyApril—the beginning of fiscal year. The price adjustment duringthis period was directed to achieve three main goals: (1) toincrease the government revenue, (2) to maintain the socio-political stability, and (3) to support development of non-oiland gas industries through providing low energy prices ofdiesel oil.

In contrast to the pattern of price adjustment in 1970–1979, thegovernment irregularly corrected the fuel prices during 1980–1989. The price adjustments had increased the real fuel price ofgasoline, diesel oil and kerosene by 22%, 82% and 108% correspond-ingly, i.e. the real price of gasoline increased from IDR 1237/l

to IDR 2253/l. In this period, the adjustment of domestic fuel pricesaimed at increasing the government revenue to compensate for theshrinking in the revenue from oil exports resulting from the drasticprice decline of international crude oil.

During 1990–1998, while the nominal price of domestic fuelsincreased on average by almost 135%, the average real price ofdomestic fuels decreased by almost 27% because of the highinflation in the period of economic crisis. During 1970–1998, theaverage real price of domestic fuels remained continuously higherthan the average international prices. The government thereforecould collect additional revenue from the gap between the sellingprice of domestic fuels and its production costs.

In contrast to the previous periods, in the period 2000–2012domestic fuel prices were irregularly adjusted with ad hocformula following the increase in international oil prices toreduce fiscal pressure. For example, the gasoline price wascorrected by IDR 1150/l (February 2000), IDR 1450/l (June2001), IDR 1810/l (March 2003), IDR 2400/l (March 2005), IDR4500/l (October 2010), 6000/l (June 2008), IDR 5500/l (1 Decem-ber 2008), IDR 5000/l (15 December 2008) and IDR 4500/l(January 2009). The highest adjustment price occurred in 2005,when gasoline prices rose by 148% from IDR 1810/l in January toIDR 4500/l in October. The retail price of gasoline, diesel oil andkerosene, however, remained unchanged since 2009, although thecurrent government is experiencing a tight fiscal space to pro-mote infrastructure developments and to finance social servicesdue to the huge fuel subsidies.

3.2. Price gap between retail prices and industrial prices

As is mentioned in the Introduction, the government has triedto set industrial fuel prices so that they follow internationalprices. While in 2001 industrial fuel prices were fixed at 50% of

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lite

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Fig. 3. Trends of administered domestic fuel prices during 1970–2012.

Source: Author’s compilation based on data from department of energy and mineral resources, Republic of Indonesia and CEIC database.


international prices, in January 2002 these prices were set at 75%of international prices. The international fuel prices refer to theprevious month’s average of Mid Oil Platt’s Singapore (MOPS) plussome adjustment factors. Instead of setting the industrial pricesas a percentage of international prices, the presidential decreeNo. 90/2002 enacted floor and ceiling fuel prices for industries.For example, the price of industrial diesel oil ranges from IDR1600/l to IDR 2050/l.

Fully deregulated industrial prices and regulated retail priceshave created a dual price system in the fuel market in Indonesia.Fig. 4 shows the price gap between the two prices. Besidesburdening the government budget, this gap has created incentivesto some people to resale subsidised fuels to industries and also toengage in cross-border smuggling. The higher international pricethe wider the price gap and the greater the subsidy to be financedby the government.

During 2001–2009, the largest gap between the subsidisedretail price and the market price of gasoline was observed in May2008, reaching IDR 3370/l. This pushed the government to adjustdomestic fuel prices, i.e. the price of gasoline rose from IDR 4500/lto IDR 6000/l. Under the administered price system, the govern-ment is always forced to make ad hoc price adjustments when thebudget allocation for fuel subsidies does not sufficiently cover theprice gap. The adjustment, however, always creates political andsocial instability because of objections from opposition parties.

5 The higher the international oil price the higher will be the cost of electricity

production since PLN, the state owned electricity enterprise, still relies on diesel

oil to generate electricity in many of its power stations. Similar to the fuel subsidy

paid to Pertamina, the government has to finance the price gap between the

production cost and selling price in the term of electricity subsidy.

4. Fuel subsidies and fiscal balance of oil and gas products

4.1. The flow out of government revenue

An increase in the international price of oil and gas increasesnot only oil and gas revenues but also expenditures. As theinternational price increases the government will receive addi-tional non-tax revenue from the revenue sharing between privatecontractors and the government and also additional income taxrevenue from a higher profit of oil and gas industries, while theincrease in expenditures comes from energy subsidies and trans-fers to sub-national governments.

Table 2 describes the magnitude of oil and gas revenues andhow these resources flow out of the Indonesian central govern-ment’s budget: first, in the form of fuel and electricity subsidies5 ;second, via sub-national government transfers as revenue sharingis distributed to some producing regions; third, 26% of theprojected net oil and gas revenue budget is transferred to sub-national governments as part of a general allocation fund (DAU).The second and third revenue flow outs are a consequence of the‘‘big bang’’ fiscal decentralisation in 2001.

Oil and gas revenues contribute a significant share to Indone-sia’s central government budget. Table 2 shows the highest sharewas in 2000; in this year, almost 43% of central governmentrevenue came from oil and gas revenues. The relative contributionof oil and gas on the central government budget has tended todecrease, mainly due to a shrinking in lifting capacity and anincrease in other government revenues. Indonesia’s budget, there-fore, is becoming less dependent on oil and gas revenues.

Historically, oil and gas revenues have exceeded fuel subsidyexpenditures and sub-national government transfers, the exceptionbeing those years with large increases in international oil prices.Until 2004, oil and gas were black gold to Indonesia’s centralgovernment budget balance. However, when the international oilprice significantly increases, oil and gas became a black hole to thegovernment’s budget balance. The government was still able toenjoy the net benefit of oil and gas revenues in the period of highinternational oil price in 2006 and 2009, mainly due to the effect ofthe adjustment of retail fuel prices in 2005 and 2008.

Viewing fuel subsidies as opportunity costs, during 2000–2013Indonesia indirectly dissipated an average of 52.1% of oil and gasrevenues a year on unproductive allocation of fuel subsidies. If thegovernment had been able to cut fuel subsidies by 51% in 2011and 78% in 2013, in both years it could have avoided a budgetdeficit.

Table 2The expenditure cash flow of central government oil and gas revenue 2000–2013 (billion USD, constant 2005 prices).

Source: Author’s calculation.

Revenue and expenditure 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012a 2013b

a) Oil and gas revenue (tax and

non-tax revenue)

16.1 14.3 10.8 11.0 13.5 14.3 19.5 15.3 22.4 12.2 16.0 19.8 17.6 14.8

Less

b) Fuel subsidies 9.5 9.4 4.3 4.1 8.6 9.9 6.2 7.6 10.8 3.1 6.2 12.3 9.1 12.1

c) Electricity subsidies 0.6 0.0 0.5 0.5 0.3 0.4 2.9 3.0 6.5 3.4 4.4 6.7 4.3 5.1

d) National budget balance (a–b–c) 5.9 4.9 6.0 6.5 4.5 4.0 10.3 4.7 5.1 5.7 5.4 0.8 4.2 �2.4

Less

e) DAU (26% of APBN projected

net oil & gas revenue)

0.9 1.3 2.5 2.2 2.1 1.3 1.3 3.7 3.3 3.2 4.2 5.1 4.6 3.8

f) Oil & gas revenue sharing to

local governments

2.3 2.1 1.5 1.8 1.9 2.8 2.7 2.0 2.6 1.8 2.7 2.8 2.8 2.1

g) Central government budget balance

(d–e–f)

2.7 1.5 2.0 2.5 0.6 �0.1 6.3 �0.9 �0.8 0.7 �1.4 �7.1 �3.1 �8.3

h) Fuel subsidies as % of total oil and

gas revenue (b/a)

59.2 65.7 39.5 37.2 63.9 69.2 31.8 49.6 48.2 25.6 38.9 62.0 51.6 81.8

i) Fuel and electricity subsidies as % of

total oil and gas revenue ((bþc)/a)

63.1 65.8 44.2 41.5 66.4 72.0 46.8 69.2 77.3 53.7 66.1 95.9 76.0 115.9

j) Oil and gas revenue as % of total

government revenue

42.8 34.8 26.7 23.6 27.2 28.0 31.5 23.8 29.4 20.8 21.3 22.0 19.6 15.7

k) Budget deficit �6.07 �5.52 �3.19 �4.81 �2.93 �1.48 �2.82 �4.52 �0.32 �6.15 �3.54 �6.27 �12.56 �9.38

l) Budget deficit as % of fuel

subsidies (k/b)

63.8 58.9 74.9 117.0 34.0 14.9 45.5 59.5 3.0 196.8 56.9 51.1 138.4 77.5

Note:

Crude oil price (USD/barrel) 29 25 22 29 34 53 64 72 97 62 80 112 105 100

Oil lifting (thousand bpd) 1405 1273 1320 1092 1072 999 1000 899 931 944 954 900 930 900

Note: Data from 2002 to 2011 are the budget realization.a Based on the 2012 revised budget.b Based on the 2013 proposed budget. Data of a, b, c, e and f from 2000 to 2006 are drawn from Agustina et al. (2008) and data from 2007 to 2013 are drawn from the

publications of Ministry of Finance.

0

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12,000

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per

lite

r

Fig. 4. Comparison between retail prices and industrial prices during 2001–2012.

Note: Data are from Department of Energy and Mineral Resources, Republic of Indonesia and from CEIC Database. Data of industrial prices of diesel and kerosene

(2009–2012) are calculated based on the MPOS spot prices of petroleum products that are adjusted with the value added tax, the domestic taxes, transportation costs

and profit margin.

Source: Author’s compilation.


4.2. The government expenditure on fuel subsidies

The share of fuel subsidies to total government expenditurehas varied widely following movements in international oilprices, the exchange rate and adjustment to the subsidy regime(Table 3). They peaked in 2001, accounting for 31% of totalgovernment expenditure. Fuel subsidies decreased sharply in

2002 following the adjustment of retail fuel prices in June 2001.In early 2003, the Indonesian government tried to close the gapbetween domestic and international oil prices by deregulatingindustrial fuel prices. Fuel subsidies then sharply increased in2004 and 2005 following increases in international oil prices, butthen decreased again in 2006 and 2007 after the governmentadjusted retail fuel prices in March and October 2005.

Table 3Indonesian Central Government Expenditure and Subsidy Trend 1998–2013. (billion USD, constant 2005 prices).

Source: Author’s calculation based on the Ministry of Finance publications.

Expenditure 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012a 2013b

a) Total subsidies (bþe) 7.1 13.7 10.7 10.6 6.7 6.7 10.5 12.4 10.4 13.6 21.4 9.6 14.6 21.9 16.2 19.7

b) Energy subsidies (cþd) 6.0 9.4 10.1 9.4 4.8 4.6 8.9 10.3 9.1 10.6 17.3 6.6 10.6 19.0 13.4 17.2

c) Fuel subsidies 5.6 8.5 9.5 9.4 4.3 4.1 8.6 9.9 6.2 7.6 10.8 3.1 6.2 12.3 9.1 12.1

d) Electricity subsidies 0.4 0.9 0.6 0.0 0.5 0.5 0.3 0.4 2.9 3.0 6.5 3.4 4.4 6.7 4.3 5.1

e) Non-fuel subsidies 1.0 4.2 0.5 1.2 2.0 2.1 1.6 2.1 1.3 3.0 4.1 3.0 4.0 3.0 2.8 2.6

f) Development expenditure 8.2 9.4 7.3 5.7 5.0 5.8 4.2 3.4 5.8 5.8 5.6 5.3 6.1 8.8 11.1 12.1

g) Total government expenditure 34.1 48.1 40.6 29.8 30.2 29.2 29.3 37.0 42.6 45.8 53.8 43.6 52.7 65.6 70.7 71.2

h) Energy subsidies as % of total subsidies (b/a) 85 69 95 89 71 69 85 83 88 78 81 68 73 87 83 87

i) Fuel subsidies as % of total subsidies (c/a) 80 62 89 89 63 62 82 80 60 56 51 33 43 56 56 61

f) Fuel subsidies as % of energy subsidies (c/b) 94 90 94 100 89 90 96 96 68 72 62 48 59 65 68 71

g) Energy subsidies as % of total expenditure (b/g) 18 20 25 31 16 16 30 28 21 23 32 15 20 29 19 24

h) Fuel subsidies as % of total gov. expenditure (c/g) 17 18 23 31 14 14 29 27 15 17 20 7 12 19 13 17

i) Development expenditure as % of total gov.

expenditure (f/g)

24 19 18 19 17 20 14 9 14 13 10 12 12 13 16 17

Note: Data from 1998 to 2011 are the budget realization.a Based on the 2012 revised budget.b Based on the 2013 proposed budget. The fiscal year of 1998 and 1999 started from April to March of following year while starting 2001 the fiscal year has changed

from January to December. The 2000 budget was only for 9 months from April to December.


However, responding to a high increase in international oilprices in 2008, the government was forced to allocate 20% of itstotal spending to fuel subsidies; again, the government increasedretail fuel prices in June 2008 to reduce fiscal pressure. As a result,the share of fuel subsidies to total government expendituredecreased from 20% in 2008 to 7% in 2009. The populist policyof reducing retail fuel prices before the 2009 election, e.g. cuttingthe gasoline price from IDR 6000 to IDR 4500, and keeping thoseprices unchanged until now has forced the government toincrease the allocation of fuel subsidies by around 19% in 2011.

Table 3 shows that, during 2004–2011, Indonesia’s share ofdevelopment expenditures in relation to total government spendingwas lower than that of the share of fuel subsidies except in 2009.Even in 2005, the share of development expenditure was lower than10%. Low development expenditure might only be enough to replaceexisting capital, but not to increase capital formation in the economy.The high share of fuel subsidies eliminates the opportunity ofinvesting more in infrastructure, which is one of the necessaryconditions to promote economic growth. Further, fuel subsidiesconsumed an average of 63.8% of the total subsidies during 1998–2013 and peaked in 2000, accounting for 89.1% of total subsidies.

However, as is mentioned before, low income groups do notbenefit from most fuel subsidies to their low consumption of fuelsparticularly of gasoline and diesel oil. Transferring subsidies frommiddle income to poor households would improve incomedistribution and encourage more equal economic growth. In otherwords, continuing the current price system in which subsidies areenjoyed by the middle class amounts to creating structuralpoverty and crippling income distribution.

5. Reviews on deregulation of retail fuel prices and itsimpact on poverty

5.1. The need of deregulating retail fuel prices

Fuel subsidies in Indonesia tend to be highly regressive to therich, a far from ideal social safety net and not environmentallyfriendly. This is due to reducing the fiscal space to invest more ininfrastructure and human capital; inefficiencies in targeting thepoor; creating disincentives for households to consume fuels in anefficient way; undermining macro-economic stability (given thepro-cyclical trend of international oil prices); distorting price

signals to industry and households; and creating opportunitiesfor corruption, speculation and smuggling (Agustina et al., 2008).Thus, there are strong economic arguments to deregulate retailfuel prices or to phase out fuel subsidies in Indonesia.

In the long-term, there are three main reasons for deregulatingretail fuel prices or removing fuel subsidies. First, without thediscovery of new oil reserves, Indonesian oil reserves would onlylast a further 15–20 years. Thus, the deregulation of retail fuel priceswould prepare households for the situation in which there is 100%pass-through of the international price into the domestic market.Second, a fuel price adjustment or deregulation policy would give anincentive to use cheaper and more abundant domestic energysources, such as coal and gas. Third, on the environmental side,fossil fuels are a relatively ‘‘dirty’’ energy. Fuel price correctionswould decrease fuel consumption and support the use of environ-mentally friendly energies, such as natural gas or thermal energy.

Deregulation might not be easy to carry out due to strongobjections and political challenges, but the government shouldcontinuously attempt to reallocate fuel subsidies into either moretargeted subsidies or social expenditures. The key elements of asuccessful strategy to contain subsidies should comprise: makingsubsidies explicit; making pricing mechanisms more robust;combining reductions in subsidies with measures to protect thepoorest; using the resulting savings well; and transparency andconsultation (Baig et al., 2007). In the case of Yemen, a promisingstrategy for a successful reform combines fuel subsidy reductionwith direct income transfers to the poorest one-third of house-holds during reform, and productivity-enhancing investment ininfrastructure, plus fiscal consolidation (Breisinger et al., 2012).

5.2. A literature review on the poverty impact of

fuel price adjustments

Many studies have shown that cutting subsidies has adverseimpacts on poverty and inequality in Indonesia and other coun-tries. Ikhsan et al. (2005) found that decreasing the fuel subsidiesin 2005, without compensation, increased the Indonesian povertyindex from 16.3% to 16.7%. Yusuf and Resosudarmo (2008) statedthat the price reform could have been progressive in reducinginequality if it had only increased vehicle fuel prices; however, inpractice it tended to increase inequality, especially in urban areaswhere the price of kerosene also increased. A uniform cashtransfer to poor households that disregards poor households’


heterogeneity tends to over-compensate the rural poor butunder-compensate the urban poor. This is because the consump-tion pattern of households in the urban area is highly dependenton industrial products and also on transportation, which meansthat the increase of fuel prices will directly increase the price ofboth products. This will significantly reduce the household wel-fare in urban area than of rural household.

Azis (2006) showed that the drastic and massive reduction infuel subsidies in 2005 was not unnecessary, especially consideringthe adverse socio-economic, poverty and political repercussions ofit. The reduction in fuel subsidies could have been substituted byreducing subsidies for the banking sector; providing that the savedmoney was spent on agricultural-related infrastructures, it couldhave produced a favourable outcome in terms of income distribu-tion and poverty conditions without deteriorating macro-economicstability or injuring investors’ confidence.

According to research by del Granado et al. (2010), a 0.25 perlitre increases in fuel prices has substantially declined householdreal income by almost 17.3% in Ghana, 3.6% in Bolivia, 5.3% in SriLanka and 12.1% in Jordan. Breisinger et al. (2012) showed thatoverall growth effects of fuel subsidy reduction are positive ingeneral, but poverty can increase or decrease depending on reformdesign in Yemen. Reform without compensation raises povertyrates up to 2.6 percentage points while reform with compensationof direct cash transfers to the poorest one-third of households willreduce the poverty rate up to 4 percentage points.

6. Research methodology

6.1. CGE modelling for poverty analysis

In recent years, a number of papers have presented differentapproaches using Computable General Equilibrium (CGE) modelsto analyse the impact of government policies on poverty andincome distribution. Savard (2003, 2005) summarised that thereare four main categories of CGE modelling for poverty analyses.First is the CGE Model with Representative Household (CGE-RH).This approach, pioneered by Dervis et al. (1982), is thetraditional method which has been widely applied in addressingthe impact of policy on income distribution. The povertyanalysis is performed by using the variation of income of the RHgenerated by CGE model with household survey data to perform ex-ante poverty comparison. CGE-RH, however, cannot capture intra-group income distribution change. Second is the Integrated Multi-Households CGE Analysis (CGE-IMH). This method incorporates alarge number of representative household compared to the CGE-RHapproach. This method can analyse intra-group income distribu-tional change but it may create difficulties in data reconciliation.Cogneau and Robillard (2000), Decaluwe et al. (2005), and Yusuf andResosudarmo (2008) applied this method.

The third approach is the CGE-Microsimulation Approach (CGE-MS) which uses a CGE model to generate prices that link to a micro-econometric household microsimulation model. This method pro-vides richness in household behaviour, while remaining extremelyflexible in terms of specific behaviours which can be modelled. Themain drawbacks to the approach are that the feedback effects ofhousehold behaviour are not taken into account in the CGE/Macromodel. Some researchers, such as Chen and Ravallion (2004),Boccafunso and Savard (2006), Savard (2003, 2005), and Dartantoand Usman (2011), utilised this approach addressing many issuesrelated to poverty analysis. The last method is the CGE-HouseholdMicrosimulation (CGE-HHS), pioneered by Savard (2003), whichattempts to use the advantages of the CGE-IMG and CGE-MSmethods. CGE-HHS proposed to examine coherence between thehousehold model and the CGE model, introducing a bi-directional

link and, therefore, obtaining a converging solution between thetwo models. Feedback effects of household model are taken intoaccount in the CGE model.

6.2. CGE-microsimulation approach (CGE-MS)

Due to the simplicity of application and modelling, and theability to capture the change of intra-group income distribution,the CGE microsimulation approach (CGE-MS) will be used in thisresearch to calculate how reducing fuel subsidies and reallocatingthe money that is saved influences poverty in Indonesia. Chen andRavallion (2004), Savard (2003, 2005), and Dartanto and Usman(2011) used this method and built micro-simulations based oneconomic assumptions that are consistent with the CGE model,notably that households take prices as given and that those pricesclear all markets. They also did not attempt to assure full consis-tency between the micro-analysis and the CGE model’s predictions.

There are five steps in calculating the poverty impact ofreducing fuel subsidies and reallocation budget policies: first,the initial condition of poverty is calculated utilising the 2005SUSENAS data (National Socio-Economic Survey) published byBPS, which covers 64,407 households. Second, using the CGEmodel, the impact of reducing fuel subsidies and reallocationbudget policies on domestic prices is simulated (including factorincomes). Third, the price changes (including factor incomes)obtained from the CGE model are entered into the SUSENAS dataset to calculate the impact of reducing fuel subsidies andreallocation budget policies on household welfare. This step isknown as the microsimulation procedure. Fourth, the poverty lineis adjusted using price changes obtained from the CGE model inwhich the poverty line becomes endogenous. Finally, the povertyincidence is recalculated using data from steps three and four andcompared with the initial poverty incidence.

6.2.1. Indonesian computable general equilibrium

Computable General Equilibrium (CGE) models are a class ofeconomic models that use actual economic data to estimate howan economy might react to changes in policy, technology or otherexternal factors. The static CGE model is built based on theextension of the 2005 Indonesian Social Accounting Matrix(SAM) and follows the algorithm of the International Food PolicyResearch Institute (IFPRI) standard CGE model developed by Lofgrenet al. (2001). The data used for the extension of SAM refer to the2005 Input–Output Table, the 2005 National Socio-Economic Survey,the labour force survey and other sources. The structure of CGEmodel including activities/commodities, factor productions, andinstitution and household used in this research follows the CGEmodel built by Dartanto (2010) and Dartanto and Usman (2011).

The elasticity data used in this CGE refers to sources such asthe elasticity in Dartanto (2010), Dartanto and Usman (2011) andother estimation on elasticities of fuel products. The Armingtonelasticities, the elasticity of substitution between imports anddomestic output in domestic demand, are 0.5 for all commoditiesexcept soybeans (2.5), rice (2.5), food crops (2.5), non-food ofagricultural products (2.5), livestock (2.5), food-beverage industry(2.5) and textile and garments (2.5), and fuel and chemicalproducts (1.5). Constant elasticity of transformation (CET) fordomestic marketed output between exports and domestic sup-plies is set equal 0.5 for all commodities except rice (2.0),soybeans (1.5), food crops (1.5), and food-beverage industry(1.5). Elasticity of substitution (CES) between factors of produc-tion is 0.25 for all activities. Elasticity of substitution betweenaggregate factors and intermediate input is 0.5 and elasticity ofoutput aggregation for commodities is 3. Furthermore, householdconsumption is modelled under the Linear Expenditure System

(footnote continued)

changes resulted from the CGE are essentially the result of simultaneous interac-


(LES), whereby the elasticities vary between commodities, andthe elasticity is less than 1 for food products and more than 1 forindustrial products and services.

6.2.2. Microsimulation

Reducing fuel subsidies and reallocating the budget willinfluence household welfare through changes in the price ofdomestic commodities and in factor incomes. The microsimula-tion procedure basically translates how price changes (factorincomes) from the CGE can influence household welfare. Thisresearch modified Chen and Ravallion’s work (2004) to calculatethe monetary value of household welfare changes in response tochanges in prices and factor incomes. The modification simplifiedthe first part of Chen and Ravallion’s formula by equating theprice of supply and demand and dropping quantities of commod-ities used as production inputs. It also includes the new elementof non-labour income change. Increasing prices would reduce ahousehold’s ability to afford an initial bundle of consumption,while increasing factor incomes would increase householdincomes. An increase in income means an increase in a house-hold’s ability to consume more. The formula for householdwelfare change is shown below

DWi ¼�Xmj ¼ 1

pjðqij�sijÞdpj

pj

þXn

k ¼ 1

wkLikdwk

wk

� �þ rlKil

drl

rl

� �ð1Þ

where

DWi is the welfare change of the household-i, i: 1,2,3,y,64,407;qij is the quantity of product-j consumed by the household-i,j¼1,2,3,y,26; product-j refers to classification in theCGE model;sij is the quantity of product-j provided/supplied by house-hold-i;ðqij�sijÞ is the net consumption of product-j that must bebought by household-i. According to the SUSENAS dataset,the value of household consumption is always larger than orequal to the value of household productionðqijZsijÞ;pj is the price of product-j;dpj is the price change of product-j;Lik is the labour supply of household-i in sector-k, k=1,2,...,8;sector-k refers to a labour category in the CGE model;wk is the wage in sector-k;dwk is the wage change in sector-k;Kil is the non-labour endowment of household-i;rl is the rate of return;drl is the change in the rate of return.

The change in household welfare is the sum of the change inhousehold expenditure and household income. The negative signin the first part of the formula indicates that increasing prices willincrease household expenditure and, consequently, lower house-hold welfare. Conversely, the positive signs of the last two parts ofthe formula indicate that increasing wages and the non-labourrate of return will increase household income, thus increasinghousehold welfare. This study assumes that the consumptionpattern of households does not change following price changesto eliminate the double accounting in calculating of householdwelfare changes.6

6 The CGE model has accommodated the substitution of consumption beha-

viour for each representative group represented by elasticities in Linear Expendi-

ture System. It has also considered the substitution between primary inputs and

intermediate inputs represented by elastiticies in the CES function and in the

aggregation between factors and intermediate inputs. Price and factor income

The model also assumes that the change of household welfarewill directly influence household consumption (expenditure) andthere is no saving activity, i.e. households are not allowed to savethe net welfare. The new expenditure function is shown below

Eiððp0jþdpjÞ,ðy0iþDWiÞÞ ¼ E0iðp0j,y0iÞþDWi ð2Þ

where

Eiððp0jþdpjÞ,ðy0iþDWiÞÞ is household-i’s expenditure aftersimulations of pashing out fuel subsidies and reallocatingpolicies. It is used to calculate the new poverty incidence;E0iðp0j,y0iÞ is initial household-i’s expenditure;p0j is the initial vector price;y0i is the initial endowment/income of household-i.

6.2.3. Endogenous poverty line and poverty calculation

Increasing commodity prices as a consequence of deregulationin fuel prices will also increase the money metric of obtaining2100 calories and also non-food components. Therefore, thepoverty line will become endogenous following a variation inrelative prices (Decaluwe et al. (2005); Dartanto and Usman,2011; Dartanto, 2013). Hence, the initial food poverty line shouldbe adjusted with the price change of food products in proportionto the share of those products in the poverty line; it should also beadjusted with the price change of non-food products. Therefore,the new poverty line that changes following a variation in prices(known as the endogenous poverty line) can be calculated as

zpr ¼ PLpr ¼ FPL0pr 1þDFPpr

FP0pr

� �þNFPL0pr 1þ

DNFPpr

NFP0pr

� �ð3Þ

where

zpr ¼ PLpr is the poverty line in province-p, p¼1,y,30, atregion-r, r¼urban and rural;FPL0pr is the initial food poverty line in province-p at region-r;DFPpr is the change in composite food price in province-p atregion-r;FP0pr is the initial composite food price in province-p at region-r;NFPL0pr is the initial non-food poverty line in province-p atregion-r;DNFPpr is the change in composite non-food price in province-p at region-r; and NFP0pr is the initial composite non-foodprice in province-p at region-r.

The aggregate value of the food poverty line (PFL) and the non-food poverty line (NFPL) for each province at the rural and urbanlevel is published only once a year by BPS. The BPS poverty linevaries across provinces and between rural and urban areas, e.g., inthe case of Central Java, the urban poverty line is IDR 143,776(USD 14.4) while the rural poverty line is IDR 120,115 (USD 12).

In order to calculate poverty, this study applies the FGT (Fosteret al., 1984) formula. The modified formula is shown below

HCa ¼1

n

Xq

i ¼ 1

PLpr�Eipr

PLpr

� �að4Þ

tions between all economic agents in the CGE model. If the microsimulation model

again considers price elasticities and income elasticies in calculating the change of

households’ welfare, there will be a double accounting in calculating the welfare

change. Most of the studies using only the microsimulation (household survey

data) for analysing the household welfare change of such fuel price reforms

consider income and price elasticities, e.g. referring to the results of Baker et al.

(1989).


where

HCa is the poverty measurement;n is the population number;i is the individual-i;PLpr is the poverty line in province-p at region-r;Eipr is the expenditure of individual-i in province-p at region-r;q is the number of individuals below or at the poverty line;a is the parameter for the FGT.

When a is zero, the poverty measurement is the headcountindex, which represents the percentage of the population belowthe poverty line. The poverty-gap index, PG, which measures thedepth of poverty, is calculated by setting a to 1. The squaredpoverty gap is obtained with a equal to 2.

6.3. Simulation scenarios

The aim of simulations is to ascertain how the level of povertychanges under various scenarios of government fuel subsidies andreallocation budget policies. The base data for the simulations,including subsidy, government consumption and transfer, isdrawn from the 2005 Social Accounting Matrix. The simulationsare performed under several scenarios, which are fundamentallydivided into four categories (Table 4): first, simulating a reductionin fuel subsidies of 25% (SIM1), 50% (SIM2), 75% (SIM3) and 100%(zero subsidies) (SIM4).

The second set of scenarios simulates cut of 25% to fuel subsidiesand the reallocation of all money to government spending andgovernment transfers to households (SIM 5 and SIM 6). Third,government cuts of 50% to fuel subsidies and the reallocation of50% of the money to government spending and governmenttransfers to households is simulated (SIM 7 and SIM 8). The finalset of scenarios simulates government cuts of 100% to fuel subsidiesand the reallocation of 50% of the money to government spending,government transfers to households and government subsidies

Table 4Simulation scenarios (billion USD).

Source: Author.

Description SIM1 SIM2 S

a. Cutting fuel subsidies 25% 50% 7b. Value of fuel subsidies 2.52 5.03 7c. Reallocation of fuel subsidies – – –

1. Government spending (% of c) – – –Education, health and government services – – –Machinery and metal products – – –Constructions and infrastructures – – –Subtotal – – –

2. Government transfers to households (HH) (% of c) – – –Agricultural labour HH – – –Agricultural HH with land o0.5 ha – – –Agricultural HH with 0.5 o land o1 ha – – –Rural non-agr. low income HH – – –Rural non-labour force HH – – –Urban non-agr. low income HH – – –Urban non-labour force HH – – –Subtotal – – –

3. Subsidies (% of c) – – –Agricultural subsidies on food productions – – –Land transportation – – –Water and air transportation – – –Government services: education and health – – –Subtotal – – –

d. Government saving to finance deficit 2.52 5.03 7

(SIM 9 and SIM 10). Under SIM8 and SIM9, the government cansave around 2.52 billion USD and 5.03 billion USD (Table 4). Theseamount of money can be used for either financing the budget deficitor increasing the fiscal space for promoting an economic growthand/or financing more on social expenditures.

This study also performs other simulations: SIM1a, SIM2a, SIM3a,SIM4a, SIM5a, SIM6a, SIM7a, SIM8a, SIM9a and SIM10a. Thesesimulations are essentially the same as the simulations in Table 4,but the main difference is that the price changes derived from theCGE model as a result of the removal of fuel subsidies are marked upby two times. These simulations are conducted to ascertain howlarge the poverty impact of the removal of fuel subsidies would be ifprice changes in the economy were larger than the price changesgenerated by the CGE model. This is because the CGE model doesnot calculate for increases in inflation caused by other factors, suchas the tendency of businesses to shift the burden of fuel price hikesto consumers by exorbitant increases in product prices.

Various simulations are conducted in order to ascertain thesensitivity of poverty in respect to changes in subsidies andreallocation policies. Furthermore, the simulations are conductedunder the following conditions: flexible government saving andfixed direct tax rates, flexible exchange rates and fixed foreignsaving, fixed capital formation, labour fully employed and mobileacross activities, capital fully employed and activity-specific andfixed domestic producer price (price numeraire).

7. The poverty impact of reducing fuel subsidies andreallocation policies

7.1. CGE results of macroeconomic variables

Generally, a decrease in fuel subsidies will be followed by adecrease in macro-economic indicators, such as private consump-tion, imports and gross domestic product (GDP), while otherindicators, net indirect tax and the consumer price index (CPI)will increase (see Appendix A). The simulation results show that a100% decrease in fuel subsidies increases the CPI by 0.77%. An

IM3 SIM4 SIM5 SIM6 SIM7 SIM8 SIM9 SIM10

5% 100% 25% 25% 50% 50% 100% 100%

.55 10.07 2.52 2.52 5.03 5.03 10.07 10.07

– 2.52 2.52 2.52 2.52 5.03 5.03

– 60% 80% 60% 80% 60% 70%

– 0.75 1.01 0.75 1.01 1.51 1.76

– 0.23 0.30 0.23 0.30 0.45 0.53

– 0.53 0.70 0.53 0.70 1.06 1.23

– 1.51 2.01 1.51 2.01 3.02 3.52

– 40% 20% 40% 20% 37% 28%

– 0.28 0.14 0.28 0.14 0.52 0.39

– 0.23 0.11 0.23 0.11 0.42 0.32

– 0.05 0.03 0.05 0.03 0.09 0.07

– 0.20 0.10 0.20 0.10 0.37 0.28

– 0.07 0.03 0.07 0.03 0.12 0.09

– 0.14 0.07 0.14 0.07 0.26 0.20

– 0.04 0.02 0.04 0.02 0.08 0.06

– 1.01 0.50 1.01 0.50 1.86 1.41

– – – – – 3% 2%

– – – – – 0.05 0.03

– – – – – 0.04 0.03

– – – – – 0.03 0.02

– – – – – 0.04 0.03

– – – – – 0.15 0.10

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increase in CPI depletes household welfare, which ultimatelydecreases household (private) consumption as well as GDP.Moreover, a 100% decrease in fuel subsidies leads to a decline inthe domestic supply of fuel and chemical products of 1.1%.Theoretically, a decrease in fuel subsidies increases the price offuels and other products that use fuels as production inputs,reducing the demand for those goods and signalling domesticproducers to lower their production.

Turning to changes in consumer prices and factor incomes, theCGE simulation shows that a decrease in fuel subsidies of 100%increases the domestic consumer price of fuel and chemicalproducts by 5.8% (see Appendix B). An increase in the domesticconsumer price of fuel and chemical products will directlyincrease the price of other products and services, such astransportation, electricity and industrial products, which utilisefuels as a production input. This figure seems very small com-pared to real price increases in the economy. There are two mainreasons: first, fuel prices had already been adjusted two times by148% in 2005; second, this CGE model does not capture the mark-up pricing behaviour of economic agents. The mark-up pricingbehaviour is defined as charging a mark-up price over themarginal costs for seeking additional benefits.

Ikhsan et al. (2005) found that, responding to the adjustmentof fuel prices in Indonesia, economic agents usually adjusted theprice more than necessary. One example of this was the demandfrom public transportation drivers and Organda (the Associationof Public Ground Transportations) to increase fares by 30% torespond to the 29% increase in fuel prices in March 2005.Transportation fares are made up not only of operational costsbut also of large capital costs. Fuels accounted for an average of13% of land transportation costs in Indonesia at the end of 2001.After fuel price hikes in 2002, it was estimated that fuel expen-diture did not exceed 20% of total production costs. Thus, theproper fare increase should have only been 5.8% (29%�20%).

Furthermore, a decrease in fuel subsidies is disadvantageous toall labour categories except agricultural labour (see Appendix C).The nominal wage rate of agricultural labour rises approximately0.47% when fuel subsidies are cut by 100% but the wage rate ofother labour categories declines by between 0.28% and 2.97%.The reason for this is that an increase in fuel prices and otherproducts reduces the demand for those products and gives a pricesignal to domestic producers to decrease the production of goodsand services, decreasing the demand for non-agricultural labourand lowering up the wages of non-agricultural labour.

7.2. Poverty impacts of reducing fuel subsidies

In the CGE-microsimulation analysis, the poverty impacts ofreducing fuel subsidies and reallocating the budget to govern-ment spending, government transfers to households and othersubsidies depend solely on how large the effects of these shocksare on changing price levels and factor incomes in the economy.The extent to which price and factor income changes caninfluence the incidence of poverty depends on consumptionpatterns and the income sources of the poor. It also depends onhow sensitive the poverty line is to price changes.

Table 5 summarises the impact of various subsidy regimes onpoverty in Indonesia. Reducing fuel subsidies theoretically inten-sifies poverty, since the purchasing power of the poor decreasesdue to increases in the price of fuel products and other productsusing fuels as production inputs. Decreases in fuel subsidies of25%, 50%, 75% and 100% increase the headcount index by 0.259,0.392, 0.67 and 1.057, respectively (in percentage points). How-ever, economic agents usually mark-up product prices to shift theburden of fuel price hikes to consumers; they sometimes seek togain by exorbitant increases in product prices. Doubling


consumer prices generated from the CGE model largely increasesthe poverty incidence by 0.476, 0.723, 1.338 and 2.341 (inpercentage points). These figures equal 997 thousand, 1514thousand, 2800 thousand and 4900 thousand people in terms ofIndonesia’s population.

At the disaggregate level, all household categories suffer fromthe removal of fuel subsidies to some degree. Households that areworking in the electricity, water, gas and construction sectorssuffer the most from the removal of fuel subsidies. If the subsidydecreases by 100%, the headcount index rises by 1.325 percentagepoints. In the case of mark-up pricing, the poverty incidence ofthis category rises by 3.231 percentage points. The second largestadverse impact of removing subsidies is observed in householdsworking in the industrial sectors. If the subsidy decreases by100%, the headcount index rises by 1.255 percentage points(3.098 percentage points in the case of mark-up pricing). Mosthouseholds in both groups, particularly sub-groups working inconstruction and industry, are essentially low income groupscharacterised as living in urban areas, unskilled and semi-skilledlabour. Hence, an adjustment in fuel prices adversely affects thesegroups in terms of both expenditure and income. This is due to asudden increase in the domestic price of fuel and related productsto an unaffordable level and also to a decline in the wages of non-agricultural labour categories.

In terms of absolute numbers, poverty increases are morefrequently observed in households working in the agriculturalsectors. In Indonesia, the 100% removal of fuel subsidies increasesthe number of poor in the agricultural household category (withand without land holdings) by 833,127 people (1,802,085 peoplein the case of mark-up pricing). Moreover, even though agricul-tural households benefit through a gradual increase in labourwages, this can only partially compensate for the household’sincrease in expenditure as a result of price increases. Therefore,decreases in fuel subsidies hurt agricultural households ratherthan benefit them.

Table 6 shows the poverty gap index that represents the gapbetween poor people’s standard of living and the poverty line,showing the shortfall in the poor’s expenditure from the povertyline expressed as an average of the population of Indonesia. It canbe interpreted as how far the poor are below the poverty line. Thepattern of change in the poverty gap index in responding todecreases in fuel subsidies is no different from the changes in theheadcount index. The lower the fuel subsidies, the wider the povertygap index. Cutting fuel subsidies of 25%, 50%, 75% and 100%increases the poverty gap index by 0.053, 0.086, 0.157 and 0.255,respectively (in percentage points). This is because the negativeimpact of domestic price decreases the expenditure (welfare) of lowincome households that were previously above the poverty line so

Table 6Simulated changes in the poverty gap index (percentage point change) of Indonesia un


Sector Population Initigap

Agriculture (with land) 57,332,312 4.71

Agriculture (without land) 20,448,294 5.52

Industry 19,916,155 2.10

Electricity, water, gas and constructions 14,312,875 3.01

Trade, hotel, restaurant, transportation and telecommunication 47,234,503 2.01

Banking, financial int., government and private services 26,863,587 1.36

Others 23,201,581 3.40

Total 209,309,307 3.24

they drop below the poverty line; the expenditure of the poor thatwere already below the line falls further away from the poverty line.The poverty gap index worsens when the economic agents increaseprices disproportionately.

7.3. Poverty impacts of reallocation fuel subsidies

Tables 7 and 8 show changes to the headcount Index and thepoverty gap index under various budget reallocation schemes.Simulation 5 (SIM5), cutting 25% of fuel subsidies and reallocatingit to government spending (60%) and government transfers tohouseholds (40%), can perfectly absorb the adverse effects ofreducing fuel subsidies and the number of poor decreases by565,770 people (0.27 percentage points). Increases in governmentspending on health, education, infrastructures and machinery/metal products generate job opportunities and gradually increasethe factor incomes of unskilled, semi-skilled and skilled non-agricultural labours. A gradual increase in wage rates over-compensates for the increase in expenditure as a result of priceincreases. Thus, the 100% reallocation of the cut 25% fuel subsidiesbenefits the poor.

Moreover, if the budget reallocation composition is changed to80% for government expenditures and 20% for governmenttransfers, the poverty incidence largely decreases by 1,118,120people (0.534 percentage points) (SIM6). This is because a largergovernment transfer to households, particularly to low incomegroups, increases the demand for food and processed foodcommodities and increases the prices of these products. Thisprice increase reduces the welfare of households, particularlythose of low income groups that spend a large proportion of theirbudget on food. Moreover, the impact of reallocating fuel sub-sidies on reducing poverty will become smaller if economicagents extensively mark-up price products over the increasedproduction costs in response to a reduction in fuel subsidies.SIM5a and SIM6a show that, even though in the mark-upcondition a full reallocation of the 25% cut in fuel subsidies stillreduces poverty, the number of poor decreases only by 114,901(SIM5a) and 614,962 (SIM6a)

SIM7 shows that if the government cuts 50% of fuel subsidies(USD 5.03 billion) and reallocates 50% of the money (USD 2.52billion) to government expenditures (60%) and governmenttransfers to households (40%), the number of poor still decreasesby 290,281. In addition, shifting government transfers to govern-ment expenditures improves the effectiveness of budget realloca-tion in terms of reducing poverty, as shown by SIM8: the povertyincidence decreases by 857,412. As shown in the results of SIM5aand SIM6a, the mark-up in prices performed by economic agentsin order to seek gains reduces the effectiveness of budget

der various fuel subsidy systems.

al povertyindex 2005

Cutting fuel subsidies Cutting fuel subsidies(mark-up pricing (doubles thanthe CGE’s result))

SIM1 SIM2 SIM3 SIM4 SIM1a SIM2a SIM3a SIM4a

0.048 0.068 0.140 0.252 0.100 0.139 0.286 0.520

0.060 0.090 0.170 0.300 0.120 0.180 0.350 0.610

0.070 0.120 0.200 0.300 0.130 0.250 0.420 0.660

0.108 0.207 0.335 0.501 0.216 0.423 0.698 1.071

0.039 0.066 0.122 0.194 0.086 0.141 0.250 0.415

0.030 0.060 0.109 0.158 0.070 0.129 0.227 0.352

0.062 0.096 0.169 0.271 0.116 0.192 0.349 0.585

0.053 0.086 0.157 0.255 0.107 0.178 0.325 0.542

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reallocation policies in reducing poverty in Indonesia. Under themark-up condition, SIM7a and SIM8a are only able to reduce thenumber of poor by 101,511 and 164,797, respectively.

The 100% removal of fuel subsidies and reallocating 50%of the money saved to government expenditures, transfersand other subsidies does not have adverse impacts on house-hold welfare. The poverty incidence even slightly decreases by0.071 percentage points (SIM9) and 0.277 percentage points(SIM10). However, policy makers should carefully interpretthese results since it is assumed that all economic agents arepredictable and do not increase the price of products largerthan the increases in production costs. This is necessary tocarry out credible price surveillances when the governmentimplements policies that influence general price levels. Withprice surveillance, the government can control the price toavoid unnecessary inflation and the public can be protectedfrom undue margins.

On the other hand, as is the case in most developingcountries, it is difficult to guarantee that the government hasa credible price surveillance system that can be usedto determine how much prices should be increased inresponse to removing fuel subsidies. It is also necessaryto have a strong institution to control and supervise thebehaviour of economic agents that, by their nature, always tryto seek benefits. If economic agents mark-up the price, a 100%removal of fuel subsidies and a 50% reallocation of them togovernment expenditures, transfers and other subsidies willincrease the number of poor by 2,222,013 (SIM9a) and1,640,115 (SIM10a). Thus, controlling inflation should be atop national concern. Easterly and Fischer (2001), observingmany countries’ experiences, found that the poor suffer morefrom inflation than the rich since high inflation tends to lowerthe income share and the real minimum wage of the bottomquintile; the lowering of these two elements tends to increasepoverty

At the disaggregate level, all household categories benefitfrom the reallocation of fuel subsidies into government expen-ditures and transfers, as shown by SIM5, SIM6 and SIM8.Landless agricultural households benefit the most from theremoval of 25% of fuel subsidies and full reallocation. Theheadcount index of this group decreases by 0.349 percentagepoints. (SIM5) and 0.751 percentage points (SIM6). Governmentspending, particularly on infrastructure, increases the demandfor unskilled labour, while government transfers to low incomegroups increases the demand for agricultural products, pushing upthe wage rate for agricultural labour. Both increases raise theincomes of landless agricultural households. However, agriculturalhouseholds with land will benefit most under SIM8, while house-holds working in the banking/financial sector and in governmentservices will benefit most under SIM7. On the other hand, house-holds working in the industrial and utility sectors are worst offunder SIM7 and SIM9.

Households working in the utility and construction sector andthe industrial sector suffer most under the mark-up pricing condi-tion (SIM9a). The poverty incidence of these groups rises by 1.764percentage points and 1.481 percentage points. Compensationpolicies on government transfers and government spending do notsufficiently cancel out the adverse impacts of reducing fuel sub-sidies. Most households in both groups, particularly sub-groupsworking in construction and industry, are low income groupscharacterised as living in urban areas performing unskilled andsemi-skilled labour. Most of them, particularly those working in theconstruction sector, are cyclical migrant workers from rural areasand they are not registered as urban residents. Their unregisteredstatus means that they are excluded from cash transfers fromgovernment assistance.

Table 8Simulated changes in the poverty gap index (percentage point change) under various budget Reallocations.


Sector Population Initialpoverty gapindex 2005

25% cutsubsidies andreallocated100%

50% cutsubsidies andreallocated 50%





SIM5 SIM6 SIM7 SIM8 SIM9 SIM10 SIM5a SIM6a SIM7a SIM8a SIM9a SIM10a

Agriculture (with land) 57,332,312 4.71 �0.072 �0.149 �0.053 �0.132 �0.043 �0.107 �0.025 �0.083 �0.065 �0.028 0.221 0.139

Agriculture (without

land)20,448,294 5.52 �0.070 �0.150 �0.040 �0.120 �0.020 �0.080 �0.010 �0.070 �0.030 �0.040 0.300 0.220

Industry 19,916,155 2.10 �0.040 �0.100 0.010 �0.050 0.040 0.000 0.020 �0.030 0.070 0.010 0.360 0.290

Electricity, water, gas and

constructions14,312,875 3.01 �0.069 �0.157 0.020 �0.069 0.079 0.010 0.039 �0.040 0.137 0.021 0.609 0.520

Trade, hotel, restaurant,

transportation and

telecommunication

47,234,503 2.01 �0.033 �0.079 �0.007 �0.053 0.016 �0.020 0.003 �0.030 0.013 0.000 0.214 0.168

Banking, financial int.,

government and

private services

26,863,587 1.36 �0.072 �0.111 �0.052 �0.090 �0.068 �0.106 �0.042 �0.081 �0.039 �0.042 0.090 0.043

Others 23,201,581 3.40 �0.051 �0.112 �0.018 �0.080 0.003 �0.047 0.001 �0.050 0.007 �0.010 0.286 0.216

Total 209,309,307 3.24 �0.058 �0.120 �0.026 �0.090 �0.009 �0.060 �0.008 �0.058 �0.006 �0.016 0.257 0.190

Table 9Simulated changes in the headcount index (percentage point change) under varying armington elasticities of substitution in fuel and chemical products.

Source: Author’s estimation.

Sector Population Initial headcount index 2005 Armington elasticity

0.75 1.50 2.50

Agriculture (with land) 57,332,312 23.81 0.272 0.291 0.501

Agriculture (without land) 20,448,294 25.73 0.338 0.346 0.579

Industry 19,916,155 11.25 0.578 0.578 0.638

Electricity, water, gas and constructions 14,312,875 17.66 0.808 0.808 0.853

Trade, hotel, restaurant, transportation and telecommunication 47,234,503 10.81 0.290 0.317 0.362

Banking, financial int., government and private services 26,863,587 6.94 0.408 0.408 0.446

Others 23,201,581 15.81 0.400 0.400 0.534

Total 209,309,307 16.40 0.380 0.392 0.511Number of poor 34,320,060 795,270 820,638 1,069,123


8. Sensitivity analysis

CGE estimation results are known to be sensitive to the valuesof Armington elasticities—the degree of substitutability amongimports, exports and domestically supplied goods. The Armingtonelasticity/structure is introduced to accommodate ‘cross-hauling’,a phenomenon commonly observed in bilateral trade statistics,when a country appears simultaneously to export and import thesame goods. However, there have been few empirical studies onestimating these elasticities. Many studies show that the resultingestimates of these elasticities vary widely. McDaniel and Balistreri(2003) confirmed that the wide-ranging estimates of Armingtonelasticities depends on the data used, the disaggregating sectorand the methodology applied.

Table 9 shows that the impact of a 50% decrease in fuelsubsidies is slightly sensitive to the variation of Armington elasti-city. An increase (or decrease) in the Armington elasticity will befollowed by an increase (or decrease) in the poverty incidence. Atthe national level, when fuel subsidies are reduced by 50%,changing the elasticity from 1.5 to 2.5 will increase the headcountindex from 0.392 percentage points to 0.511 percentage points,which is equivalent to an increase from 820,638 poor people to1,069,123. Conversely, changing the elasticity from 1.5 to 0.75 willdecrease the number of people in poverty from 820,638 to 795,270.The crucial question is: what is the appropriate Armington elasti-city for the substitution of fuel and chemical products? In order toprecisely estimate the poverty impact of removing fuel subsidies,

the elasticities used in the CGE model should also be preciselyestimated.

9. Concluding remarks

There is an urgent need for phasing out fuel subsidies inIndonesia due to a worsening of income distribution and a severebudget deficit as the costs of fuel subsidies rise. The causes of thisrise include declining oil production, a high crude oil price, and amassive increase in fuel consumption led by the rapid growth ofthe middle class, and reluctance by the government to reform theretail fuel prices. The success of gradual deregulation of industrialfuel prices during 2001–2003 should inspire the ruling govern-ment to adopt a non-populist policy of phasing out fuel subsidiesto achieve long run fiscal sustainability, to promote efficient offuel consumption and to improve income distribution.

A gradual reform of phasing out fuel subsidies is needed toprepare households for the situation when international fuelprices have 100% pass-through into the domestic market sinceIndonesian oil reserves are predicted to only last another 15–20years. It is also needed to increase the fiscal space for promotingeconomic growth as well as for providing more targeted andbetter social protection. Fuel subsidies make up on average 18% oftotal government spending, while since 2004 the share of devel-opment expenditure to total spending in Indonesia has beenlower than the share of fuel and energy subsidies. Fuel subsidies,

Table A1Simulated macroeconomic indicators and domestic production changes (%) under various simulations of fuel subsidy systems and reallocation policies.

Source: CGE simulation results.

Description Initial value SIM1 SIM2 SIM3 SIM4 SIM1a SIM2a SIM3a SIM4a SIM5 SIM6 SIM7 SIM8 SIM9 SIM10 SIM5a SIM6a SIM7a SIM8a SIM9a SIM10a

Selected macroeconomic indicators (real value)Private consumption 23,848.9 �0.02 �0.04 �0.06 �0.09 �0.04 �0.08 �0.13 �0.18 �0.60 �0.79 �0.62 �0.82 �1.26 �1.46 �0.64 �0.83 �0.70 �0.90 �1.45 �1.64

Exports 10,011.0 �0.04 �0.08 �0.12 �0.17 �0.08 �0.16 �0.25 �0.35 �0.14 �0.18 �0.18 �0.22 �0.40 �0.43 �0.22 �0.26 �0.34 �0.38 �0.74 �0.78

Imports �9191.8 �0.04 �0.09 �0.14 �0.19 �0.08 �0.17 �0.27 �0.38 �0.16 �0.20 �0.20 �0.24 �0.43 �0.47 �0.24 �0.28 �0.38 �0.42 �0.81 �0.85

Net income tax 780.8 0.30 0.62 0.95 1.30 0.61 1.24 1.91 2.61 0.16 0.06 0.48 0.38 1.00 0.90 0.77 0.67 1.73 1.63 3.60 3.50

Gross domestic product 31,502.8 �0.02 �0.05 �0.07 �0.10 �0.04 �0.09 �0.14 �0.20 �0.03 �0.03 �0.05 �0.05 �0.12 �0.13 �0.07 �0.07 �0.14 �0.15 �0.32 �0.33

Consumer price index (CPI) 120.0 0.16 0.24 0.46 0.77 0.33 0.48 0.92 1.54 0.06 0.01 0.12 0.08 0.54 0.46 0.22 0.17 0.37 0.32 1.31 1.23

Selected domestic output growthFood croops 2231.6 0.01 0.02 0.03 0.04 0.02 0.03 0.05 0.07 0.01 0.01 0.02 0.01 0.06 0.05 0.03 0.02 0.05 0.05 0.13 0.12

Livestock 768.5 0.02 0.04 0.05 0.07 0.04 0.07 0.11 0.15 �0.11 �0.15 �0.10 �0.13 �0.19 �0.29 �0.08 �0.12 �0.02 �0.06 �0.04 �0.14

Forestry 270.9 0.02 0.04 0.06 0.08 0.04 0.08 0.12 0.16 0.18 0.27 0.20 0.29 0.39 0.48 0.22 0.30 0.28 0.36 0.55 0.64

Fishery 748.9 0.01 0.02 0.03 0.04 0.02 0.04 0.07 0.09 �0.08 �0.11 �0.07 �0.10 �0.16 �0.18 ��0.06 �0.09 �0.03 �0.06 �0.07 �0.10

Oil and metal mining 1497.3 �0.11 �0.22 �0.42 �0.53 �0.22 �0.43 �0.85 �1.06 �0.19 �0.22 �0.30 �0.33 �0.70 �0.71 �0.41 �0.43 �0.73 �0.76 �1.77 �1.77

Other mining and quarrying 363.7 �0.02 �0.04 �0.07 �0.09 �0.04 �0.08 �0.13 �0.18 0.54 0.73 0.52 0.71 1.03 1.22 0.51 0.69 0.44 0.63 0.85 1.03

Rice 1330.6 0.01 0.03 0.04 0.06 0.03 0.06 0.09 0.12 �0.05 �0.08 �0.03 �0.06 �0.07 �0.10 �0.02 �0.05 0.02 0.00 0.05 0.02

Food and beverage industry 3493.4 0.04 0.08 0.12 0.17 0.08 0.16 0.25 0.33 �0.15 �0.17 �0.11 �0.13 �0.23 �0.34 �0.07 �0.09 0.06 0.04 0.10 �0.01

Textile-clothes-leather Industry 1424.9 0.02 0.04 0.06 0.08 0.04 0.08 0.12 0.15 �0.20 �0.32 �0.18 �0.30 �0.49 �0.50 �0.16 �0.28 �0.10 �0.22 �0.34 �0.35

Wood processing industry 415.4 0.04 0.07 0.11 0.15 0.07 0.15 0.22 0.29 0.05 0.12 0.09 0.16 0.12 0.22 0.12 0.19 0.23 0.30 0.42 0.51

Pulp-paper and metal industry 5097.4 0.02 0.04 0.06 0.09 0.04 0.09 0.13 0.17 0.01 �0.01 0.03 0.02 0.09 0.08 0.05 0.04 0.12 0.10 0.26 0.26

Fuel and chemical industry 3734.6 �0.26 �0.50 �0.80 �1.10 �0.53 �1.00 �1.60 �2.20 �0.38 �0.41 �0.61 �0.64 �1.29 �1.40 �0.90 �0.93 �1.61 �1.64 �3.49 �3.60

Electricity–gas–water 921.9 �0.02 �0.04 �0.07 �0.09 �0.04 �0.09 �0.13 �0.19 �0.26 �0.26 �0.28 �0.28 �0.58 �0.68 �0.30 �0.30 �0.37 �0.37 �0.77 �0.86

Construction 5587.4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.90 1.20 0.90 1.20 1.79 2.09 0.90 1.20 0.90 1.20 1.79 2.09

Restaurant 2487.4 0.00 �0.01 �0.02 �0.01 �0.01 �0.02 �0.04 �0.02 �0.40 �0.39 �0.40 �0.40 �0.74 �0.81 �0.41 �0.40 �0.42 -0.42 �0.76 �0.83

Ground transportation 1089.4 �0.01 �0.03 �0.01 �0.10 �0.02 �0.05 �0.03 �0.20 �0.57 �0.84 �0.59 �0.86 �1.14 �1.41 �0.59 �0.87 �0.64 �0.91 �1.34 �1.61

Financial services 1866.8 0.01 0.01 0.02 0.02 0.01 0.02 0.03 0.04 �0.13 �0.13 �0.13 �0.13 �0.26 �0.26 �0.12 �0.12 �0.11 �0.11 �0.22 �0.22

Government and private services 3400.3 0.03 0.07 0.10 0.14 0.07 0.14 0.21 0.28 0.55 0.83 0.58 0.86 1.27 1.35 0.62 0.89 0.72 1.00 1.55 1.63

T.

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Table B1Simulated price changes (%) under various simulation of fuel subsidy systems and reallocation policies.

Source: CGE simulation result.

Commodity SIM1 SIM2 SIM3 SIM4 SIM1a SIM2a SIM3a SIM4a SIM5 SIM6 SIM7 SIM8 SIM9 SIM10 SIM5a SIM6a SIM7a SIM8a SIM9a SIM10a

Food croops 0.16 0.12 0.30 0.60 0.32 0.24 0.60 1.20 �0.42 �0.69 �0.46 �0.74 �0.72 �1.07 �0.26 �0.53 �0.34 -0.62 -0.12 -0.47

Soybeans 0.04 0.10 0.20 0.50 0.09 0.20 0.40 1.00 �0.12 �0.27 �0.06 �0.22 �0.15 �0.03 �0.08 �0.23 0.04 �0.12 0.65 0.47

Other croops 0.08 0.16 0.35 0.55 0.17 0.32 0.69 1.10 �0.42 �0.57 �0.34 �0.49 �0.33 �0.75 �0.34 �0.49 �0.18 �0.33 0.21 �0.20

Livestock 0.05 0.10 0.20 0.40 0.10 0.20 0.40 0.80 �0.57 �0.84 �0.52 �0.79 �0.74 �1.26 �0.52 �0.79 �0.42 �0.69 �0.34 �0.86

Forestry 0.15 0.20 0.30 0.60 0.30 0.40 0.60 1.20 0.65 0.78 0.70 0.84 1.59 1.69 0.80 0.93 0.90 1.04 2.19 2.29

Fishery 0.13 0.25 0.48 0.70 0.25 0.50 0.95 1.40 �1.11 �0.70 �0.99 �1.57 �1.76 �2.44 �0.99 �1.57 �0.74 �1.32 �1.06 �1.74

Oil and metal mining �0.20 �0.50 �0.70 �0.90 �0.40 �1.00 �1.40 �1.80 �0.42 �0.61 �0.72 �0.91 �1.53 �1.58 �0.62 �0.81 �1.22 �1.41 �2.43 �2.48

Other mining and quarrying �0.30 �0.60 �1.00 �1.10 �0.60 �1.20 �2.00 �2.20 1.01 1.49 0.71 1.19 1.69 2.12 0.71 1.19 0.11 0.59 0.59 1.02

Rice 0.12 0.13 0.35 0.59 0.24 0.27 0.71 1.18 �0.68 �1.28 �0.66 �1.26 �1.25 �1.69 �0.56 �1.16 �0.53 �1.13 �0.67 �1.10

Food and beverage industry 0.04 0.00 0.10 0.40 0.07 0.00 0.20 0.80 �0.28 �0.53 �0.32 �0.57 �0.31 �0.58 �0.25 �0.49 �0.32 �0.57 0.09 �0.18

Textile�clothes� leather industry 0.03 0.10 0.20 0.30 0.07 0.20 0.40 0.60 �0.22 �0.43 �0.15 �0.36 �0.37 �0.51 �0.19 �0.40 �0.05 �0.26 �0.07 �0.21

Wood processing industry 0.00 �0.10 �0.01 0.00 0.00 �0.21 �0.02 0.00 0.52 0.58 0.41 0.47 0.87 1.00 0.52 0.58 0.31 0.37 0.87 1.00

Pulp�paper and metal industry 0.04 0.00 0.10 0.30 0.08 0.00 0.20 0.60 0.15 0.15 0.11 0.11 0.41 0.39 0.19 0.19 0.11 0.11 0.71 0.69

Fuel and chemical industry 1.40 2.70 4.20 5.80 2.80 5.40 8.40 11.60 1.21 1.19 2.51 2.49 5.44 5.31 2.61 2.59 5.21 5.19 11.24 11.11

Electricity–gas–water 0.16 0.10 0.30 0.60 0.33 0.20 0.60 1.20 �0.56 �0.74 �0.62 �0.81 �0.89 �1.11 �0.40 �0.58 �0.52 �0.71 �0.29 �0.51

Construction 0.09 0.10 0.25 0.54 0.19 0.20 0.49 1.08 1.75 2.36 1.76 2.36 4.01 4.67 1.85 2.45 1.86 2.46 4.55 5.21

Trade �1.90 �1.00 �4.90 �10.40 �3.80 �2.00 �9.80 �20.80 �3.10 �4.26 �2.20 �3.36 �13.75 7.99 �5.00 �6.16 �3.20 �4.36 �24.15 �2.41

Restaurant 0.03 �0.04 0.05 0.18 0.07 �0.08 0.09 0.36 �0.95 �1.12 �1.02 �1.20 �1.78 �2.02 �0.91 �1.09 �1.06 �1.24 �1.60 �1.84

Hotel 0.05 0.01 0.10 0.30 0.11 0.01 0.20 0.60 �0.48 �0.54 �0.53 �0.59 �0.75 �0.76 �0.43 �0.49 �0.52 �0.58 �0.45 �0.46

Ground transportation 0.19 0.40 0.64 0.93 0.39 0.80 1.28 1.86 0.29 0.38 0.50 0.59 0.92 1.08 0.48 0.58 0.90 0.99 1.85 2.01

Air–water transp. and telecommunication 0.06 0.02 0.14 0.30 0.12 0.04 0.28 0.60 �0.59 �0.68 �0.64 �0.72 �1.09 �1.17 �0.53 �0.62 �0.62 �0.70 �0.79 �0.87

Warehousing �0.04 �0.18 �0.11 �0.11 �0.07 �0.36 �0.22 �0.21 �0.06 �0.17 �0.09 0.03 0.23 0.18 0.02 0.13 �0.27 �0.15 0.12 0.08

Financial services 0.04 0.00 0.11 0.18 0.08 0.00 0.22 0.35 �1.19 �1.36 �1.23 �1.40 �2.30 �2.54 �1.14 �1.31 �1.23 �1.40 �2.12 �2.37

Real estate 0.04 �0.01 0.06 0.26 0.09 �0.03 0.12 0.52 �0.49 �0.67 �0.55 �0.73 �0.96 �1.08 �0.44 �0.63 �0.56 �0.75 �0.70 �0.82

Government and private services 0.03 �0.04 0.04 0.17 0.07 �0.09 0.08 0.35 3.17 4.30 3.09 4.22 6.84 7.91 3.20 4.33 3.05 4.18 7.01 8.08

Individual services 0.04 �0.01 0.04 0.22 0.09 �0.01 0.08 0.44 �0.40 �0.38 �0.46 �0.43 �0.57 �0.58 �0.36 �0.34 �0.46 �0.44 �0.34 �0.36

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mostly enjoyed by middle and upper class, also consumed anaverage of 63.8% of the total subsidies during 1998–2013. Trans-ferring fuel subsidies from middle income to poor householdswould improve income distribution and accelerate more equaleconomic growth. Furthermore, if the government had beensuccessful in cutting fuel subsidies by 51% in 2011, there wouldhave been no budget deficit in that year.

A promising strategy for reforming the fuel subsidies combinesgradual fuel subsidy reduction with compensation and realloca-tion policies. The CGE microsimulation results show that reducingfuel subsidies by 25% increases poverty incidence by 0.259percentage points. However, if the saved money is fully allocatedto government spending and transfers, the adverse impact can becancelled out; even the poverty incidence will be reduced by 0.27percentage points. In addition, removing 100% of fuel subsidiesand then reallocating 50% to government expenditures, govern-ment transfers and other subsidies does not have an adverseimpact on household welfare; the poverty incidence even slightlydecreases by 0.071 percentage points (SIM9) and 0.277 percen-tage points (SIM10). However, this reallocation budget might noteffectively compensate for the adverse impacts of the 100%removal of fuel subsidies if the economic agents try to seek gainsthrough mark-up pricing surpassing the increase in productioncosts. Hence, the government should perform price surveillancethat can be used to determine how much prices should beincreased in respond to the removal of fuel subsidies. Moreover,the budget reallocation should focus on government spendingrather than on government transfers due to the effectiveness ofthe former in reducing poverty.

Acknowledgements

I would like to thank the University of Indonesia and TheMinistry of National Education for partly financing this researchthrough the National Research Strategic Fund 2012. I would alsolike to thank Mr. Nurkholis, University of Indonesia, for researchassistance, the participants of 22th JASID Conference at NagoyaUniversity for their comments, and two anonymous referees fortheir constructive and valuable comments and suggestions toimprove the quality of this paper. Any remaining errors are myown responsibility.

Appendix A

See Table A1.

Appendix B

See Table B1.

Appendix C

See Table C1.

References

Agustina, C. et al., 2008. Black Hole or Black Gold? The Impact of Oil and Gas Priceson Indonesia’s Public Finances. The World Bank, Policy Research WorkingPaper 4718 (WPS 4718). Available at: /http://econpapers.repec.org/paper/wbkwbrwps/4718.htmS (Accessed 12 April 2011).

Azis, I.J., 2006. A drastic reduction of fuel subsidies confuses ends and means.ASEAN Economic Bulletin 23 (1), 114–136.

Baig, T., Mati, A., Coady, D., Ntamatungiro, J., 2007. Domestic Petroleum ProductPrices and Subsidies: Recent Developments and Reform Strategies. IMF

http://econpapers.repec.org/paper/wbkwbrwps/4718.htm

http://econpapers.repec.org/paper/wbkwbrwps/4718.htm


Working Paper, WP/07/71. Available at: /http://www.imf.org/external/pubs/ft/wp/2007/wp0771.pdfS (Accessed 19 August 2011).

Baker, P., Blundell, R., Micklewright, J., 1989. Modelling household energyexpenditures using micro-data. The Economic Journal 99 (397), 720–738.

Boccafunso, D., Savard, L., 2006. Impacts Analysis of Cotton Subsidies on Poverty:A CGE Macro-Accounting Approach. Universite De Sherbrooke, Faculted‘Administration, Department D‘Economique, Working Paper 06-03, January2006.

Breisinger, C., Engelke, W., Ecker, O., 2012. Leveraging fuel subsidy reform fortransition in Yemen. Sustainability 4, 2862–2887.

Chen, S., Ravallion, M., 2004. Welfare impacts of China’s accession to the worldtrade organization. The World Bank Economic Review 18 (1), 29–57.

Clements, B., Jung, H.S., Gupta, S., 2007. Real and distributive effects of petroleumprice liberalization: the case of Indonesia. The Developing Economies 45 (2),220–237.

Cogneau, D., Robillard, A.S., 2000. Income Distribution, Poverty and Growth inMadagascar: Micro Simulations in a General Equilibrium Framework, IFPRITMD Discussion Paper No. 61.

Dartanto, T., 2010. Volatility of world rice prices, import tariffs and poverty inIndonesia: a CGE microsimulation analysis. Economics and Finance Indonesia58 (3), 335–364.

Dartanto, T., 2013. The application of an endogenous poverty line and itsrelationship with the poverty impact of economic shocks: an empiricalinvestigation. The Singapore Economic Review 58 (1), 1350005 (25 pages).

Dartanto, T., Usman, 2011. Volatility of world soybeans prices, import tariffs andpoverty in Indonesia: a CGE microsimulation analysis. The Journal of AppliedEconomic Research 5 (2), 139–181.

Davis, A., Kang, A., Vincent, J., Washington, D., 2001. How important is improvedwater infrastructure to microenterprises? Evidence from Uganda. WorldDevelopment 29 (10), 1753–1767.

Decaluwe, L.B., Savard, L., Thorbecke, E., 2005. General equilibrium approach forpoverty analysis: with an application to Cameroon. African DevelopmentReview 17 (2), 213–243.

del Granado, J.A., Coady, D., Gillingham, R., 2010. The Unequal Benefits of FuelSubsidies: A Review of Evidence for Developing Countries. IMF Working Paper,WP/10/202.

Dervis, K., de Melo, J., Robinson, S., 1982. General Equilibrium Models forDevelopment Policy: A World Bank Research Publication. Cambridge Univer-sity Press, Cambridge.

Easterly, W., Fischer, S., 2001. Inflation and the poor. Journal of Money, Credit andBanking 33 (2-1), 160–178.

Fan, S., Hazell, P., Thorat, S., 2000. Government spending, growth and poverty inrural India. American Journal of Agricultural Economics 82 (4), 1038–1051.

Foster, J.E., Greer, J., Thorbecke, E., 1984. A class of decomposable povertymeasures. Econometrica 52, 761–776.

Ikhsan, M., Sulistyo, M.H., Dartanto, T., Usman, 2005. Study of the impact ofincreasing fuel price 2005 to poverty. LPEM FEUI, Working Paper 10.

Jung, H., Thorbecke, E., 2003. The impact of public education expenditure onhuman capital, growth, and poverty in Tanzania and Zambia: a generalequilibrium approach. Journal of Policy Modeling 25, 701–705.

Lofgren, H., Harris, R.L., Robinson, S., 2001. A Standard Model of ComputableGeneral Equilibrium (A Standard Computable General Equilibrium (CGE)Model in GAMS). IFPRI-TMD Discussion Paper No. 75 (online), May 2001,Washington, DC. Available at: /http://www.ifpri.org/pubs/microcom/5/mc5.pdfS (Accessed 15 May 2011).

McDaniel, C.A., Balistreri, E.J., 2003. A review of Armington trade substitutionelasticities. Economie Internationale 94–95, 301–304.

OPEC, 2005. Annual Statistical Bulletin 2005. (Online) Available at: /http://www.opec.org/library/annual%20statistical%20bulletin/interactive/2005/filez/fwpage.htmS (Accessed 10 January 2013).

OPEC, 2008. Annual Statistical Bulletin 2005. (Online) Available at: /http://www.opec.org/opec_web/static_files_project/media/downloads/publications/ASB2008.pdfS (Accessed 10 January 2013).

OPEC, 2012. Annual Statistical Bulletin 2005. (Online) Available at: /http://www.opec.org/opec_web/static_files_project/media/downloads/publications/ASB2012.pdfS (Accessed 10 January 2013).

Roberts, J., 2003. Poverty Reduction Outcomes in Education and Health PublicExpenditure and Aid. Working Paper 210. (Online) March 2003. Available at:/http://www.odi.org.uk/resources/download/1776.pdfS (Accessed 31 May 2011).

Savard, L., 2003. Poverty and Income Distribution in a CGE-Household Micro-Simulation Model: Top-Down/Bottom Up Approach. CIRPEE Working PaperNo. 03-43, October 2003. Available at: /http://www.cirpee.org/fileadmin/documents/Cahiers_2003/CIRPEE03-43.pdfS (Accessed 21 August 2011).

Savard, L., 2005. Poverty and inequality analysis within a CGE framework:a comparative analysis of the representative agent and microsimulationapproaches. Development Policy Review 23 (3), 313–331.

Son, H.H., 2008. Conditional Cash Transfer Program: An Effective Tool for PovertyAlleviation? ADB: ERD Policy Brief Series no.51. (Online). Available at: /http://www.adb.org/Documents/EDRC/Policy_Briefs/PB051.pdfS (accessed 24.06.11).

Yusuf, A.A., Resosudarmo, B., 2008. Mitigating distributional impact of fuel pricingreform: the Indonesian experience. ASEAN Economic Bulletin 25 (1), 32–47.

http://www.imf.org/external/pubs/ft/wp/2007/wp0771.pdf

http://www.imf.org/external/pubs/ft/wp/2007/wp0771.pdf

http://www.ifpri.org/pubs/microcom/5/mc5.pdf

http://www.ifpri.org/pubs/microcom/5/mc5.pdf

http://www.opec.org/library/annual&percnt;20statistical&percnt;20bulletin/interactive/2005/filez/fwpage.htm





http://www.opec.org/opec_web/static_files_project/media/downloads/publications/ASB2008.pdf






http://www.odi.org.uk/resources/download/1776.pdf

http://www.cirpee.org/fileadmin/documents/Cahiers_2003/CIRPEE03-43.pdf

http://www.cirpee.org/fileadmin/documents/Cahiers_2003/CIRPEE03-43.pdf

Reducing fuel subsidies and the implication on fiscal balance and poverty in Indonesia: A simulation...

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Transcript of Reducing fuel subsidies and the implication on fiscal balance and poverty in Indonesia: A simulation...