Chapter One
1.1 Background
Fuel subsidies are known as any government action directed primarily at the energy sector that lowers the cost of energy production, raises the price received by energy producers or lowers the price paid by energy consumers.

Fuel subsidies have long been used in many countries, both developed and developing, to encourage the production of goods and services through lowering the cost of production and in certain cases to lighten the burden of rising prices on consumers. As in other countries, the Sudanese government has been subsidizing fuel products where they are sold below the market price. Evidently, fossil fuel subsidy has made a hole in the country’s budget, contributing to the fiscal deficit, which stood at 5.2% of gross domestic product GDP in 2012. (IMF Sudan report 2013).

Many researches have been conducted to provide better understanding about fossil fuel subsidies. Nonetheless, most discussions focused on the cost and benefit of subsidies while some others assessed the impact of subsidy reform. There are several reasons to justify the presence of subsidies. First of all, low price of energy guarantees energy access for the poor. Moreover, by subsidising energy, the price of other commodities will be more affordable to the poor. Secondly, fossil fuel subsidy plays an important role in supporting industrial development and boost investment.

However, there are some critics toward the presence of fossil fuel subsidy. Those who criticize it argue that fossil fuel subsidies bring negative impacts on the economy and environment. Subsidies depress the government budget and decrease the budget available for infrastructure, which will hinder growth in the future.

Recent studies also show that the cost of subsidies outweighs its benefit and that the subsidies are wrongly targeted. The evidence shows that the rich are those who enjoy much of the benefit of subsidies. Conducting research in Indonesia, Agustina et al. (2008) found that most of the subsidies went to the richest 20%. Similarly, Dartanto (2013) found that about 72% of oil subsidies had been enjoyed by the 30% highest income groups in the societies. The International Monetary Fund (IMF) estimates about Sudan found that about 48% of Fuel subsidies had been enjoyed by the 20% highest income groups in the society reflecting the high consumption of subsidized energy among these households.

This situation raises a call for subsidy reform, which aimed to eliminate the presence of fossil fuel subsidies. It was started in the Pittsburgh Summit Commitment in 2009 when some countries agreed to remove subsidies. While some other countries still mitigate the adverse impact of subsidy removal before deciding to phase out the subsidies. Some countries are in doubt to phase out subsidies since its cost and benefit is still unclear.

Sudan is among the countries that are implementing such a policy reform to reduce fuel subsidy gradually. The subsidy cuts were announced as part of a programme designed to deal with the country’s widening fiscal deficit following the secession of South Sudan in 2011.

There are so many issues related to fossil fuel subsidies. However, this research will focus on the impact of fossil fuel subsidies toward economy especially on GDP as a good indicator for economic growth, and the main objective of this research is to investigate the relationship between fossil fuel subsidies and economic growth.

1.2 Statement of the Problem
Fuel subsidy have long been used in many countries, both developed and developing, to encourage the production of goods and services through lowering the cost of production and in certain cases to lighten the burden of rising prices on consumers. Fossil fuel subsidy is critical to the Sudanese economy, whenever the price of fuel goes up, the price of everything goes up. This is because transport cost for providing essential services goes up and it creates multiplier effect in the economy.

Although Subsidy fills the gap between the domestic price and the international price and keeps the price lower than its international price bot some prior studies show that fuel subsidies hinder growth in the long term through its effects on government budget.

1.3 Research Objectives
In this research, critical attention is devoted towards investigating the relationship between fuel subsidy and economic growth. This research try to attain the following:
To determine the responsiveness of fuel subsidy to changes in gross domestic product.

To ascertain the existence of a relationship between fuel subsidy and economic growth in Sudan.

To explore policy initiatives that can be put in place to promote economic growth.

1.4 Research Hypotheses
There a significant relationship between fuel subsidy and gross domestic product.

Fossil-fuel subsidies have a negative effect on gross domestic product.

There is a causal relationship between fuel subsidy and gross domestic product.

1.5 Methodology
An econometric approach has been adopted drawing growth model by putting GDP as a dependent variable, and fuel subsidy as independent variable with the inclusion of three other explanatory variables from which to estimate the impact of fuel subsidy on economic growth.

The research employs a quantitative time series approach using annual country level observations between 1990 and 2015. This will include ordinary least squares (OLS) regression analyses, unit root stationarity tests, Granger causality test, normality, heteroscedasticity, serial correlation and stability tests.

1.6 Scope and Limitation of the research
Since the research focuses on the relationship between fuel subsidy and economic growth in a specific country, Sudan. The scope of this research is to analyze the relationship between fuel subsidy and economic growth in Sudan, limited to the 1990 to 2015. The main reason why the scope is limited from the year 1990 is due to the lack of fuel subsidy data.

1.7 Structure of the Research
This research will comprise five chapters structured as follows: chapter one is an introductory. It states the research problem and its significance, objectives, hypothesises, methodology and source of information. Chapter two provides the theoretical background through reviewing the relevant parts of the extensive literature on fossil fuel subsidies and its positive and negative impacts on economic. Chapter three overviews the main characteristics of the Sudan Economy and its performance and polices. Chapter four includes econometrics model and the empirical analysis which aims to investigate the relationship between fuel subsidy and economic growth. Chapter five will be reserved for data analysis, interpretation of the results, conclusions and recommendations.

Chapter Two
2. 1 Subsidies: definition and measurement
2.1.1 What is a subsidy?
It has sometimes been argued that the concept of a subsidy is just too elusive to even attempt to define. As a result, the fairly large body of research on government subsidies uses a variety of concepts to define a subsidy. In the most general terms, a subsidy can be defined as: any government assistance that allows consumers to purchase goods and services at prices lower than those offered by a perfectly competitive private sector. Under this definition, subsidies to consumers include cases where the government, as a producer of goods and services, sells its output at a price that does not reflect all costs, including a normal return to capital, or compensates the private sector for doing so.

This is a broad definition in order to identify all existing subsidies in a sector, regardless of whether they are considered good or bad. This includes most support that could be considered a subsidy except for environmental externalities (such as carbon emissions or pollution).

This definition extends beyond the narrow subsidy concepts that are employed in fiscal or national accounts, and it leaves room for a wide range of government activities to be defined as subsidies. However, such a broad definition is necessary to capture both explicit and implicit subsidy elements that are contained in different forms of government intervention while a wide array of government activity may contain subsidy elements. Subsidies may be classified on the basis of the following seven categories:
Cash subsidies or Cash grants: direct government payments to producers or consumers.

Credit subsidies: government guarantees, interest subsidies to enterprises, or soft loans (i.e., low-interest government loans).

Tax subsidies: reductions of specific tax liabilities.

Equity subsidies: government equity participations.

In-kind subsidies: government provision of goods and services at below-market prices.

Procurement subsidies: government purchases of goods and services at above-market prices.

Regulatory subsidies: implicit payments through government regulatory actions that alter market prices.

But the above classification has at least three shortcomings. First, the various subsidies contained within each of the seven categories are not homogeneous. Tax subsidies, for example, may take on different forms, including those obtained through, tax credits, tax deferrals, or the accumulation of tax arrears. Second, some subsidies may, at least a priori, belong to several different categories. For example, consignment subsidies, that is, grants given to projects that are only repayable should the project turn out to be commercially successful, may, if the project is unsuccessful, be a cash grant, or, when the project is successful, become a credit subsidy when the interest rate is below the market rate. Third, it leaves ample room for ambiguities and measurement problems. For example, overvalued exchange rates affect market prices and access, and, while they contain subsidy elements (e.g., to those who purchase imported goods), they also entail costs or negative subsidies (e.g., to exporters); the full extent of the subsidy element of overvalued exchange rates may be difficult to establish, even on a gross basis.

2.1.2 How to measure subsidies?
There are several ways to measure subsidies, each of which has its advantages and shortcomings. One of most popular way to measure subsidies is budgetary cost which can be measured either on a gross or net basis. However, government budget data only provide an incomplete picture of the full extent of subsidy outlays, as they may show subsidies either under the budget category `subsidies’, under various other headings, or not at all.
More specifically, using government budgets for assessing the cost of subsidies has three main shortcomings:
First, the budget category `subsidies’ does not contain all government subsidies. In government fiscal accounts, only cash subsidies are classified as subsidies; other types of subsidies (i.e., credit, tax, equity, in-kind, procurement, and regulatory subsidies) are classified elsewhere or excluded from fiscal accounts. For example, tax subsidies show up implicitly as reduced tax revenue, but not explicitly in the budget category (subsidies), also loans are frequently classified as (net lending) rather than subsidies.

Second, government fiscal accounts do not capture most operations that create subsidies. Hence, a significant part of subsidy operations is carried out (off budget). For instance, some subsidy operations, such as payments to cover operational losses of state enterprises, have often been kept `off budget’. also, government fiscal accounts often do not contain subsidization operations provided by international organizations. For example, subsidies bestowed upon countries in the context of the common agricultural policy of the European Union and paid from the common budget are not reflected in the national budgets.

Third, fiscal accounts do not show the full economic impact of current subsidy practices. In many cases the budgetary impact may be delayed, but, eventually, it is likely to occur.

2.2 Subsidies as a policy tool
2.2.1 Why subsidize?
There are numerous reasons why governments may decide to use subsidies as a policy tool. From an economic perspective, the main purpose of subsidies is to reallocate resources, that is, to alter economic activity and behaviour to achieve an outcome that is `more desirable’ from what would occur otherwise. Hence, arguments for subsidies are often based on some concept of efficiency or economic justice. But even when subsidies generate a more desirable outcome, it does not mean that the entire value of the subsidy is corrective in nature, or that the particular type of subsidy used for a given purpose is best among the available policy alternatives.

Economic arguments for using government subsidies generally fall into three main categories:
offsetting various market imperfections.

exploiting economies of scale in production.

meeting social policy objectives, including, for example, protecting the poor, changing the distribution of income, and increasing or retaining employment.

2.2.2 How governments subsidize?
Any given policy objective can usually be pursued through different policy tools. Subsidization objectives are no different. Subsidies are intended to benefit specific groups of beneficiaries, but the extent to which they do frequently depends on how the subsidy is provided.

Fossil fuel subsidies, which exist in many countries, may be used to illustrate these points. The intended beneficiaries of fossil fuel subsidies are consumers, but the subsidy may be paid to either consumers or producers, and if it is given to producers, it may either be directed at inputs or outputs, or be given in the form of general operating support.

Defining Fossil Fuel Subsidy
Countries effort to advance fossil fuel subsidy reform have suffered from the lack of an established definition of what constitutes a subsidy, which makes the assessment of public support and cross-country comparison very difficult. and gives countries more room to omit mention of particular policies.

The World Trade Organisation (WTO) defines a subsidy as ‘any financial contribution by a government, or agent of a government, that confers a benefit on its recipients in comparison to other market participants’. This definition of subsidies and its detailed components has been accepted by the 153 member states of the WTO, and can be used as a basis for identifying fossil fuel subsidies, which include subsidies for the production and consumption of coal, oil and gas.

The World Trade Organisation takes a broad approach and defines a subsidy as “any financial contribution by a government, or agent of a government, that confers a benefit on its recipients”.

in the context of fossil fuels, subsidies are often split into two non-exclusive categories: those that reduce the cost of consuming fossil-based energy, called consumer subsidies, and those that support the domestic production of fossil fuels, called producer subsidies. This research focuses on consumer subsidies only. Though subsidies come in many different forms, the types of fossil-fuel consumer subsidies that are most commonly observed include:
Direct government expenditure to maintain fossil-fuel prices at below-market levels.

Selling domestically produced energy at below-market prices.

Regulation requiring other market actors to absorb the cost of selling fossil fuels at below-market prices.

Setting prices that do not recover the full costs of energy production or the costs of maintenance and reinvestment in energy infrastructure.

Foregoing revenue through tax exemptions, rebates or credits for fossil fuel consumers.

Several methodologies –not mutually exclusive– can be used to identify and measure consumer subsides:
Price-gap approach
Measures the net price effect of all energy subsidies and taxes in place. It does this by quantifying deviations between the price of international benchmarks and the price of fossil fuels within a country, adjusted for the costs of bringing the commodity to the market.

The basic formula of price gap approach is as follows:
Measuring the amount of subsidies per litres/gallons:
?P=Pr-PcCalculating the total amount of subsidies in a given year:
?P? Price gap
Pr ? Reference price /International price
Pc ? Consumer price
S ? Size of subsidy
E ? Fossil fuel energy consumption
A relatively similar formula was also introduced by the International Energy Agency (IEA) in 2015 to calculate their consumer subsidies. The formula is as follows:
Subsidy = (Reference price – End-user price) × Units consumed
From those two formulas, if the difference between the reference price and end user price is negative, the difference represents taxes. Meanwhile if the difference is positive, the difference represents subsidies.

Bottom-up approach
This approach captures transfers created by specific policies, such as the direct transfer of funds or liabilities and credit support.

Hidden cost approach
Estimates the value of energy that is consumed but not sold. It does this by estimating the difference between a utility’s current revenue and the revenue it would receive if it operated efficiently-charging tariffs that cover full costs, collecting all bills and with normal losses.

The consumer support estimate
Is a framework for organizing information on consumer support. It covers both measures that lower prices and those that support consumers through other means, thus requiring the use of both price-gap and bottom-up estimation methods or their equivalents.

2.4 International organisations Methodologies for estimating fossil fuel subsidy
Despite the lack of globally agreed definitions, three international organisations (the IEA, the IMF and the OECD) have attempted to collect data on fossil fuel subsidies in a systematic way, albeit with different methodologies:
International Energy Agency (IEA)
The IEA defines an energy subsidy as “any government action directed primarily at the energy sector that lowers the cost of energy production, raises the price received by energy producers or lowers the price paid by energy consumers. The IEA provides estimates annually of consumer fossil fuel subsidies for 40 developing countries, including the world’s top subsidisers. They are calculated using the price-gap approach, based on the differential between the end user price of a specific fossil fuel and a reference price of the same fuel. The IEA estimates that fossil fuel consumption subsidies in 2013 totalled USD 548 billion, or 5% of the total GDP of the 40 countries included in the analysis.

Organisation for Economic Co-operation and Development (OECD):
The OECD takes a different approach to estimate the extent of consumption and production subsidies together in its member states. The OECD uses an inventory based approach to estimate the value of fossil fuel subsidies in its member states. This method identifies all government measures (subsidies and tax breaks) that support fossil fuel production or consumption, and calculates and adds up the value of all these measures based on the government’s budget. The OECD estimates that in the 2005-2011 period an annual average of USD 55-90 billion was spent on fossil fuel (production and consumption) subsidies in its member states.

International Monetary Fund (IMF):
The IMF provides the most comprehensive pre-tax and post-tax subsidy estimates for 176 countries. Pre-tax subsidies are mostly based on the price-gap approach, and are therefore similar to IEA estimates. The IMF’s estimate for global pre-tax subsidies in 2011 totalled USD 492 billion, relatively close to the IEA’s estimate of USD 523 billion for the same year. According to the IMF, when the costs of climate change, local air pollution, congestion, accidents and road damage are included in the calculated subsidies for fossil fuels (which are not included in the OECD and IMF estimates), the global cost to society will be USD 5.3 trillion in 2018.

Although this progress in the estimation of subsidies is extremely valuable, substantial gaps remain because of limited transparency at the national level, and a full accounting of global energy subsidies has never been completed. As a result, it is likely that existing global estimates are well below the actual levels of subsidies.

2.5 Economic, social and environmental costs of subsidies for fossil fuel
When the full economic, social and environmental costs and benefits of fossil fuel subsidies are taken into account, their net costs far outweigh the benefits of sustaining them, and there are increasingly less costly alternatives that can achieve the same policy objectives.

Here are some Economic, social and environmental costs of fossil fuel subsidies:Creating a significant burden on government budgets
Energy subsidies can create a burden on government budgets (and more widely on trade flows and exchange rates), as when domestic fuel prices do not adjust automatically to changes in world prices, the government must step in to offset a portion of the shift. More directly, energy-consumption subsidies lead to greater domestic demand for energy products that must be imported, or that could potentially be exported, thus decreasing revenue and worsening the trade balance.

These impacts can be particularly acute in countries that produce fossil fuels and which generate a significant portion of their revenues from oil, gas or coal, where subsidies have a significant impact both domestically and internationally.

The significant proportion of many country’s budgets spent on maintaining subsidies to fossil fuels is a drain on public finances and reduces the resources available to address social and development objectives. In a number of countries that provide high levels of fossil fuel subsidies to consumers, such subsidies may be equivalent to, or significantly exceed, expenditure on health.

Decreasing competitiveness of the economy
Governments often use the under-pricing of energy inputs to support production across particular sectors or firms. The purpose of these subsidies is often to promote national or regional economic development. However, these subsidies may, in fact, encourage an inefficient allocation of resources across the economy by undermining efficiency, and encouraging over-consumption.

Countries where energy prices are much lower than the cost of producing it are characterised by very high consumption per capita and low energy efficiency. In Venezuela, which has some of the world’s highest levels of fossil fuel subsidies, petrol consumption per capita is 40% higher than in any other country in Latin America, and more than three times the regional average for Latin America and the Caribbean(LAC).

This impact of subsidies on inefficient over-consumption of resources by key industries and energy production has an impact not only on domestic consumption, as in Venezuela, but also means its highly subsidised oil is distributed internationally. Furthermore, every barrel sold domestically at a subsidised price cannot be exported at the international market price for hard currency.

Compromising energy security
Energy subsidies often start out as temporary income buffers. According to many governments these subsidies are intended to protect the population from the impact of international price hikes.66 In fact, governments may be less concerned about fluctuations in energy prices than about the resulting fluctuations in income (potential consumption) and its distribution.67 Since fossil fuel subsidies have been found to aggravate inequality and undermine the capacity of the poorest to obtain access to energy, they may in fact do more harm than good in protecting populations from volatile energy prices.

Perpetuating inequality and limiting access to energy and failing to address the needs of the poorest
Consumer subsidies are often justified as a way to help the poorest households to obtain access to energy. There is evidence that fossil fuel subsidies are actually regressive, since their benefits accrue mainly to middle- and higher income groups, while their costs are borne by the whole population.

IMF review of subsidies in Sudan found that the benefits from subsidies are captured by higher-income households, the top income quintile receives 48 percent of total subsidy, compared to 3 percent received by the bottom income.

Figure (2.1): Distribution of subsidy benefits by social groups

Source: Ministry of Petroleum and IMF estimates.

Although the benefits of subsidies accrue mostly to middle-class and wealthier sectors, the adverse impact of their removal can still fall disproportionately on the poor. As a result, the poor will be directly affected not only by the rising prices resulting from reforming subsidies, but also indirectly through the increased cost of transport and food. Any reforms to phase out subsidies for fossil fuels should therefore include measures to mitigate the likely negative impacts on the poorest households.

Damaging public health by increasing air Pollution
In many towns and cities, the pollution associated with the combustion of fossil fuels either for uses such as transport, or in transformation activities (to generate electricity and heat), is a major public health problem. It is estimated that, globally, air pollution resulting from the combustion of fossil fuels was responsible for 3.7 million premature deaths in 2012.

The IMF has found that phasing out subsidies to fossil fuels would lead to reduced emissions of air pollutants such as sulphur dioxides (SOx), nitrogen oxides (NOx) and particulate matter, which are not only harmful for public health but also cause environmental problems such as acid rain, and material damage to infrastructure.

Eliminating subsidies to high-emission sectors forms part of the Kyoto protocol (Article 2.1 pledges Annex I parties to reduce “subsidies in all greenhouse gas emitting sectors that run counter to the objective of the Convention and application of market instruments” – UNFCC, 2009).

Also Paris Agreement, which was agreed in December 2015, sets the framework for immediate actions and long-term strategies to prevent dangerous climate change. This includes opportunities to address a significant obstacle to the Low Carbon Transition – subsidies and public finance for fossil fuels. Under the Paris Agreement, governments have committed to limiting global temperature rise to well below 2°C and pursuing efforts to limit this increase to 1.5°C.

To meet this pledge, the vast majority of fossil fuels will need to remain in the ground, with all countries requiring a shift to energy systems that are fully clean.

Article (2.1c) in Paris agreement says: ‘Making financial flows consistent with a pathway towards low greenhouse gas emissions and climate resilient development’.

Article (2.1c) highlights one of the main objectives of the Paris Agreement: the global financial system – including that which is driven by government subsidies and public finance – must work for climate action and not against it. It is clear that to meet the climate objectives set out under the Paris Agreement, we will need to limit fossil fuel production and reduce fossil fuel consumption. Making the global financial system work for climate action and not against it requires ending all forms of government support to the production and consumption of fossil fuels.

The IMF has found that phasing out subsidies to fossil fuels would lead to reduced emissions of air pollutants and a combination of subsidy reform and corrective taxes on fossil fuels could result in a 23% reduction in these emissions as well as a 63% decrease in deaths worldwide from outdoor fossil fuel air pollution.

2.6 Framework for analyzing whether to introduce, retain, redesign or remove a fossil fuel subsidy
Policymakers considering whether to introduce, retain, redesign, or remove a particular fuel subsidy may find it helpful to ask a number of question concerning the subsidy. A suggested list of questions is given in Figure (2.2). If any answer is negative, the policymaker should consider either phasing out the subsidy or redesign the subsidy.

Figure (2.2): Schematic Approach to Assessing fossil fuel Subsidies
1555234201983Question 1: Does the policy substantially achieve its objectives?
00Question 1: Does the policy substantially achieve its objectives?

32888905248111Redesign or phase out subsidy
00Redesign or phase out subsidy
571505249545Retain (existing) or introduce (new) subsidy
00Retain (existing) or introduce (new) subsidy
1644650910590  Step 2: Value costs and benefits of the subsidy policy and its alternatives
00  Step 2: Value costs and benefits of the subsidy policy and its alternatives
42321083708914Redesign or phase out subsidy
00Redesign or phase out subsidy

1584002228546Question 2: Is the policy the most socially efficient instrument to achieve its objectives?
00Question 2: Is the policy the most socially efficient instrument to achieve its objectives?

385450470872Is the impact of the policy consistent with the country’s overall strategy on GHG emissions?
00Is the impact of the policy consistent with the country’s overall strategy on GHG emissions?
155300678622Question 3: Does the policy avoid negative externalities?
00Question 3: Does the policy avoid negative externalities?

1591751235778Question 4: Is this use of funds a budgetary priority?
00Question 4: Is this use of funds a budgetary priority?


If any answer is negative, the policymaker should consider either phasing out the subsidy or redesign the subsidy.

Chapter Three
3.1 Introduction Sudan is a low-income fragile country facing significant domestic and international constraints and large macroeconomic imbalances such as widening of the fiscal deficit which rise to 0.2 percentage points in 2016. Decades of internal conflicts and U.S. sanctions have undermined economic stability and growth.

While the start of oil production in 1999 triggered rapid growth that tripled per capita income within a decade, Sudan lost the bulk of its oil exports and related budget revenues following the secession of South Sudan in 2011.

Since 2012, the authorities have launched reforms to adjust to the loss of oil revenues. Measures included exchange rate adjustments, subsidy reductions, fuel price hikes, and tax increases. Tax collections and public financial management were strengthened, and social spending was increased to mitigate the impact of the adjustment on the poor. The reforms helped reduce the fiscal deficit, slow money growth, ease inflation, and support growth. The authorities’ recent five-year reform plan for 2015–2019 continues in the same direction.

3.2 Economic Growth in Sudan
Despite GDP expanding more than seven times since 1960, Sudan’s economic growth has not been inclusive. Growth has averaged 3.9 percent per year since 1960, it has been volatile with a standard deviation of 140 percent.

Sudan’s economic growth was adversely affected by a number of factors, including declining oil revenues because of low export prices, ageing oil fields and reduced inflows of oil transit fees from South Sudan. GDP growth is estimated at 3% in 2016, compared to 4.9% the previous year and forecast at 3.4% and 3.6%, respectively, for 2017 and 2018.In the short and medium terms, growth will be determined by developments in the agricultural and mineral sectors, skills development and prudent macroeconomic policies and structural reforms aimed at improving the business climate. Significant downside risks include continuing civil wars in some parts of the country and low global commodity export prices.

The macroeconomic imbalances and the consequent widening of the fiscal deficit by 0.2 percentage points in 2016, continue to constrain growth. Although the current account deficit narrowed by 1.1 percentage points in 2016 and expected to further widen to (5.6% of GDP) in 2018. Closing the fiscal and current account deficits is a major policy priority especially in the face of low tax revenues and shortfalls in oil export receipts as well as difficulties in accessing concessional financing.
However, the partial conditional easing of the US trade sanctions in early 2017 is expected to contribute to economic stability.

Figure (3.1): Real GDP growth (Annual percent change)

Source: World Bank Data
3.3 Challenges of Inclusive Growth in Sudan
Limited fiscal resources and macroeconomic imbalances:
Government revenue is low (particularly tax revenue at 6.2 percent of GDP in 2015), thereby limiting resources for social spending and investment in infrastructure critical for private sector growth. And macroeconomic imbalances are high-inflation has averaged 35 percent in the past four years.

Internal conflicts:
The UN estimates that there were 3.1 million internally displaced people in Sudan in 2015 owing to internal conflicts. Continued conflicts have been an important factor in the absence of resolution of Sudan’s external
3.4 Macroeconomic policies in Sudan
3.4.1 Fiscalpolicy
The government uses fiscal policy to allocate and redistribute resources because the allocation and distribution resulting from private sector activities are not satisfactory in most cases, especially in the Less Developed Countries.

The oil decade, from 1999-2010, witnessed the strongest growth episode in the country’s history, there are serious concerns about the negative impact of the post-separation adjustments on real growth and social development. The immediate impact of the post-separation adjustment was ?scal. So all subsequent budgets focused on cutting spending, increasing taxes and removing subsidies such as fuel and sugar subsidies in order to contain the fiscal deficit. But the government needs to implement critical reforms to establish widespread support to avoid subsidies removal effects.

In 2016 the total spending fell to 10.8% of GDP compared with 11% in 2015 because of the partial removal of subsidies, but the shortfalls in oil revenues and slow progress in raising non-oil revenues widened the deficit to an estimated 1.8% of GDP in 2016, up from 1.6% in 2015.

Figure (3.2): Public finances (percentage of GDP at current prices)

Source: World Bank Data
3.3.2 Monetary policy
For many developing countries, including Sudan, the monetary policy is considered to be one of the most influences on productive sectors and it continues to contribute in the growth domestic product of these countries. Sudan has been plagued with several challenges where the productive sectors are suffered from the lack of monetary and financial policies.

Monetary policy in recent years is largely expansionary because of the sizeable government financing needs. Also the monetary policy in Sudan is hampered by fiscal policy dominance and reliance on central bank financing of the budget deficit.

3.5 Fossil Fuel subsidy in Sudan
Fossil fuel subsidies have long been used in many countries, both developed and developing, to encourage the production of goods and services through lowering the cost of production. As in other countries, the Sudanese government has been subsidizing fuel products where they are sold below the market price.

Evidently, fuel subsidy has made a hole in the country’s budget, contributing to the fiscal deficit, which stood at 5.2% of GDP in 2012.

Table (3.1): Production costs, retail Prices, and subsidies in 2012:
Diesel Gasoline LPG Kerosene Jet A Fuel Oil
Production Cost 4196 5584 3576 4437 4472 894
Cost of Crude 3734 4739 2958 4287 4287 809
Refining Cost 82 92 78 23 23 23
Transport 27 31 34 0 35 32
Profit Margin & Marketing 109 158 420 127 127 30
VAT 85 154 86 0 0 0
Production Tax 159 409 0 0 0 0
Retail Price 2147 3878 1200 1858 4108 378
Unit Subsidy 2049 1706 2376 2578 364 517
in percent of retail price 95 44 198 139 9 137
Table (.) Production Costs, Retail Prices, and Subsidies (SDG/Metric ton)
Following the 2011 secession of South-Sudan, Sudan has lost almost 75% of its oil production representing approximately 55% of its fiscal revenues, and two-thirds of its foreign exchange earnings. This slowed growth and brought rising inflation as well as deterioration of the fiscal and current account balances. Government’s primary policy response to the catastrophic economic events following secession was a package of macroeconomic reforms.

Government formulated the 3-year Economic Salvation Programme (2012-2014), including tax reform, a gradual reduction in fuel subsidies, cuts in non-priority public expenditures, and a strengthening of social safety nets to reduce the impact of these reforms on the poor.

On June 2012 the Sudanese government has increased the price of selected fuel products in the context of the revised 2012 budget, thereby significantly reducing fuel subsidies. the price of gasoline has increased from 8.5 to 12.5 SDG per gallon, the price of diesel from 6.5 to 8 SDG per gallon, the price of liquid petroleum gas (LPG) from 13 to 15 SDG per gallon.

Also in September 2013, the Sudanese government implemented its second fuel subsidy cuts. The price of petrol was increased by 68 per cent, from SDG12.5 to SDG21/gallon. Diesel prices rose by 75 per cent from SDG8 to SDG14/gallon, and the official price of a 12.5kg cylinder of LPG was boosted by two thirds, from SDG15 to SDG25.2 At the same time, the official exchange rate was also devalued. These were the sharpest price increases in domestic in the series of austerity measures.

Implementation of the authorities’ adjustment program had been done to restore macroeconomic stability and improve growth prospects over the medium term, and growth is expected to accelerate gradually to about 4.7 percent in 2019. And the IMF has estimated that the September subsidy reform will generate about 797 million SDG in budgetary savings.

Chapter Four
4.1 Introduction
This chapter deals with estimation procedures that will be undertaken to ascertain the association that exists between fossil fuel subsidy and economic growth. As such, will encompass stationarity test, normality test, co-integration, granger causality and diagnostic tests.

4.2 Source of Data
In order to analyze the relationship between of fossil fuel subsidy and economic growth, we use secondary data sets. The secondary data employed in this research were taken from several reliable sources such as Central bank of Sudan, Central bureau of statistics, IMF, and The World Bank. However, the fossil fuel subsidy data are only available for certain period. Hence, the time coverage of data is depending on the availability of the data. The data for fossil fuel subsidies were taken from the IMF and spanned from the period of 1990 to 2015.

The IMF calculate subsidies by using price gap approach. They compare the domestic price with the reference price. The data about subsidies provided by IMF capture oil subsidies, electricity subsidies and natural gas subsidies.

The data about GDP, CPI, Export and Import (which being used to measure openness) are taken from the World Bank.

4.3 Model Specification
Prior studies on growth show that there are several factors which contribute to economic growth. Chen and Feng (2000:4) mentioned that investment, human capital, international trade and inflation significantly influence growth. While some other research found that demographic factors such as population and population growth also influence economic growth. Furthermore, Hussin and Saidin (2012) on their research in Association of Southeast Asian Nations ASEAN countries found that openness contributes significantly to growth in Indonesia.

Based on prior research and finding, we try to develop model that will be used in this research. Since this research will examine the impact of fossil fuel subsidy, we add subsidies variable in the model.

The model has four explanatory variables consisting of fossil fuel subsidy, openness, investment and consumer price index of the country as independent variables.

Thus we proposed equation to measure the impact of fossil fuel subsidy on economic growth written as:
GDP=?1+?2FFsubs+?3Open+?3Inv+?4CPI … (1)
GDP= Gross domestic product in million SDG
FFsubs= Total fossil fuel subsidies in million SDG
Open= Average of the degree of openness (in %). Conventionally the degree of openness is measured by using (X+M)/GDP.

Inv= Total Investment in million SDG
CPI= Consumer price index
4.4 Definition of Variables:
The dependent variable in this research is Growth in which measured by annual GDP per capita as explained above. The explanatory variable consists of several independent variables such as fossil fuel subsidies openness and consumer price index.

The total value of all final goods and services produced within Sudan. The series is published by World Bank in millions of SDGs. GDP is widely used as an indicator for economic growth as it captures the value of output produced and services rendered in an economy, giving a good indication of aggregate productive activity within it.

Fossil fuel subsidy
Since the main objective of this analysis is to investigate the impact of fossil fuel subsidy on growth we put it as the main independent variable.

In this research, the fossil fuel subsidy is measured in million SDG. The data about subsidy were taken from IMF. According to prior studies, the relationship between fossil fuel subsidy and growth can be either positive or negative.

Hussin and Saidin (2012) defined openness as the degree of the economy interacts with other countries (the rest of the world). Commonly openness measured by using this formula:
Openness= (total export + total import)GDPHere we expect that the more open the economy, the higher the economic growth (the relationship between openness and growth is positive).

Investment is defined as total value of gross accumulation. Data regarding total investment is obtained from the Sudanese Central Bureau of Statistics and spanned from the period of 1990 to 2015.

Consumer price index
Consumer price index reflects changes in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. Which means it depicts the real inflation rate faced by the end user.

The data for CPI were taken from central bureau of statistics and spanned from the period of 1990 to 2015.

Table (4.1): Expected signs for Variables
Variable Data source Period Expected relationship
FFsubsIMF 1990 – 2015 ( – ) or ( + )
Open CBOS 1990 – 2015 ( – ) or ( + )
CPI CBOS 1990 – 2015 ( + )
INV CBOS 1990 – 2015 ( + )
4.5 Stationarity test
In time series analysis, a great deal of attention is given to stationarity of the variables in order to get rid of the problem of spurious regression.

Damodar and Gujarati (2004) outlined that stationarity is a condition that exists when the data’s mean, variance and covariance do not have a unit root. This implies that the mean and variance are constant over time and the value of the covariance between the two time periods depends only on the distance or gap or lag between the two time periods and not the actual time at which the covariance is computed.

It is often said that most macro-economic variables follow a random walk model, i.e., exhibiting a unit root behaviour. A random walk model can be justified when the following properties hold:
Mean: EYt=?Variance: VarYt=(Yt-?)2=?2Co-variance: ?k=EYt-?Yt+k-?Where:
Yt= is a series of random walk.?k= is the auto covariance at lag k. If k=0, we obtain ?0, which is simply the variance of Y (=?2).

If one or more of the above conditions fail, the random walk process Yt is said to be nonstationary exhibiting a unit root problem.

The stationarity test that has become widely popular in time series econometric analysis is the unit root test. Unit root test is a statistical procedure that is designed to make judgment as to whether a given time series data implies a unit root or the time series is stationary. In this research, we use Augmented Dickey Fuller to test whether a given time series data implies a unit root or the time series is stationary.

Augmented Dickey Fuller test
The null hypothesis for the test is that the time series is non-stationary, while the alternative hypothesis states that there is no unit root or the time series is stationary.

The general form of Augmented Dickey Fuller (ADF) is estimated by using the following models:
Yt=?Yt-1+?tIf ? =1, the above equation becomes a random walk, that is, a non-stationary process. As a result of this there tends to be the so called unit root problem which means there is a situation of non-stationarity in the series.

However, if ? <1, this means that the series Yt is stationary.

4.6 Econometric tests
Based on theory of statistics and econometrics we proceed with a set of standard econometric tests to ensure we are using suitable methods and our regressions fulfil the required assumptions.

4.6.1 Normality Test
There are several tests for normality such as histogram of residuals normal probability plot (NPP), Anderson–Darling and Jarque–Bera tests. The Jarque–Bera test for normality is employed in this research.

The Jarque – Bera test is a test based on OLS residuals. First, it requires calculating the Skewness and Kurtosis and then measures the OLS residuals as:
n = is the sample size.

S = is the skewness coefficient.

K = is the kurtosis of the coefficient.

Therefore, the JB test of normality is a test of the joint hypothesis that S and K are 0 and 3, respectively. In that case the value of the JB statistic is expected to be 0.

Under the null hypothesis that the residuals are normally distributed, If the computed p value of the JB statistic is suf?ciently low, one can reject the hypothesis that the residuals are normally distributed. But if the P value is reasonably high, which will happen if the value of the statistic is close to zero, we do not reject the normality assumption.

4.6 2 Serial Correlation Test
Serial Correlation is a correlation among members of the series of error terms ordered in time. It is mainly caused by incorrect functional forms, auto regressions, manipulation of data, data transformation and non-stationarity of the data.

The problem of serial correlation can be detected using the graphical method, Geary test, Durbin – Watson d test and Breusch–Godfrey (BG) test. In this research, the BG test that is based on the Lagrange Multiplier principle is chosen to test the serial correlation.

To illustrate the test, let:
Yt=?1+?2Xt+utAssume that the error term ut follows the ? th-order autoregressive, AR(p), scheme as follows:
ut=?1ut-1+?2ut-2+…+??ut-?+?twhere ?t is a white noise error term
The null hypothesis H0 to be tested is that:
H0:?1=?2=…=??=0That is, there is no serial correlation of any order.

4.6 3 Heteroscedasticity test
one of the important assumptions of the classical linear regression model is that the variance of each disturbance term ui, conditional on the chosen values of the explanatory variables, has a normal distribution with mean zero and variance of ?2. This is the assumption of homoscedasticity. Symbolically:
Varui=?2 i=1, 2, … ,nBut when the error term does not have constant variance, i.e.

Varui=?i2we call it heteroscedasticity.

Consequences of heteroscedasticity:
We couldn’t establish con?dence intervals and test hypotheses with usual t, F tests.

The usual tests are likely to give larger variance than the true variance.

The variance estimator of ? by OLS is a biased estimator of the true variance.

We will use Autoregressive Conditional Heteroscedasticity (ARCH) LM test to test if there is a heteroscedasticity problem or not.

H0: no heteroscedasticity
H1: there is heteroscedasticity
4.7 Granger Causality
Granger causality seeks to determine if one variable causes a change in the other variable and the direction of causality between the two variables. Assuming that we have two variables Xtand Yt, then granger causality can be employed to determine the causal relationship between the variables and their direction of causality.

Granger causality test will be utilized to research if there is causality relationship between fuel subsidy and economic growth whether it is bidirectional, unidirectional or no causality relation between them.

4.8 Stability Diagnostic test
Stability tests are tests that are undertaken to determine if the utilised model is stable, that is, if it satisfies the OLS assumption. This requires that both the model and the residuals be subjected to recursive estimate tests. The recursive test requires that both the model and the residuals values lie within a stipulated band. If not so, then a structural break is said to exist and the model is not stable.

CUSUM test of the stability over time of the coefficients of a linear regression model, which is usually based on recursive residuals, can be applied to ordinary least squares residuals to find out if the model is stable or not at 5% level of significance.

The CUSUM test is based on a plot of the sum of the recursive residuals. If this sum goes outside a critical bound, one concludes that there is a structural break at the point at which the sum began its movement toward the bound.CUSUM test of the stability over time of the coefficients of a linear regression model, which is usually based on recursive residuals, can also be applied to ordinary least squares residuals.

Chapter Five
5.1 Introduction
This chapter provides an empirical analysis of the data using the techniques reviewed in chapter four and the interpretation of the results obtained.

5.2 Results
5.2.1 Descriptive Summary
The descriptive statistics of the independent variable (GDP) and the dependent variables are presented in the Table (5.1).

Table (5.1): Descriptive Statistics
Mean 111624.5 2512.423 25019.61 0.197302 110.2504
Median 51746.20 1986.780 10153.25 0.227185 71.68000
Maximum 582936.7 6079.000 121793.0 0.386260 502.5300
Minimum 1101.100 646.0900 102.6600 0.007799 0.235000
Std. Dev. 144935.9 1669.378 32249.18 0.113658 128.2619
Skewness 2.002578 1.112072 1.627665 -0.490114 1.829735
Kurtosis 6.472248 2.977650 4.760560 2.096758 5.674324
Jarque-Bera30.43927 5.359592 14.83814 1.924753 22.25571
Probability 0.000000 0.068577 0.000600 0.381984 0.000015
Observations 26 26 26 26 26
Interpretation of Table
The first two rows in table 3 show the average value of the series as a mean and the middle values of the series as the median. The maximum and minimum values of the series are also given for each series under the row maximum and minimum, respectively.

Skewness measures the asymmetry of the distribution of the series around the mean. Thus, among the values of skewness in Table (5.1), only openness is close to symmetric distribution with the value of -0.49. all Series except openness are negatively skewed implying that these distributions have a long left tail.

The row under kurtosis in table 3, measures the flatness of the distribution of the series. A normal distribution has a kurtosis value of 3 and hence (FFsub) is said to be near the normal distribution with the kurtosis value of 2.97.

However, by simple observation of the values of skewness and kurtosis it is difficult to tell whether a given series is normally distributed or not. In Table 3 above, the result for Jarque Bera (JB) test for normality is given for each variable. Under the null hypothesis of normal distribution JB statistic follows a chi-square (?2) distribution with two degrees of freedom.

According to the results of Table (5.1), the null hypothesis of normal distribution is rejected for the series such as GDP, Investment and CPI due to the sufficiently low p-values of the JB statistic. Whereas the null hypothesis of normal distribution cannot be rejected for FFsub and Openness since its p-value is reasonably high (more than 0.05).

5.2.2 Stationarity of the series
To avoid estimating a spurious regression model, we check for the stationarity of the series before doing any analysis. To check for stationarity, we apply the Augmented Dickey Fuller (ADF) test.

The result of Augmented Dickey Fuller unit root test is summarized in Table (5.2) below:
Table (5.2): Stationarity Results
Variables Test
Statistic Probability Test critical values (5%) order of integration Decision
GDP -5.275745 0.0003 -2.998064 I(2) Reject the null hypothesis
FFsub-4.875246 0.0007 -2.991878 I(1) Reject the null hypothesis
Open -6.029837 0.0000 -2.991878 I(1) Reject the null hypothesis
INV -12.12649 0.0000 -2.998064 I(2) Reject the null hypothesis
CPI -3.447858 0.0201 -3.004861 I(2) Reject the null hypothesis
From the results, we found that all the series are not stationary at their level form but stationary at 1st difference, and 2nd difference. In addition, we checked for the stationarity with intercept.

For the FFsub and Open, after first differencing in the series, the ADF supports a hypothesis that the series is stationary. Whereas GDP, INV and CPI were found to be stationary at their second difference.

5.2.3 Regression Outputs
In this research the relationship between GDP (dependent variable) and the independent variables is estimated by applying ordinary least squares (OLS). Below is the result of the regression:
Table (5.3): Regression Outputs
Dependent Variable: GDP
Method: Least Squares
Sample (adjusted): 1991 2015
Included observations: 25 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.  
FFsub-12.61847 4.132869 -3.053198 0.0063
INV 1.335467 0.424275 3.147644 0.0051
OPEN -49295.01 27438.65 -1.796554 0.0875
CPI 1639.574 201.8060 8.124505 0.0000
C 26326.24 10358.21 2.541582 0.0194
R-squared 0.905164     Mean dependent var23273.42
Adjusted R-squared 0.886197     S.D. dependent var40439.40
S.E. of regression 13642.13     Akaike info criterion 22.05657
Sum squared resid3.72E+09     Schwarz criterion 22.30035
Log likelihood -270.7071     Hannan-Quinn criter. 22.12418
F-statistic 47.72251     Durbin-Watson stat 2.013495
Prob(F-statistic) 0.000000 From the pooled regression we can see that the R-Squared is 90.51%, while the Adjusted R-Squares is 88.61%. The R-squared shows that about 90% variation of growth can be explained by the model.

Durbin-Watson value (2.01) indicates that there is no autocorrelation problem in the model.

The low p-value of (FFsub) indicates that there is a significant relationship between fossil fuel subsidy and growth. And the negative coefficient of FFsubs shows that fossil fuel subsidy has a negative impact on economic growth. The coefficient implies that 1-unit increase in FFsubs will decrease Growth by 12.61 units.

The result is in line with Granado and Coady (2010) who claim that the presence of subsidies depresses government budgets and creates hindrances on growth. Meanwhile, Mourougane (2010) found that energy subsidies decrease government ability to invest in the infrastructure and productive sectors which will hinder growth in the future.

5.2.4 The impact of other explanatory variables toward growth
In this research we suspected that other explanatory variables would have a positive relationship toward growth.

The research found empirical evidence that Investment and Consumer price index (CPI) are positive and significant at 5% toward growth. Openness is expected to have positive impact toward growth, since the degree of openness facilitates the flow of capital and technology which are important to boost growth, but regression results failed to find a significant relationship between the degree of openness and economic growth.

5.2.5 Granger Causality
Table (5.4): Granger Causality test
Pairwise Granger Causality Test
Lags: 2
 Null Hypothesis: F-Statistic Prob. 
 FFSUB does not Granger Cause GDP  1.09227 0.3579
 GDP does not Granger Cause FFSUB  0.26823 0.7679
It can be noted that fossil fuel subsidy does not granger cause economic growth and that economic growth does not granger cause fossil fuel subsidy. This is because their respective p-values are more than 5% and hence we can accept their null hypotheses.

So there is no causal relation between fossil fuel subsidy and economic growth.

5.2.6 Normality Test Outputs
The normality test for the residual is undertaken using the Jarque-Bera (J.B.) statistic.

Figure SEQ Figure * ARABIC 5 Jarque-Bera (J.B.) statisticFigure (5.1): Jarque-Bera (J.B.) statistic

The J.B. test from Figure (5.1) shows that the error terms are normally distributed. This is due to the low test statistic which is 0.131059 and very high p-value which is equal 0.936571.

5.2.7 Serial Correlation Test Outputs
In this research, the Breusch-Godfrey test that is based on the Lagrange Multiplier principle is chosen to test the serial correlation. Table (5.5) shows the result of the test:
Table (5.5): Breusch-Godfrey Serial Correlation LM Test
F-statistic 0.297553 Prob. F(2,18) 0.7462
Obs*R-squared 0.800085 Prob. Chi-Square(2) 0.6703
The Breusch-Godfrey serial correlation Lagrange Multiplier test confirms that the residual terms in the model are serially independent.

The observed R-squared which is used to make decision of the correlation test has a value of 0.800085 with a p-value of 0.6703. The large Chi-square probability value implies the nonexistence of the serial correlation problem at 5% level of significance.

5.2.8 Heteroscedasticity Test Outputs
We used Autoregressive Conditional Heteroscedasticity LM to test if there is a heteroscedasticity problem or not. Table (5.6) shows the test results:
Table (5.6): Heteroscedasticity Test (ARCH)
Heteroscedasticity Test: ARCH
F-statistic 2.267788 Prob. F(1,22) 0.1463
Obs*R-squared 2.242764 Prob. Chi-Square(1) 0.1342
The F-statistics of the test has a value of 2.267788 with a p-value of 0.1463. On the other hand, the observed R-squared of the ARCH LM test has a value of 2.242764 and the one lagged Chi-squared probability value is 0.1342.

From these results, ARCH LM test strongly suggests that there exists no heteroscedasticity in the residual terms of the model. Hence, the null hypothesis of no heteroscedasticity can’t be rejected implying that the variance of the error term is constant.

5.2.9 CUSUM Stability Diagnostic test
The figure below shows the result for CUSUM stability test:
Figure SEQ Figure * ARABIC 6 CUSUM stability testFigure (5.2): CUSUM stability test

Results shows that both the model and the residuals values lie within a stipulated band. So, there is no structural break and the model is stable.

5.3 Conclusions
While the rationale for reducing subsidy is to ease the financial burden of the government is commonly understood, its consequences on Sudanese economic growth remain to be discovered. This research aimed to find empirical evidence about the relationship between fossil fuel subsidy and economic growth. In order to meet the objectives, this research employed ordinary least squares (OLS) to ascertain the presence of a significant relationship between fuel subsidy and economic growth.

The analysis results show support for Hypothesis 1 that there a significant relationship between fuel subsidy and economic growth, also results show support for Hypothesis 2 that fuel subsidy may have a negative effect on GDP because the result of the regression found that fuel subsidy has a negative impact toward economic growth. The negative sign of fuel subsidy variable toward growth are consistent with some prior research such as Erika Sulistiowati (2015) which found that oil subsidies are negative toward growth. This is supported by Granado and Coady (2010) who mentioned that subsidies depress government budget and hinder growth.

However, Hypothesis 3 that there is causal relationship between fuel subsidy and economic growth is not supported by Granger Causality results.

This research also found empirical evidence that other explanatory variables, Investment (INV) and Consumer price index (CPI), are positive and significant toward economic growth.

Our research illustrates that the highest proportion of fuel subsidy’ benefits (48%) goes to the highest top 20% of the income group, and the results support the viewpoint that there is no reason to keep fuel subsidy.
5.4 Recommendations
Short term:
Develop a detailed plan to reduce inflationary impact of reducing fuel subsidies, ensuring the availability of key goods and consumer staples, and reducing simultaneous government expenditure on other programs as possible.

Further investigate how subsidy savings could be redirected to affected people.

Long term:
Prepare structural mechanisms to reduce the impacts of subsidy reform, for example, energy-saving options and retraining the labour force that will be most affected by the reform (for example, freight transporters and farmers).

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