International Journal of Economics, Commerce and Management

United Kingdom Vol. III, Issue 5, May 2015

http://ijecm.co.uk/ ISSN 2348 0386

CRUDE OIL PRICE AND GROWTH OF OUTPUT

THE CASE OF GHANA

William Godfred Cantah

Department of Economics, University of Cape Coast, Ghana
William.cantah@ucc.edu.gh

Emmanuel E. Asmah
Department of Economics, University of Cape Coast, Ghana
easmah@ucc.edu.gh

Abstract
Crude oil has become an integral part of the Ghanaian economy. This makes the growth of the
Ghanaian economy vulnerable to fluctuations in the world price of crude oil, especially when the
country still depends largely on imported crude oil to meet her crude oil needs. This study
therefore employed the ARDL approach to cointegration to examine the relationship between
crude oil price and Ghana’s economic growth using annual data set from 1967 to 2012. Unlike
previous studies on crude oil price economic growth relationship for Ghana, this study controlled
for the effect of fiscal policy in the relationship. This study adopts the neoclassical growth model
of Solow. The results of the study indicated the existence of a long run relationship between
crude oil price and economic growth in Ghana. Also, the study revealed that oil price increases
had a negative impact on economic growth in both the short run and long run and this was
reinforced by increases in government expenditure in response to the oil price in the form of fuel
subsidies. The policy implications of the study are that fuel subsidies should be removed and
the country should consider alternative sources of energy which are cheaper relative to crude oil
price.

Keywords: Crude oil Price, Economic Growth, Output, Ghana

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INTRODUCTION
Energy is a vital ingredient in achieving sustained growth of every nation and Ghana is not an
exception. Sources of energy in Ghana comprise 69.5% biofuels and waste (predominantly
used by rural households for cooking), 24.1% crude oil and 6.4% Hydro (International Energy
Agency, 2012). Crude oil is the main source of energy for the productive sectors of the
Ghanaian economy. It accounts for about 89%, 39% and 99.7% of energy consumption in the
agricultural sector, industrial sector and transport sector respectively (Energy Commission,
2006).Consequently, changes in the price and availability of crude oil can have significant
impact on the economic growth of the Ghanaian economy. Despite the important role crude oil a
play in the Ghanaian economy coupled with rising consumption of the product, the country
depends largely on imported crude oil to meet domestic demand for petroleum products. This
makes the country very vulnerable to changes in the international price of crude oil. The effect
of crude oil price fluctuations on the growth of an economy is transmitted through both supply
and demand channels (Jimenez-Rodriguez & Sanchez, 2005). In Ghana, increases in the world
price of crude oil is transmitted into the domestic economy through increases in the domestic
prices of petroleum products in the country. As a major source of input for the productive
sectors of the economy, such increases tend to have serious repercussions on the country’s
economic growth.
Ghana has experienced poor growth rates in the past during periods of crude oil price
hikes. For example, between 1973 and 1983 Ghana experienced an average decline in per-
capita GDP of more than 3% a year (Fosu & Aryeetey, 2008). The crude oil price shocks of
1974 and 1979/81 was partly blamed for this economic misfortune (Aryeetey & Harrigan, 2000).
Crude oil price during this period, quadrupled from the 1972 price of $2.48 to $11.58 per barrel
by 1974. The price of crude oil further increased to $36.83 per barrel by 1980 (British
Petrochemical, 2012). It is however difficult to completely attribute the downturns in economic
activity during this period to the volatile nature of oil prices. This because the period of poor
economic performance in Ghana also coincided with political instability, high levels of corruption
as well as high levels of economic mismanagement. This period of economic misfortunes and
crude oil price shocks was followed by economic reforms in the country and relatively low crude
oil prices. This development ensured that the country enjoyed stable uninterrupted growth with
the GDP growth rate averaging 5% year since the inception of democracy in 1993 (Killick,
2010).
This stable trend in economic growth was however disrupted in 2000 and 2008 following
high crude oil prices during these periods. With crude oil prices averaging $28.3 per barrel in
2000, domestic prices of crude oil products rose by more than 20%; budget deficit also

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increased by 87.7% and economic growth fell from 4.4% in 1999 to 3.7% in 2000 with inflation
rising to 40.8%. The nominal exchange rate between the Ghana Cedi and the US dollar
depreciated from GH¢0. 35 per dollar in January 2000 to GH¢0. 63 per dollar by December
2000 (World Bank, 2012). Similarly, the country's stable growth trend was also disrupted again
in 2008 after the global rise in food and crude oil prices, with crude oil price reaching about $147
per barrel in July 2008. Government's efforts to insulate domestic consumers, at least to some
extent, resulted in fiscal deficit. Inflation rate accelerated from 10.9% in 2006 to 12.8%, 18.45%,
and 20.75% in 2007, 2008, and 2009 respectively. The exchange rate also depreciated by
about 50% against the US dollar between 2008 and the first half of 2009 (Mhango, 2010). On
the other hand, crude oil serves as a source of tax revenue and input into domestic production
of goods and services. Crude oil therefore contribute to and thereby influence the Gross
Domestic Product of the country as a source of energy. Hence, an assessment of the effect of
crude oil price on the Ghanaian economy is critical to estimating the growth path of the
economy.
Interestingly, related studies on the relationship between crude oil price and economic
growth in sub-Saharan African countries especially Ghana are very few (e.g. Olomola &
Adejumo, 2006; Akpan, 2009). Studies by Jumah and Pastuszyn (2007) and Tweneboah and
Adam (2008) appear to be the only two known studies on Ghana. Moreover, most of these
studies did not capture the effects of fiscal policy on the relationship between crude oil price and
the growth of the Ghanaian economy. Fiscal policy has been identified as a major channel
through which oil price fluctuations affect the growth of developing countries (Bhanumurthy,
Das, & Bose, 2012). Also, these studies employed the VAR framework in their analysis.
However, Kilian and Vigfusson (2011) demonstrated that oil price VAR models are
fundamentally misspecified and this renders the parameter estimates inconsistent and inference
invalid. It is against this background that this study seeks to investigate the relationship between
crude oil price and economic growth in Ghana employing the newly developed autoregressive
distributed lag (ARDL) approach to cointegration.
The rest of the paper is organised as follows. Section 2 explores the relationship
between crude oil price and the Ghanaian economy with specific emphasis on fuel subsidies. In
section 3 we examine the theoretical and empirical literature on oil price and economic growth.
Section 4 goes further to present the sources of data and methodology used for the study.
Section 5 discusses the results and presents summery and conclusions of the study while
section 6 presents the recommendations and policy implications of this study.

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CRUDE OIL PRICE AND THE GHANAIAN ECONOMY

Ghana’s macroeconomic performance has been associated with high and low points over the
years. The period between 1973 and 1983 can be described as the dark years of the Ghanaian
economy. The country within this period experienced a downward spiral in economic
growth,high rates of inflation, shortage of goods and services among others. Industrial output
during this period fell by 12.5%, budget deficit increased by 44.94%, total money supply climbed
to over 500% cumulatively with inflation rate reaching 117% by 1977 (Hutchful, 2002; Fosu and
Aryeetey, 2008). This period as indicated earlier coincided with the first two oil price shocks
experienced by the world economy as well as periods of political instability and economic
mismanagement in Ghana.
Huge budget deficits in the past have been attributed to the provision of fuel subsidies by
the government and this tend to have some negative effect on the Ghanaian economy. Periods
of poor economic growth in the country was usually preceded by increases in the price of crude
oil and huge fiscal deficits in the economy. Ocran (2007) maintains that, a major cause for the
huge fiscal deficits can be attributed to failure of the government to adjust domestic price of
petroleum products to reflect the increase in the world price of crude oil.

Figure 1 Domestic Price of Oil and World Price

160

140

120

100

80

60

40

20

0
2009M03

2010M01

2011M11
2008M01
2008M03
2008M05
2008M07
2008M09
2008M11
2009M01

2009M05
2009M07
2009M09
2009M11

2010M03
2010M05
2010M07
2010M09
2010M11
2011M01
2011M03
2011M05
2011M07
2011M09

2012M01
2012M03

Domestic Price World Price

.
Source: National Petroleum Agency and Global Economic Monitor (GEM) Commodities (2014)

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Similarly, a report by the Centre for Policy Analysis (CEPA) (2007) indicated that the public
expenditure and currency crises of 2000 which continued into 2001 and 2002 was as a result of
the continual increase in the international price of crude oil and the failure of government to
pass it on to the consumer. Thus, the decision by the government to absorb increases in crude
oil prices through subsidies rather than allowing for automatic price adjustments meant that the
government expenditure exceeded its targets in those years. The cost of crude oil subsidies
rose to about 2% of GDP by 2002. In January 2003, the government introduced a pricing
formula that linked domestic prices to world prices in order to reduce the burden on the
government in terms of subsidies. However for political expediency, the automatic adjustment
was deserted and further increases in the world price of crude oil were not passed on to
consumers but rather absorbed by the government through its expenditure. Despite high world
oil prices that rose above $50.00 in 2004, the government maintained the low price and ended
up subsidizing crude oil products to the tune of $200 million, representing 2.5% of GDP (Ocran,
2007). The government spends an average of $432 million annually on the subsidization of
petroleum products in the country. Utility and fuel subsidies cost the government an amount of
GH¢809.0 million in 2012, with an additional amount of GH¢955.8 million due to be paid in 2013
(Ministry of Finance and Economic Planning, 2013). Figure 1 below gives a graphical view of
the variations in the world price of crude oil and the price that is paid by domestic consumers in
Ghana. As indicated in Figure 1, the prices paid by Ghanaians with regards to oil is far below
the world price and this gives an indication of the amount that is paid by the government in the
form of oil price subsidies.
It is important to mention that, the option to raise the subsidy, although politically
popular, could also have adverse effects on the growth prospects of the economy. Large
subsidies on domestic petroleum price may redirect public expenditure away from more
productive expenditures or can contribute to unsustainable budget deficits. A rise in a subsidy is
obviously an increase in government spending. If, as a reaction to higher government
expenditure, taxes are not raised or other government expenses cut down to balance the actual
trend, the government will have to face a higher budget deficit.
Generally, governments of developing countries are faced with three possibilities to
finance their expenditure. These are, to print additional currency, to issue public debt or borrow
from domestic financial institutions and through foreign borrowing. The Government of Ghana
has in the past financed subsidies through borrowing from the domestic financial marketor
through the printing of money. Borrowing from the domestic financial market may result in the
crowding out of private investment and may adversely affect growth of the economy. Also the
printing of additional currency may increase inflationary pressures on the economy. Akçay et al.

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(2002) argue that, even when a central bank does not monetise the deficit, adjustments in the
private sector to higher deficit policies may very well lead to inflation. According to the Bank of
Ghana (2004), the predicament of an oil price surge for central banks is that it is often
problematic from a stabilisation policy viewpoint, in the sense that higher oil prices not only push
up inflation (thus calling for a rise in interest rates), but also dampen growth (necessitating rates
to be lower than otherwise). Hence there is the need to examine the relationship between crude
oil price increases and the growth of the Ghanaian economy given the level of subsidies on the
product in terms of for the effect of fiscal policy response.

REVIEW OF RELATED LITERATURE

The relationship between oil price and economic growth has received a plethora of theoretical
and empirical research over the past decades but have however focused largely on the on USA
and other developed economies of the world. Earliest theoretical works on the relationship
between crude oil price and economic growth includes works done by Rasche and Tatom
(1977), Bernanke (1983) Finn (2000) and more recently Hamilton (2009).
Rasche and Tatom (1977) argued that oil price fluctuations can be an important indicator
of a supply-side shock that reduces the potential output of an economy. Thus a rise in the price
of crude oil indicates an increase in the scarcity of energy, which is a basic input of production.
Consequently, even if all resources in an economy are fully employed and efficiently used, the
potential to produce is reduced because in economic terms, crude oil has become scarcer. The
growth of output and productivity are therefore reduced. Bernanke (1983) elaborated on the
capital equipment utilisation hypothesis. Bernanke (1983) demonstrated in a partial equilibrium
model that oil price shocks would tend to reduce value added, because firms will defer
irreversible investment decisions as they endeavour to find out whether the increase in oil price
is transitory or lasting. Thus, producers will find it more and more desirable to postpone
permanent investment decisions when they are uncertain about future crude oil price changes.
Such decisions is also likely to negatively affect the growth of output of an economy. Finn
(2000) also formulated a model with perfectly competitive markets, but integrated energy as an
essential input for the utilization of capital. The model considers variations in the utilisation rates
for productive capital as a function of energy use. This creates an indirect channel (that works
through capital stock) and the usual direct production function channel, for channelling the
impact of fluctuations in energy usage to the economy. Crude oil price increase or shock causes
sharp, simultaneous declines in energy use and capital utilisation. The reduction in the use of
energy works through the representative firm’s production function directly; this causes a
reduction in output and labour’s marginal productivity. Based on a survey of literature, Hamilton

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(2009) maintains that a key mechanism through which oil price shocks affects an economy is
through disruption in the expenditure of consumers and firms on non-oil goods and services.
However, if this disruption does not occur, the effects of an oil price hike on the economy will be
governed by the factor share argument.

Figure 2: Transmission channels of oil price changes

Source: Adapted from Chuku et al. (2010) and Bhanumurthy et al. (2012)

Various channels through which changes in the price of crude affects the growth of an economy
have been identified in the literature on crude oil and economic growth. Channels identified in
the literature includes the supply and side effect, inflation effect, and the real balance effect
(Brown & Yücel, 2002: Jiménez-Rodríguez & Sánchez, 2005: Chuku ,Effiong, & Sam, 2010 and
Bhanumurthy, Das, & Bose, 2012). Figure 2 illustrates the channels of transmission from oil
price increase to growth of GDP.

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Assuming that the demand for crude oil is inelastic to changes in the price level, an increase in
the international price of oil for example, will translate to higher import bill for net oil importing
economies, ceteris paribus. This situation will result in a higher trade deficit and consequently
cause a deterioration of the country’s current account balance. This will eventually result in
lower economic growth rates. This channel (Import Channel) is indicated in Figure 2 by the link
from international oil price to the current account balance to GDP.
The second channel through which an increase in the international price of oil can affect
an economy is through an increase in the domestic price of the commodity (the Price Channel).
For most developing countries, not all the increase in the international price of crude oil is
passed on to domestic consumers of the commodity. The government in the form of fuel
subsidy usually absorbs part of the price increase. This is indicated in Figure 2 by link between
oil subsidy and pass-through ratio. Similarly, due to changes in the terms of trade that is likely to
occur as a result of the oil price increase, the country’s exchange rate will be affected and this is
likely to affect the prices of commodities in the domestic market. This is also indicated by the
link between the exchange rate and pass-through ratio. This implies that only a portion of the
price increase (Pass-Through Ratio) will be passed onto the domestic market. The proportion
that is passed onto the domestic market will cause a rise in the level of inflation and
subsequently lead to a rise in the cost of production. This results in a reduction in profit levels
and consequently, lead to a reduction in investment and employment and GDP growth. In
Figure 2, this is indicated by the link from international oil price to inflation, producer price index
(PPI), investment to long-term capacity utilization and interest and then to GDP.
On the demand side, high oil price causes prices of consumer goods to increase. Real
money supply falls as demand for money also increases. This leads to a rise in interest rates.
Monetary authorities may also respond to the increase in price levels by tightening monetary
policy (raising interest rates). High interest rates discourage investment and this tends to affect
GDP as indicated in Figure 2.
In the absence of complete pass through (which is the case for most African countries as
indicated by African Development Bank (2009)), an oil price increase will raise subsidy on oil
and for that matter, on government expenditure. It is important to mention, however that, most
oil price increases are not predictable, hence oil price subsidies may result in budget deficits. If
the deficit is financed from domestic sources (through borrowing from the domestic financial
market), then it is likely that, it leads to the crowding out effect. This situation is indicated by the
link between fiscal deficit and interest rates. Thus, government borrowing from domestic
sources to finance the fuel subsidy will lead to high interest rate and consequently cause
investment levels to fall.

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Empirical Literature
Although, a number of empirical studies on the relationship between crude oil price and
economic growth exist, most of such studies have largely focused developed economies. Very
little empirical literature exist on net oil importing economies in sub-Sahara Africa. More so, very
little exist on an emerging oil exporting countries like Ghana in the literature. The relationship
between crude oil price and economic growth varies depending on a country’s sectorial
composition, institutional structures, and macroeconomic policies among others (Chuku et al.
2010).
Studies focusing on developed economies (see for e.g.Hamilton J. D., 1983: 1996: 2010;
Lee, Ni and Ratti 1995; Hooker 1996; Jimenez-Rodrigues and Sanchez 2005; Schmidst and
Zimmermann, 2007; Filis and Chatziantoniou 2013) have revealed that, crude oil price
increases tends to have adverse effect on industrial output and economic growth. Nevertheless,
they all concluded that this relationship has not been stable for these countries over time. The
unsteady relationship that had been perceived in the literature was confirmed in a study by
Blanchard and Gali (2007) who compared the present response of inflation and output to oil
price shocks in a group of developed economies to those in the 1970s. Blanchard and Gali
(2007) concluded that the main cause behind the weak responses of economies in recent years
is smaller energy intensity, a more flexible labour market and improvements in monetary policy.
On the other hand, studies on the crude oil price economic growth relationship for
developing economies have reported varied results. Chang and Wong (2003) examined the
effects of oil price fluctuations on the Singaporean economy and found an insignificant negative
relationship between oil price shocks and Singapore’s gross domestic output, inflation and
unemployment rate. On the contrary, studies by Olomola (2006), Akpan (2009) and Oriakhi and
Osaze (2013) have all found a positive relationship between oil price increases and growth of
output in Nigeria (possibly because Nigeria is a net exporter of crude oil), studies by Wakeford
(2006), and Bouzid (2012) have all found a negative relationship between oil price and
economic growth for South Africa and Tunisia respectively.
Focusing on studies on Ghana, Jumah and Pastuszyn (2007) investigated the
relationship between oil price shocks and monetary policy in Ghana for the period 1965 to 2004.
The objective of the study was to examine the relationship between the world price of crude oil
and aggregate demand in Ghana via the interest rate channel by means of cointegration
analysis. The study did not identify a direct significant relationship between output and crude oil
price changes, however, the study found that the international price of crude directly affected
the price level which tends to negatively affect real output. The results also indicated that
monetary policy is initially stilled in response to an increase in the price of oil in order to lessen

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any growth effects but at the cost of higher inflation. The resultant higher inflation, however
stimulates a further tightening of monetary policy. In addition, the output does not revert quickly
to its initial level after an oil price shock, but declines over an extended period.
Similarly Tweneboah and Adam (2008) estimated a vector error correction model to
explore the long run and short run linkages between world crude oil price and monetary policy in
Ghana for the period 1970:1 to 2006:4. The results of the study indicated that there is a long run
relationship between oil price, domestic price level, GDP, exchange rate and interest rate in
Ghana in which oil price positively impact the price level while negatively impacting output. The
study also revealed that an unexpected oil price shock is followed by an increase in inflation rate
and a decline in output in Ghana. On the response of interest rate to a rise in the price of oil,
Tweneboah and Adam argued that monetary policy has in the past been with the purpose of
reducing any growth consequences of oil price shocks but at the cost of higher inflation.
The problem with these two studies is that, both failed to examine the intervening effect
of fiscal policy on the relationship between oil price and the growth of output in Ghana. Fiscal
policy response have however been identified by Bhanumurthy, et al.(2012) as a major channel
through which changes in the international price of crude oil affects the growth of most
developing countries. In Ghana, government spends an average of US$432 million year on fuel
subsidies only, this is likely to have a significant impact on the relationship between oil price and
economic growth in Ghana. In addition, these two studies used the VAR framework in their
estimations, Kilian and Vigfusson (2011) demonstrated that oil price VAR models are
fundamentally misspecified and this renders the parameter estimates inconsistent and inference
invalid. The contribution of this study to the existing literature on the relationship between oil
price and the growth of output is that, it examine this relationship in the face of fuel subsidies
and also uses the ARDL approach to cointegration, identifying how fiscal policy influence the
relationship between oil price and economic growth and converting international price of crude
oil into the domestic currency.

METHODOLOGY
Sources of Data
The study employed annual dataset from 1967-2012 to investigate the existence of long run
relationship between crude oil price and economic growth as well the short-run dynamics in the
case of Ghana.Data on the main variable of interest (GDP, Consumer Price Index, Money
Supply, Labour force, Investment, Government expenditure and Exchange rate) was obtained
from the World Development Indicators 2013 edition of the World Bank. Data on international
crude oil prices was obtained from the BP statistical review 2013.

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Variables of choice
The following set of potential determinants of economic growth are derived from a survey of
existing theory and empirical literature and used in our study: International crude oil price,
Government expenditure, an interaction between oil price and government expenditure,
consumer price index, money supply, exchange rate, labour supply and investment. Their
definitions and measurements are discussed in turns.

International Price of Crude oil

From the literature crude oil price is usually hypothesized to affect economic growth as a basic
input of production (Jiménez-Rodríguez & Sánchez, 2005). In order to capture the effect of oil
price on the growth of the Ghanaian economy, the study used Brent Crude oil prices provided
by the BP statistical review. Since the increase in crude oil prices as indicated in the literature
adversely affects investment and output, a negative relationship between oil price and economic
growth is expected to be negative.

Government Expenditure
Government expenditure refers to general government spending at any level. Government
expenditure is used in this study as a policy variable and also as a major determinant of GDP.
Following the works of Easterly and Rebelo (1993); Doh-Nani (2011) and Ayibor (2012), the
ratio of government expenditure to GDP is used in the study. The Keynesian proposition
suggests that government expenditure will result in a rise in economic growth. Government
expenditure could however; result in a reduction in economic growth because of the crowding
out effect on private investment and the inflationary pressures it can lead to (Allen &
Ndikumana, 2000).

Interaction between oil price and government expenditure

For the purpose of this study and to control for the effect of fiscal policy response to changes in
the price of crude oil, the study interacted crude oil price with government expenditure. This
variable is included in the study to account for the fiscal policy channel through which oil price
fluctuations affect economic growth. The idea is that the impact of oil price fluctuations on the
Ghanaian economy may be influenced by government expenditure. West African Monetary
Agency (2008) and Bhanumurthy et al. (2012) noted that, high oil price results in higher
government spending in most developing countries due huge subsidies on petroleum products.
In Ghana for example, crude oil related products such as petrol, kerosene, liquefied petroleum
gas (LPG) and diesel are usually subsidized by government in order to reduce the possible

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effect of the high crude oil price on the poor. Due to the volatile nature of oil prices in the world
market, it becomes very difficult for the government to adequately plan its subsidies for the
commodity. For this reason the government may resort to domestic borrowing and this may
adversely affect the growth of the economy through high interest rates and crowding out of
investment.

Consumer Price Index

Consumer Price Index (CPI) is a measure that captures the changes in the price level of a
market basket of consumer goods and services purchased by the household. In this study, the
CPI is employed to control for the effect of high oil price on domestic goods and services. High
oil price may result in a rise in the general price level and this may be detrimental to economic
growth. Rapid increases in the general price level of the economy may result in uncertainty
about the future profitability of investment projects. This is because, higher prices of consumer
goods and services may dampen demand for goods and services in the economy and for this
reason, investors may resort to more conservative investment strategies than would otherwise
be the case, eventually leading to lower levels of investment and economic growth.

Money Supply
Money Supply is the total amount of monetary assets available in an economy at a specific time.
These comprise the sum of currency outside banks, demand deposits other than those of the
central government, as well as savings and other time deposits (World Bank, 2012). Following
Bernanke’s et al. (1997) influential paper, the present study includes money supply to capture
the influence of the monetary policy in response to changes in the price of crude oil. This is
because the central bank may respond proactively or reactively to fluctuations in oil price, which
in turn may affect the growth of the economy.

Exchange Rate
Demand for crude oil is relatively inelastic, hence the increase in oil prices increases
expenditure on imports by the oil importing country. This may result in an increase in the supply
in the local currency, thus weakening the currency relative to foreign currencies. The weakened
currency will increase the burden of payments and lead to balance of payment problems and
reduction in other imports, which will ultimately affect economic growth. Hence the inclusion of
exchange rate in the model. This study uses official exchange between the Ghana and the US
dollar as a measure of the exchange rate as was used by (Jiménez-Rodríguez & Sánchez,
2005). Exchange rate depreciation may lead to increase in the export of goods and services

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since goods produced in the economy become relatively cheap. This will have a positive impact
on economic growth. Depreciation of the domestic currency may also result in the reduction of
imports. However, the impact of exchange rate depreciation on the economy may depend on
the balance of payment position of the country.

Investment
The study follows the work of Fosu and Aryeetey (2008) and uses Gross fixed capital formation
as a proxy for investment in this study. Gross fixed capital formation is defined as the total value
of additions to fixed assets by domestic enterprises, less disposals of fixed assets during the
year, Plus additions to the value of non-produced assets such as discoveries of mineral
deposits, plants, machinery, and equipment purchases; and the construction of infrastructure
and commercial and industrial buildings (Baafi, 2010). Investment is included in the model
because, fluctuations in crude oil prices lead to a rise in the level of uncertainty which
subsequently results in the deferral of irreversible investment which in turn affect real GDP
growth. It is important to note however that high rate of investment results in high economic
growth (Barro & Sala-I-Martin, 1992).

Labour
Labour force (L) consists of the proportion of the population that is economically active. In this
study, the proportion of the total population aged between fifteen (15) years and sixty-five (65)
years who are active and productive is used as a proxy for the labour force. Jayaraman and
Singh (2007) argued that, there can be no growth without the involvement of labour. Solow
(1956) and Swan (1956) also recommended that labour force should be incorporated in the
growth model because of its impact on the work force, hence the inclusion of labour force in the
study. All things being equal, the higher the labour force the higher the supply of labour and
hence output.

Economic Reform
Economic Reform Dummy is used in the study to capture the possible influence of economic
reforms on economic growth in Ghana. It takes the value of zero (0) for the pre-economic reform
period and one (1) for periods after the economic reforms. (.I.e. Zero for the period 1970 to 1982
and one for the period 1983 to 2012). Since the purpose of economic reforms in Ghana was to
reduce Ghana's debts and to improve its trading position in the global economy as well as
restoring economic productivity at minimum cost to the government, this variable is expected to
have a positive impact on the growth of real GDP.

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Theoretical Model Specification

Since the objective of these study is to examine the relationship between growth of output and
oil price, following Rasche and Tatom (1977), the study adopts the Solow growth model as
specified below.

𝑌𝑡 = 𝑓 𝐾𝑡 , 𝐴𝑡 𝐿𝑡 , ℓ (1)

Where Y is output per capital, A is the total factor productivity or the Solow Residual, K is capital
stock,Lis labour force and ℓ is the naperian “e”Applying the Cobb-Douglas production function,
Solow stated the equation

𝛼 𝛽𝑡 ε t
𝑌𝑡 = 𝐾𝑡 𝑡 , 𝐴𝑡 𝐿𝑡 ℓ (2)

It is important to note that A is not fixed, but varies with different production functions based on
the factors being studied. This production function is widely used in literature; including Rasche
and Tatom (1977a); Ram and Ramsey (1989);Fosu (1990), and Fosu and Aryeetey (2008).
Apart from the traditional input of production, the model also assumes other conventional inputs.

Empirical Model
This section presents the empirical model to be estimated. From existing theory and empirical
literature, the following are the general working hypotheses: International oil price increases has
a negative effect on the growth of output, however this effect is reduce by expansionary fiscal
policy in response to the increase in the price of crude oil. Thus an increase in the price of crude
oil is likely to cause output levels in the economy to fall due to its effect on the cost of
production. However the study hypothesise that an increase in government expenditure in
response to the increase in the price of crude oil price will help reduce the negative impact of oil
price increases on the growth of output.
This study adopts the neoclassical growth model of Solow, which is specified in equation
(2) above. It is important to mention that, literature on economic growth indicates that, there are
multitudes of potential variables that can affect the TFP (A) in equation (2). However, owing to
the availability of data and following Rasche and Tatom (1977a), Ram and Ramsey (1989), the
study examined the following variables of interest resulting in:

𝐴𝑡 = 𝑓 𝑂𝑡 , 𝐺𝑡 , 𝐸𝑋𝐶𝑡 , 𝐶𝑃𝐼𝑡 , 𝑀𝑆𝑡 , 𝑂𝐺𝑡 , 𝐷𝑅𝑡 (3)

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This implies that :

𝛽 𝛽 𝛽 𝛽 𝛽 𝛽 𝛽
𝐴𝑡 = 𝑂𝑡 1 , 𝐺𝑡 2 , 𝐸𝑋𝐶𝑡 3 , 𝐶𝑃𝐼𝑡 4 , 𝑀𝑆𝑡 5 , 𝑂𝐺𝑡 7 , 𝐷𝑅𝑡 8 (4)

Where O is real crude oil price, G is government expenditure, EXC is bilateral exchange rate
between the Ghana cedi and the US dollar, CPI is consumer price index, MS is the money
supply, OG is the interaction between oil price and government expenditure, and DR is dummy
for economic reforms. Ghana under took an economic reform programme in 1983 which
resulted in some changes in the structure of the economy and hence we try to control for this
changes in the economy. By substituting equation (4) into (2) and by specifying an extended
Cobb-Douglas production function to represent the production of technology of an economy, the
study obtains;

𝛽 𝛽 𝛽 𝛽 𝛽 𝛽 𝛽 𝛽
𝑌𝑡 = 𝜂𝐾𝑡𝛼 , 𝑂𝑡 1 , 𝐺𝑡 2 , 𝐸𝑋𝐶𝑡 3 , 𝐶𝑃𝐼𝑡 4 , 𝑀𝑆𝑡 5 , 𝐿𝑡 6 , 𝑂𝐺𝑡 7 , 𝐷𝑅𝑡 8 ℓ𝜀 𝑡 (5)

By taking the logarithm of the variables involved in the equation (5):

𝑙𝑛𝑌𝑡 = ln 𝜂 + 𝛼𝑙𝑛𝐾𝑡 + 𝛽1 𝑙𝑛𝑂𝑡 + 𝛽2 𝑙𝑛𝐺𝑡 + 𝛽3 𝑙𝑛 𝐸𝑋𝐶𝑡 + 𝛽4 𝑙𝑛𝐶𝑃𝐼𝑡

+𝛽5 𝑙𝑛𝑀𝑆𝑡 + 𝛽6 𝐿𝑡 + 𝛽7 𝑙𝑛𝑂𝐺𝑡 + 𝛽8 𝐷𝑅𝑡 + 𝜀𝑡 𝑙𝑛ℓ (6)
Let ln 𝜂= 𝛽0 and 𝑙𝑛ℓ=1, equation (6) can therefore be written as:
𝑙𝑛𝑌𝑡 = 𝛽0 + 𝛼𝑙𝑛𝐾𝑡 + 𝛽1 𝑙𝑛𝑂𝑡 + 𝛽2 𝑙𝑛𝐺𝑡 + 𝛽3 𝑙𝑛 𝐸𝑋𝐶𝑡 + 𝛽4 𝑙𝑛𝐶𝑃𝐼𝑡 + 𝛽5 𝑙𝑛𝑀𝑆𝑡 + 𝛽6 𝐿𝑡 + 𝛽7 𝑙𝑛𝑂𝐺𝑡
+ 𝛽8 𝐷𝑅𝑡 + 𝜀𝑡 (7)

The coefficients 𝜶, 𝜷𝟏 , 𝜷𝟐 , 𝜷𝟑 , 𝜷𝟒 , 𝜷𝟓 , 𝜷𝟔 , 𝜷𝟕 , 𝒂𝒏𝒅 𝜷𝟖are the elasticities of the respective variables,

𝜷𝟎 is the drift component, t denotes time and  is the error term.

Since the emphasis of this study is to examine the relationship between crude oil price and
economic growth, the suitable technique to adopt is a cointegration analysis and error correction
modeling. Consequently, the study employed the Autoregressive Distributed Lag (ARDL)
approach by Pesaran and Pesaran (1997), Pesaran and Shin (1999) and Pesaran, Shin, and
Smith (2001). This approach has some econometric advantages over the other cointegration
techniques. First, the ARDL technique does not require pre-testing of the series to ascertain
their order of integration since the test can be conducted irrespective of the other of integration.
In addition, ARDL modeling incorporates adequate number of lags to capture the data
generating process from general to specific modeling framework (Laurenceson and Chai, 2003

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as cited in Shrestha and Chowdhury, 2005). Furthermore, the bounds test approach to
cointegration gives more robust results in small samples than the Johasen approach. Thus, the
ARDL approach to cointegration is more efficient in finite samples compared with the Johansen
approach that requires large data samples for one to get a valid result (Pesaran & Shin, 1999).
Also, the problem of endogeneity is addressed in this technique. Pesaran and Shin (1999),
argued that modeling the ARDL with the appropriate lags will adjust for both serial correlation
and endogeneity problems. Jalil, Ma, and Naveed, (2011) contend that endogeneity is less of a
problem if the estimated ARDL model is free of serial correlation. The problem of endogeneity is
primarily important since the causal relationship between financial development and economic
growth cannot be ascertained beforehand. The use of the ARDL approach is further justified by
the relatively small sample size of our dataset covering annual dataset from 1967 to 2012. The
ARDL approach is therefore, considered to be very suitable for analysing the underlying
relationship. Hence, we specify the ARDL representation of equation (3) as:

Δ𝑙𝑛𝑌𝑡 = 𝛽0 + 𝜆𝑙𝑛𝑌𝑡−1 + 𝛼𝑙𝑛𝐾𝑡 + 𝛽1 𝑙𝑛𝑂𝑡 + 𝛽2 𝑙𝑛𝐺𝑡 + 𝛽3 𝑙𝑛𝐸𝑋𝐶𝑡 + 𝛽4 𝑙𝑛𝐶𝑃𝐼𝑡 + 𝛽5 𝑙𝑛𝑀𝑆𝑡

𝑃 𝑃 𝑃 𝑃

+𝛽6 𝐿𝑡 + 𝛽7 𝑙𝑛𝑂𝐺𝑡 + 𝛾Δ𝑙𝑛𝑌𝑡−𝑖 + 𝜑Δ𝑙𝑛𝐾𝑡−𝑖 + 𝜙1𝑖 Δ𝑙𝑛𝑂𝑡−𝑖 + 𝜙2𝑖 Δ𝑙𝑛𝐺𝑡−𝑖

𝑖=1 𝑖=1 𝑖=1 𝑖=1
𝑃 𝑃 𝑃 𝑃

+ 𝜙3𝑖 Δ𝑙𝑛𝐸𝑋𝐶𝑡−𝑖 + 𝜙4𝑖 𝛥𝑙𝑛𝐶𝑃𝐼𝑡 −𝑖 + 𝜙5𝑖 Δ𝑙𝑛𝑀𝑆𝑡−𝑖 + 𝜙6𝑖 Δ𝐿𝑡−𝑖 _

𝑖=1 𝑖=1 𝑖=1 𝑖 =1
𝑃

+ 𝜙7𝑖 Δ𝑙𝑛𝑂𝐺𝑡−𝑖 + 𝜐𝑡 (8)

𝑖=1

Where denotes the first difference operator, P is the lag order selected by the Schwarz
Bayesian Criterion (SBC), is the drift parameter while is the error term which is .
The parameters and are short-run parameters and are the long-run
multipliers. All the variables are defined as before. The study estimated equation (4) with the
bounds test by employing the OLS method, which is normally the first procedure in the ARDL
model. The F-test was used to test for the presence of long-run relationship among the
variables in equations (8). The null hypotheses of no long-run relationship among the variables
in equations (8) is tested against the alternative hypotheses of a long- run relationship as
follows:

𝐻0 : 𝛼 = 𝛽1 = 𝛽2 = 𝛽3 = 𝛽4 = 𝛽5 = 𝛽6 = β7 = 0
𝐻1 : 𝛼 ≠ 𝛽1 ≠ 𝛽2 ≠ 𝛽3 ≠ 𝛽4 ≠ 𝛽5 ≠ 𝛽6 ≠ 𝛽7 =0

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The existence of cointegration among the variables under consideration is tested based on the
F-statistic. Given that, the asymptotic distribution of the F-statistic is non-standard without
considering the independent variables being I (0) or I (1), Pesaran and Pesaran (1997) have
provided two sets of critical values for the different numbers of regressors (k), and whether the
ARDL model contains an intercept and/or trend. Therefore, the calculated F-statistic is
compared with these sets of critical values developed on the basis that the independent

variables are I(d) (where 0  d  1). The lower critical bound assumes that all the variables are I
(0), meaning that there is no cointegration among the variables, while the upper bound assumes
that all the variables are I (1). So if the calculated F- statistic falls outside the upper critical
value, then a null hypothesis of no cointegration will be rejected regardless of whether the
variables are I (0) or I (1) implying a long- run relationship among the variables. Provided that
cointegration has been established from the ARDL model, the long run and error correction
estimates of the ARDL and their asymptotic standard errors are then obtained.

𝑃 𝑃 𝑃 𝑃

ln 𝑌𝑡 = 𝜇 + 𝜆𝑙𝑛𝑌𝑡−𝑖 + 𝛼𝑙𝑛𝐾𝑡−𝑖 𝛽1 𝑙𝑛𝑂𝑡−𝑖 + 𝛽2 𝑙𝑛𝐺𝑡−𝑖 +

𝑖=1 𝑖 =1 𝑖=1 𝑖=1
𝑃 𝑃 𝑃 𝑃 𝑃

𝛽3 𝑙𝑛𝐸𝑋𝐶𝑡−𝑖 + 𝛽4 𝑙𝑛𝐶𝑃𝐼𝑡−1 + 𝛽5 𝑙𝑛𝑀𝑆𝑡−𝑖 + 𝛽6 𝐿𝑡−1 + 𝛽5 𝑙𝑛𝑂𝐺𝑡−𝑖

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1

+𝛽8 𝐷𝑅𝑡 + 𝜐𝑡 (9)

The ARDL error correction representation of the series is also estimated as

𝑃 𝑃 𝑃 𝑃

Δ𝑙𝑛𝑌𝑡 = 𝜙0 + 𝛾𝑙𝑛𝑌𝑡−𝑖 + 𝜑Δ𝑙𝑛𝐾𝑡−𝑖 + 𝜙1𝑖 Δ𝑙𝑛𝑂𝑡−𝑖 + 𝜙2𝑖 Δ𝑙𝑛𝐺𝑡−𝑖 +

𝑖=1 𝑖=1 𝑖=1 𝑖=1
𝑃 𝑃 𝑃 𝑃

𝜙3𝑖 Δ𝑙𝑛𝐸𝑋𝐶𝑡−𝑖 + 𝜙4𝑖 𝛥𝑙𝑛𝐶𝑃𝐼𝑡−𝑖 + 𝜙5𝑖 Δ𝑙𝑛𝑀𝑆𝑡−𝑖 + 𝜙6𝑖 Δ𝐿𝑡−𝑖 +

𝑖=1 𝑖=1 𝑖=1 𝑖=1
𝑃

𝜙7𝑖 Δ𝑙𝑛𝑂𝐺𝑡−𝑖 + 𝜙8 𝐷𝑅𝑡 + 𝜉𝐸𝐶𝑇𝑡−1 + 𝜖𝑡 (10)

𝑖=1

Where is the speed of adjustment of the parameter to long-run equilibrium following a shock to
the system and ECTt-1 is the residuals obtained from equations (9). The coefficient of the lagged
error correction term is expected to be negative and statistically significant to further confirm
the existence of a cointegrating relationship among the variables in the model.

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EMPIRICAL RESULTS AND DISCUSSIONS

Unit roots test results
Before carrying out the ARDL or Bounds test to cointegration, and the Granger-causality test,
unit roots test was first conducted in order to examine the stationarity properties of the variables
in the study. While the ARDL approach to cointegration does not necessitate the pretesting of
the variable for unit roots, it is imperative to perform unit roots test to verify whether the
variables are not integrated of an order higher than one, to avoid spurious results. This is
necessary, because the computed F-statistics provided by Pesaran et al. (2001) are not valid in
the presences of I (2) variables. The results from the unit roots test indicates that all the
variables of interest are integrated of order one (I(1)).

Table 1: Test for order of Integration (using Eviews)

ADF-Test Statistic (with Intercept) PP-Test Statistic (with Intercept)
Variable Levels First Difference Levels First Difference
LY 3.4048 -3.8728*** 3.2281 -3.8902***
LO -2.1238 -6.2498*** -2.1286 -6.2198***
LCPI -1.5035 -3.7692*** -1.1113 -3.8212***
LREER 3.1227 -4.0142*** -1.3314 -3.6420***
LMS 0.1587 -5.6186*** 0.1451 -5.5939***
LG -0.4633 -4.9087*** -0.6089 -4.7527***
LOG -0.9269 -5.3184*** -0.9367 -5.3525***
LL -1.5039 -5.0169*** -1.7346 -3.6706***
LK -0.2445 -6.2163*** -0.1536 -6.2277***

Bounds Test
The study then proceeded to estimate equation (8) in order to examine the long-run
relationships among the variables. Due to the fact that the sample size for the study is small and
given that the study employed annual data, a lag length of two (2) is used in the bounds test.
Pesaran and Shin (1999) proposed that, a maximum lag length of two (2) for annual data should
be used in the bounds testing approach to cointegration. After the determination of the lag
length, the F-statistic that is computed within the framework of the Unrestricted Error Correction
Model (UECM) was compared with the lower and upper critical values in Pesaran and Pesaran
(2009). Table 2 reports the bounds test results for Real GDP (LY). From Table 2, the F-statistic
for the model with Real GDP (LY) as the dependent variable isFLY(.) = 3.1651. It exceeds the
upper critical bound at ten percent significance level. This means that the null hypothesis of no
cointegration among the variables in equation (8) is rejected. This suggests the existence of a
long-run relationship between economic growth and its explanatory variables.

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Table 2: Bounds test for the existence of cointegration

Critical Value Bounds Intercept with no trend 90% Level 95% Level 99% Level
I(0) I(1) I(0) I(1) I(0) I(1)
K=8 1.956 3.085 2.272 3.447 2.848 4.126
Dependent Variable F-Statistic
F(LY) = F(LY | LO, LCPI, LEXC, LMS, LG, LOG, LLF, LK) 3.1651
Note: K is the number of regressors.
Estimated from WDI (2012) and BP Statistical Review data using Microfit 4.1 package; the critical values
are obtained from Appendix B Case II, statistical tables of Pesaran and Pesaran (2009).

Long run estimates

Since the results of the cointegration analysis indicate the existence of a long run relationship
between economic growth and the explanatory variables in the study, the study proceeded to
estimate the long run impact of the explanatory variables on economic growth using the ARDL
framework. Long run estimates of the ARDL model in Table 3 reveal that, crude oil price,
consumer price index and the interaction term (oil price and government expenditure) have a
negative impact on the growth of output at ten (10), one (1) and five (5) percent significance
level respectively. This result is in line with the expectations of the study.

Table 3: Long Run Model

Variables Coefficient Standard Error
LO -0.0390* 0.0211
LG 0.2782** 0.1069
LREER 0.0344* 0.0170
LCPI -0.0044*** 0.0009
LMS 0.0030 0.0499
LOG -0.0648** 0.0270
LK 0.0537** 0.0229
L 0.0359** 0.0142
DR -0.0104 0.0678
C 19.6846*** 2.2921
Note: ***, ** and * denotes significance at 1%, 5% and 10% respectively
Estimated from WDI (2012) and BP Statistical Review data using Microfit 4.0 package

On the other hand, government expenditure, bilateral exchange rate, labour force and capital
stock, had a positive impact on the growth of output as expected. However, the long run
estimate of the money supply was insignificant, though positive as expected. It is important to
note that Table 3 presents a regression with an interacting term as postulated by Wooldridge
(2009). Wooldridge (2009) argues that a regression equation that involves an interaction term
must be interpreted with extreme care. This is because the marginal effects of the interacted

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variables must not be considered in isolation. The marginal effect of oil price on real GDP, for
example, must be interpreted by taking into account the effect of government expenditure as
well. The long-run estimates indicate a negative relationship between crude oil price and growth
of output. The coefficient of crude oil price (LO) in Table 3 is negative and statistically significant
at ten percent. From Table 3, the effect of oil price on growth of output is given by,

𝑑𝐿𝑌
= −0.039046 − 0.064818𝐿𝐺 (11)
𝑑𝐿𝑂

To get the actual effect of oil price increases on the growth of output, the study follows
Wooldridge (2009) and plugs in the mean value of government expenditure, as indicated in
appendix 1, into equation (11).

𝑑𝐿𝑌
= −0.039046 − 0.064818 20.83210 = −1.3893 (12)
𝑑𝐿𝑂

This means that, one percent increase in the price of crude oil will cause real GDP to fall by
1.3893 standard deviations from the mean value of real GDP. This implies that government
efforts to reduce the impact of high oil prices on the consumers through increased subsidies
reinforces the negative impact of oil price increases on the growth of output. This implies that,
the study fails to accept the null hypothesis that increases in government expenditure in
response to oil price increasing has a reducing effect on the negative effect of oil price increase
on the growth of output in the Ghanaian economy. One possible reason for this is that, large
subsidies (via increase in government expenditure) on domestic petroleum price may redirect
public expenditure away from more productive expenditures which may have adverse effect on
the growth of the economy. In addition oil price changes cannot be easily predicted due to its
volatile nature, hence it becomes difficult for the government to adequately plan for the subsidy
to pay in a particular year. This situation may compel government to shift resources from other
sectors of the economy to help finance the differences in subsidies that may occur and as such
it tend to have adverse effect on these sectors of the economy which ultimately affect the
growth of the Ghanaian economy. This result is consistent with theoretical exposition put
forward by Bernanke (1983) and Finn (2000). According to economic theory, an increase in
crude oil price tends to reduce capital utilization and this causes output to fall. In Ghana, crude
oil serve as a major source of energy for the country’s productive sectors (Armah, 2003), hence
the result obtained in this study suggests that, high crude oil prices adversely affect the

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country’s output level. The result is also consistent with previous empirical studies on the
relationship between crude oil price and output. Bouzid (2012) found a statistically significant
negative relationship between crude oil price and economic growth in Tunisia. Also, Kiani
(2011), in a study of the impact of high oil prices on the growth of the Pakistani economy found
that crude oil price increases adversely affected output of the economy.
Government expenditure (LG) which was used as policy variable, was statistically
significant and exerted a positive impact on output. To identify the actual effect of government
expenditure on the growth output, the study estimated equation (13) derived from table 3,

𝑑𝐿𝑌
= 0.27816 − 0.064818𝐿𝑂 (13)
𝑑𝐿𝐺

From Appendix 1, the study substituted the mean value of oil price into equation (13)

𝑑𝐿𝑌
= 0.27816 − 0.064818 3.623787 = 0.0433 (14)
𝑑𝐿𝐺

This implies that, a percentage increase in government expenditure will cause Real GDP to
increase by 0.0433 standard deviations from the mean value of Real GDP. This result indicates
that government expenditure is an important channel through which the economy can achieve
increased output. The result also implies that the involvement of the government in the domestic
determination of crude oil prices reduces the positive effect of government expenditure on the
growth of output. This result is consistent with the Keynesian proposition. The result is also in
line with the findings of several empirical studies. Bhanumurthy, Das, and
Bose (2012) found a positive relationship between government expenditure and output
for India. Swaray (2011) and Ayibor (2012) all found a positive relationship between output and
government expenditure for Sierra Leone and Ghana respectively. The long run estimates also
indicates that labour force capital stock are important channels through which the country can
achieve growth of GDP. However, the study shows that high price level in the country is inimical
to the growth of GDP.

Short run dynamics

Once the long-run relationship among the variables has been established within the framework
of the ARDL approach to cointegration, the study further estimates the short run relationships.
From Table 4, the coefficient of the lagged error correction term (ECMt-1) is negative and

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significant at one percent significance level. This actually confirms the existence of a
cointegrating relationship among the variables in the model. The ECM represents the rate of
adjustment to restore equilibrium in the dynamic model after a disturbance. The coefficient of
the error correction term is -0.46296. This implies that, about 46 percent of the deviations from
the long-term growth of output caused by previous year’s shocks converges back to the long run
equilibrium in the current year. The result shows that, the speed of adjustment is relatively low in
the model.
The short run coefficient of crude oil price is positive and significant at 5% significance
level. This is consistent with the argument of Kliesen (2008), who indicated that the price
elasticity of demand for crude oil is low in the short-term as such firms and consumers are
unable to change their production or consumption patterns instantaneously after price changes
have occurred. However, considering the fact that oil price increases over the past decades has
been accompanied by increases in government spending in the form of fuel subsidies, it is
important to identify the net effect of oil price increase on the growth of output, given the level of
government spending. The study follows the same procedure as indicated in the explanation of
the long-run effect. Hence the effect of oil price increase on economic growth in the short-run is
given by; 0.049737-0.030008 (20.83210) = -0.5754. This means that, an increase in the price of
crude oil will result in a reduction in economic growth by 0.5754 standard deviation from the
mean value of economic growth. The result implies that in the short-run, the increasing effect of
oil price increases on the growth of output in the Ghanaian economy declines as the
government increases its spending in response to the increases in the price of crude oil. This
result is consistent with results obtained by Gounder and Bartleet (2007).

Table 4: Error Correction Model

Variables Coefficient Standard Error
dLOt 0.0497** 0.0201
dLGt 0.1288** 0.0468
dLREERt 0.0159* 0.0077
dLCPIt -0.0020*** 0.0004
dLMSt 0.0014* 0.0007
dLOGt -0.0300** 0.0110
dLKt 0.0249** 0.0095
dLt 0.0166** 0.0074
DR -0.0048 0.0311
C 9.1133*** 1.5946
ECMt-1 -0.4630*** 0.0910
Note: ***, ** and * denotes significance at 1%, 5% and 10% respectively
Estimated from WDI (2012) and BP Statistical Review data using Microfit 4.0 package

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Government expenditure on the other hand exerted a positive effect on economic growth. The
short-run effect of government expenditure on economic growth is given by 0.12878-
0.030008(3.623787) = 0.0200.
Thus, a percentage increase in government expenditure will cause economic growth to
increase by 0.02 standard deviation from the mean of economic growth. Both the long and short
run results confirms the key role fiscal policy plays in promoting economic growth as indicated
by John Maynard Keynes’ The General Theory of Employment, Interest, and Money. Inflation
was negative and money supply was significant in the short run, however, its impact (0.0014) on
economic growth was relatively small.

Model diagnostics and stability tests

Hansen (1992) cautioned that estimated parameters of a time series data could differ over time.
Consequently, it is critical to conduct parameter tests in order to check for model
misspecification that may arise as a result of unstable parameters and subsequently lead to bias
estimates. Table 5 below shows the results for the model diagnostics and goodness of fit. The
results in Table 5, indicate an R-squared value of 0.70959, suggesting that about 71% of the
total variations in economic growth is explained by variations in crude oil price and the other
explanatory variables in the model. Table 3 also shows that the overall regression is significant
at one percent (F-Stat (10, 32) = 7.5745[0.000]). This implies that the explanatory variables in
the model are good predictors of economic growth. Furthermore, the results in Table 5 show
that there is no evidence of spurious regression as the Durbin-Watson (DW) is greater than the
adjusted R-squared. (R-Bar-squared). This result is confirmed by the test for serial correlation.
Also, table 5 indicates that the errors are normally distributed and the model passes the
Ramsey’s RESET for correct specification of the model as well as the white heteroscedasticity
test.
Finally, to check the stability of the coefficients of the model, the study employed the
CUSUM and CUSUMSQ of recursive residuals stability tests as suggested by Pesaran and
Pesaran (1997). According to Bahmani-Oskooee (2004), the null hypothesis for this test is that
the coefficient vector is the same in every period. The plot of the CUSUM and CUSUMSQ of
recursive residual stability test in the Appendix 2 indicates that all the coefficients of the
estimated model are stable over the study period since they are within the 5 percent critical
bounds.

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Table 5: Model Diagnostics

Diagnostics Diagnostics
2
R-Squared (R ) 0.70959 R-Bar-Squared 0.60654
S.E. of Regression 0.029467 F-Stat (10, 32) 7.5745[0.000]
Mean of Dependent Variable 0.035396 S.D. of Dependent Variable 0.046978
Residual Sum of Squares 0.026918 Equation Log-likelihood 97.5729
Akaike Info. Criterion 85.5729 Schwarz Bayesian Criterion 75.0057
DW-statistic 2.1161
Diagnostics LM-Version F-Version
2
Serial Correlation 𝜒𝐴𝑢𝑡𝑜 0.48688[0.485] F(1,30)= 0.34358[0.562]
2
Functional Form 𝜒𝑅𝐸𝑆𝐸𝑇 (1) 0.69466[0.405] F(1,30)= 0.49260[0.488]
2
Normality 𝜒𝑁𝑜𝑟𝑚 (2) 0.47037[0.790] Not Applicable
Heteroscedasticity 2
𝜒𝑊ℎ𝑖𝑡𝑒 (1) 0.25367[0.614] F(1,41)= .2433[0.624]
Estimated from WDI (2012) and BP Statistical Review data using Microfit 4.0 package

SUMMERY AND CONCLUSIONS

In this study we have estimated an ARDL model to explore the long run and short run
relationship between world crude oil price and Ghana’s economic growth for the period 1967 to
2011. The estimates of the ARDL model revealed that crude oil price, government expenditure,
real effective exchange rate, consumer price index, oil price-government expenditure, capital
stock and labour force have a significant effect on real GDP in the long run with oil price, CPI
and fiscal policy response having a negative effect on real GDP. The error correction term, was
negative and significant. This means that the economy will be able to recover from the previous
year’s shock. More specifically, the error correction term implied that, about 46.3 percent of the
previous year’s disequilibrium was adjusted in the current period.
Based on the results obtained in the study, the study concludes that increases in the
international price of crude oil are detrimental to the growth of the Ghanaian economy. The
effect of oil price increase on output in the long-run is however greater than the short run effect.
The results also implies the negative effect of oil price increases on the Ghanaian economy is
not reduced by increases in government spending in response to the oil price increases. This
means that fuel price subsidies does not really eliminate the negative impact of oil price hikes
on economic growth. Also, fiscal policy response to oil price increases has an adverse effect on
the economy.

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RECOMMENDATIONS AND POLICY IMPLICATIONS

Given the fact that increases in government expenditure over the years in response to oil price
increases has only resulted in reinforcing the negative effect of oil price on the Ghanaian
economy, the present study recommends the complete elimination of fuel subsidies in the
Ghanaian economy. The study however, recommends that taxes on the commodity should be
reduced and if possible completely removed. This is because removal of taxes will go a long to
reduce the cost of production of producers. More so, the removal of the oil price subsidy is likely
to reduce government borrowing, especially from domestic sources and this will reduce the
crowding out effect of government expenditure that is likely to cause a reduction in private
investment. Also, removal of subsidies will help reduce the allocative and productive
inefficiency.
Also given the fact energy is an important element to the growth of the Ghanaian
economy, searching for an alternative source of energy that is cheaper than crude oil could go a
long way to reduce the negative effect of oil price on the growth of the Ghanaian economy.

DIRECTION FOR FUTURE RESEARCH

The main aim of this study was to examine the relationship between crude oil price and
economic growth in Ghana. The study did not however consider specifically, the possible effect
of oil price fluctuations on the various sectors of the economy. Hence, it will be interesting for
future research to be focused on disaggregating the impact of crude oil on the economy by
considering the impact of such fluctuations on the main productive sectors of the economy
namely: industrial, agricultural and the services sectors. In other words looking at the impact of
oil prices on the various sectors of the economy separately would be worthwhile. This will help
in identifying the main channels (by sectors) through oil price fluctuations affects Ghana’s
economic growth.

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APPENDICES
1: Summary statistics
LY LK LO LG LREER LCPI LMS LL LOG
Mean 23.04 21.00 3.62 20.83 5.23 0.34 21.50 53.60 16.36
Median 22.89 20.86 3.60 20.68 4.98 1.09 21.32 52.79 17.40
Maximum 24.05 22.52 4.71 21.71 7.58 5.33 22.87 57.73 27.29
Minimum 22.52 19.26 2.34 19.72 4.43 -5.73 20.38 51.18 5.19
Std. Dev. 0.43 0.94 0.66 0.48 0.82 3.80 0.65 2.23 7.50
Skewness 0.73 -0.01 -0.29 0.04 1.39 -0.35 0.46 0.55 -0.13
Kurtosis 2.32 1.75 2.23 2.61 4.11 1.71 2.10 1.82 1.62
Jarque-Bera 4.83 2.95 1.72 0.30 16.75 4.05 3.11 4.93 3.70
Probability 0.10 0.23 0.42 0.86 0.00 0.13 0.21 0.19 0.16
Sum 1036.64 945.12 163.07 937.45 235.35 15.36 967.41 2412.18 736.16

Source: Estimated from WDI (2012) and BP Statistical Review data using Eviews 7
Note: Std. Dev. represents Standard Deviation while Sum Sq. Dev. represents Sum of Squared Deviation

2: Plot of cumulative sum and cumulative sum of squares of recursive residuals stability tests
Plot of Cumulative Sum of Recursive Residuals (using Microfit 4.1)

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals (using Microfit 4.1)

1969 1974 1979 1984 1989 1994 1999 2004 2009 2011

The straight lines represent critical bounds at 5% significance level

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