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Abstract

Despite the advocacy for climate change goals and encouragement for renewables, crude oil continues to dominate the energy markets. Crude oil price, however, is prone to global price fluctuations. Analyses of these effects on agricultural commodity markets in Ghana is neglected which this study seeks to answer. A nonlinear approach (NARDL) is employed in evaluating the effects of global oil price fluctuation on Ghana’s agricultural commodity market vulnerability, proxied by their producer prices. Different indicators are used since crude price fluctuations might have varying impacts on macroeconomic indicators in both magnitude and signs. Quarterly time series data are sourced from different institutions including data from the Bank of Ghana, FAO, and British Petroleum Statistical Review spanning from 1980: Q1 to 2019: Q4. Empirical results indicate that crude oil price fluctuations have different signs and magnitude for agricultural food commodities prices for both short- and long-runs. The findings of the study bring to bear how the agricultural activities in Ghana respond to variability in global crude oil prices. Policies mitigating changes in prices of animal products ought to be grounded on other issues instead of changes in crude oil prices. Also, programs such as “Planting for Food and Jobs” must be implemented in effectively.

JEL Classification: Q02; C32; E31; H23

Plain Language Summary

Crude oil still dominates our energy markets even though we want o mitigate climate change. We study the effect of changing crude oil on agricultural commodity markets in Ghana. We find that crude oil price fluctuations have diverse signs and effects on agricultural food commodities prices for both short- and long-runs. These show the agricultural activities in Ghana respond to variability in global crude oil prices. These call for policies to mitigate changes in the prices of agricultural commodities.

Keywords

agricultural commodity prices crude oil price fluctuations Ghana nonlinear approach vulnerability

Introduction

Although there is strategic global advocacy for achieving the climate change goals. In addition, the promotion and development of electric cars and the encouragement for sources of renewable energy, crude oil unequivocally dominates global energy demands and supplies are progressing well (Kriskkumar & Naseem, 2019). The petroleum industries in countries that produce oil and gas play significant contributions to economic growth. These important roles are mainly through generation of revenue and serving as linkages for the other sectors of the economy (Swe & Emodi, 2018). It is without doubt, that most emerging and petroleum producing economies, like Ghana, rely heavily on revenue from petroleum production for fiscal budgeting and socio-economic development aspirations. In Ghana, oil has become an essential energy source in recent times than it was in the previous times (Awunyo-Vitor et al., 2018; Lin et al., 2014). It is used extensively as fuel for transport, agricultural activities, and an essential material in the manufacturing industry (Etornam & Denis, 2015).

These indicate that crude oil prices could have a major influence on the economy through the revenue from crude oil. Also, in developing countries in Sub-Sahara Africa (SSA), crude oil prices are used as indicators for economic prospects (Hung, 2021), unemployment, rate of inflation, movement of currency as well as to determine political unrest levels (Kriskkumar & Naseem, 2019; Lang & Auer, 2020). In spite of these important roles played by crude oil in a petroleum dependent economy, it is susceptible to global price variability. Generally, increases in crude oil price are connected to economic growth of countries that export crude oil via the channel of generation of revenue, although it is not always the case (Kriskkumar & Naseem, 2019; Moshiri & Banihashem, 2012). The economic growth, because of crude oil prices increases, might even be weakened as crude oil prices hikes might deteriorate the economic circumstances that favour economic growth. This could be through exchange rate appreciation, making poor policies and cultivation of rent-seeking behavior (Kriskkumar, & Naseem, 2019; Moshiri & Banihashem, 2012). It, therefore, shows that there are chances that crude oil prices hikes, and plunges can have damaging effects on the economic activities of countries that export crude oil which could result in Dutch diseases. These shocks are often felt in developing economies and emerging oil-producing countries which Ghana is not exempted.

In recent times, fiscal budgeting has been threatened by exogenous factors such as low global oil prices and global pandemics including Covid-19 and Omicron which recently occurred (Hung, 2021; Hung and Vo, 2021). These factors cause restrictions on travelling (witnessed during the lockdowns) which could affect oil prices. These variabilities in world oil prices affect many aspects of the economy of oil producing countries (International Energy Agency, 2013). In Ghana’s first 10 years of oil production since discovery, the petroleum industry in Ghana has been experiencing a highly unpredictable crude oil prices settings ranging from US$47.54 to US$65 per barrel (Petroleum Commission Ghana, 2017). Figure 1 shows that the contribution to the budget is unstable. It falls steadily from 2014 up to 2017 and in 2020.

Figure 1.

Percentage share of oil revenue to budget.

The discoveries of crude oil in Ghana seems to have the potential to cause serious major economic crises (Buchberger, 2011). According to British Petroleum (2020), Covid-19 pandemic enormously affected the global economy and demand for and supply of energy through price deterioration. Ghana was also greatly affected as well. Ghana’s revenue from oil export declined due to instabilities in the economy of the world and the high unpredictability of prices of crude oil. According to Ghana’s Public Interest and Accountability Committee (PIAC, 2021), operations of upstream revenue deteriorated significantly from 2019 proceeds of US$925 million to 2020 revenue of US$638 million. This indicates a decline of about 30.96% in revenue from crude oil in just 1 year period. Thus, the variability in global oil prices could impact on the economy of Ghana and make it vulnerable. These vulnerabilities could be felt in the agricultural sectors, government expenditure, balance of trade, unemployment among others.

In fact, variability in global oil price affects the economy not only through direct channels, but also through indirect channels (Varyash et al., 2020; Zaaher & Maayan, 2015). For example, rising crude oil price caused a rise in prices of imported goods and the increased burden of external debt of Turkey (Aydin & Acar, 2011). This in turn negatively affected GDP growth of Turkey (Aydin & Acar, 2011). These impacts of global oil price variability might have different impact on different sectors of the economy. Hence, the responses to global variability in prices of crude oil to the economic activities of Ghana could be asymmetric (Ayisi, 2020; Sarkodie et al., 2019). This nullifies the assumption that there is symmetrical effect of crude oil price fluctuations on the activities of the economy. Since there are differential responses to varying fluctuations by the various sectors and not equal responses when crude oil prices changes.

According to Baek et al. (2019); Hung (2021); Hung and Vo (2021), many studies have been conducted to link prices of crude oil and economic of countries from the early 1980s. This is mainly due to the impact of variability in world’s prices of crude oil on vulnerability of economies of both the developed and developing countries including the works of Al Rasasi and Yilmaz (2016); Humbatova and Hajiyev (2019); Nusair (2016); Timilsina (2015); and Zhang and Qu (2015) for economic growth, mostly using GDP or industrial productions as proxies. Also, Abounoori et al. (2014); Misati et al. (2013); and Sek et al. (2015) analyzed crude oil price effect on rate of interest and general prices changes (inflation). Furthermore, Basher et al. (2012) and Diaz et al. (2016) evaluated crude oil price effects on the stock markets using stock prices, Alkhateeb et al. (2017); Almutairi (2020); and Karaki (2018) and considered employment while Hung (2021) considered agriculture commodity market and oil price. In the case of Ghana, studies such as Nchor et al. (2016); Czech and Imbeah (2019) considered the effects of shocks in oil prices and their effects on the vulnerability of Ghana economy. Nchor et al. (2016) used the Vector Autoregressive (VAR) and Vector Error Correction Mechanism (VECM) while Czech and Imbeah one factor variance analysis and Tukey’s honest significant test. Damba et al. (2021) applied the GARCH model to analyze crude oil price volatility on the cocoa sector in Ghana. These studies indicate that macroeconomic aggregates movements of domestic countries seem to adjust to crude oil prices.

Despite these abundance of studies on the effects of oil prices, studies on emerging oil-producing countries such as Ghana are not abundant. Most of the studies mainly focused on economies belonging to OPEC and oil economies in the Middle East which have long history of crude oil production, whereas studies on emerging oil economies from SSA such as Ghana are limited. The analyses available have been mainly on panel analyses, stressing on advanced exporters and producers of crude oil which include countries of the Gulf Cooperation Council, the OPEC countries and the ASEAN-5 (Philippines, Singapore, Malaysia, Thailand, and Indonesia). Nevertheless, there are scanty studies utilizing nonlinear effect of crude oil price variability for specific countries. Using a single country study allows you to account for the variability of a peculiar character of that country.

Furthermore, most of these studies concentrated on economic growth, specifically aggregated GDP of the selected countries in their empirical analyses. However, different sectors of the economic growth of a country are most probable and expected to be balanced by either a surplus or a deficit of other sectors. GDP consists of many macroeconomic variables and summing them up might not reflect the true situation of global oil price effect. These remarks lead this study to suspect that aggregation measures are susceptible to biasness which is an important shortcoming of their studies. GDP of Ghana must be disaggregated for better analyses. It is therefore clear that there is still sufficient scope to contribute to literature bearing in mind Ghana’s experiences in dealing with the global crude oil price variability. To facilitate a deeper understanding, an extra precise valuation of the global oil price variability on the vulnerability of the Ghana economy using time series analyses. This paper analyzes the effects of fluctuations of world crude oil price and its effects on the vulnerability of the agricultural sector of the economy of Ghana. Agricultural sector accounts for export earning for almost 40% and provides 90% of food needs of Ghana (Food and Agriculture Organization of the United Nations, 2021). According to the Ghana Statistical Services (GSS, 2022), the agricultural sector in Ghana contributes 33% to GDP and accounts for about 78.7% of employment in the rural areas. Hence, its vulnerability to crude oil price variability might impact other sectors of the economy. This paper hypothesizes that world crude oil price fluctuations have varying significant effects on the vulnerability of the agricultural sector in Ghana.

Thus, this paper enlightens the understanding of the effect of crude oil price fluctuations on the agricultural sector in Ghana to make policies in energy sector toward addressing the impact of global oil price variability. This has become necessary since Ghana has not gained a significant influence in the world oil supply and influence on determination of crude oil prices. Further, this study’s finding can also help the Government of Ghana and even local and foreign oil companies in making other sound investment decisions to diversify their revenue and look everywhere and not only rely solely on crude oil. It is against this backdrop that this study examines the effects of world crude oil prices fluctuations on the vulnerability of Ghana economy. The study utilizes a nonlinear approach in analyzing the global oil price variability on the vulnerability of the sectors of the economy of Ghana; because the impacts of fluctuations in crude oil prices on agricultural activities are most probable to differ in both magnitude and signs. Effects of increase in oil price on cocoa might be different from that of animal products.

The rest of the paper is organized as follows: Section 2 presents the literature review focusing on the petroleum sector, theoretical and empirical reviews. Sources of data, analytical techniques and model specification are presented under Section 3 of research methods. Furthermore, analyses and discussions of results are conducted in Section 4. The last section, Section 5 presents the conclusions and policy implications of the study.

Ghana Petroleum Sector

According to Public Interest and Accountability Committee (PIAC, 2018), exploration and production operations remain powerful, apparently sustained by the moderately elevated and steady crude oil prices ranging from $70 to $79 for over most periods of 2018. During these periods, the Greater Jubilee Field continued all-year production activities. Production of crude oil from the reservoir of the Ntomme and Enyenra in the Tweneboa-Enyenra-Ntomme (TEN) Field continued progressively during 2018 period. According to the PIAC 2018 report, three producing fields in Ghana—TEN, Sankofa Gye Nyame (SGN) and Jubilee—produced 62.135 million barrels of crude oil in 2018. In comparison with 2017 total crude oil production of 58,6.658 million barrels, there was an increase of a 5.93% in 2018 periods. The average achieved price by Ghana National Petroleum Corporation (GNPC) of US $68.487 per barrel was higher than the US$54.43 per barrel benchmark price estimated by the government of Ghana in 2028. This average price that was achieved was for all the three fields producing crude oil in Ghana. This higher average price resulted in a higher revenue outturn for the year (GNPC, 2018). This is depicted in Figure 2. From Figure 2, petroleum revenue received in 2017 was US$540.41 million while that of 2018 was US$977.09 million. This indicates an increase in petroleum revenue of US$436.68.

Figure 2.

Petroleum revenues in US$ millions.

However, in 2019 a total revenue of US$925.036 million was accrued, which was a decline of 5.33% of the 2018 revenue. A total revenue of 2020 declined further to US$638.643 million. From Figure 2, since the inception of crude oil production (December 2010) to 2020, petroleum, revenues have been fluctuating. This could be due to price variability of global oil prices as indicated in Figure 3. From Figure 3, it is observed that price contribution of petroleum revenue has been very volatile. This implies that global oil price variability might affect other sectors of the economy because Ghana relies so much on the revenue stream from crude oil.

Figure 3.

Analysis of petroleum receipts from 2011 to 2020 in US$.

The major sources of petroleum receipt since 2011 is the carried and participating interest (CAPI). The next major source of receipt is the royalties (ROTY) and then corporate income tax (CITA). Other such as surface rental and petroleum holding fund have insignificant contributions to petroleum receipt in Ghana. Although these sources of petroleum receipts have been fluctuating, they are more stable than the petroleum price contributions to petroleum revenue in Ghana.

Literature Review

There are studies on the effect of oil price shocks on the economies of the world which have employed different techniques including VECM, VAR, and others. Also, there are a lot of discussions among scholars on the relationship between markets for agricultural commodity and crude oil prices (Hung, 2021; Hung & Vo, 2021; Su et al., 2019). These studies have reported mixed results depending on the location and context of the study. For instance, Vo et al. (2019) analyzed the causal associations between markets for oil and agricultural commodities and found that that the crude oil prices play a important role in explaining variations in the prices and volatility of agricultural commodities. In the same vein, Taghizadeh-Hesary et al. (2019) corroborated that prices of agricultural food respond positively to any innovations from the crude oil market. Shiferaw (2019) supported the findings of Vo et al. (2019), but the author noticed that the relationship between the agricultural commodity and energy prices is time-varying, which means that the prices of agricultural commodities and crude oil prices experience strong co-movement. Similarly, Su et al. (2019) discovered that the dynamic, positive bidirectional causality exists between crude oil and agricultural prices and provided evidence that price spillover between two variables happens to agricultural commodities. The study by Pal and Mitra (2019) found a strong relationship between returns of crude oil and agricultural commodity markets.

Hung (2021) and Hung and Vo (2021) evaluated time-frequency connectedness and the effects of spillovers between crude oil price, S&P 500 and gold assets using the wavelet coherence and spillover index by Diebold and Yilmaz (2012). The authors found significant dependent among the variables in pre and during the Covid-19 outbreaks. In a different study, Zafeiriou et al. (2018) employed ARDL cointegration in evaluating crude oil prices effects on agricultural commodities. The study found that crude oil prices have effect on agricultural commodities, which validates its interaction with agricultural commodities. In another study, Vo et al. (2019) modelled the relationship between agricultural and crude oil prices. Using different techniques including structural vector autoregressive (SVAR), impulse response functions, and variance decomposition, Vo et al. (2019) showed that different oil price shocks have varying contributions to prices of agricultural commodities. Conversely, Umar et al. (2021) employed Granger-causality tests, static and dynamic connectedness spill over index and found that oil price shocks granger cause changes in prices of grains, wheat, and live cattle.

Similarly, Burakov (2016) and Rezitis (2015) estimated the long-run relationship existing between prices of crude oil and agricultural commodity in a panel method. Using ARDL, Rezitis found that oil price fluctuations have significant impact on agricultural commodity prices while Burakov used the impulse response analyses to show that prices of agricultural products are not sensitive to oil prices fluctuations in Russia, especially in the short run. For the long run, no granger causality relationship existed between oil and agricultural commodities prices. These studies imply that there is correlation between oil prices and prices of agricultural commodities.

Wang et al. (2014) analyzed oil price and prices of agricultural commodities by modelling joint dynamic between agriculture and crude oil prices using SVAR. The study results indicated that response of agricultural commodities’ prices to prices of crude oil changes rely on oil supply shocks, other oil specific shocks, or demand shocks. They found that oil shock explains a minor variation of prices of agricultural commodity. In a similar vein, de Nicola et al. (2016); Ji et al. (2018); Lucotte Y. (2016); Roman et al. (2020); and Pal and Mitra (2018) employed multivariate dynamic conditional correlation to analyze the co-movement of major agricultural, energy, and food commodity price returns. The authors found that there is high correlation between prices of return on energy and agricultural commodity prices.

Ayisi (2020) evaluated the asymmetry effect of prices of crude oil on welfare implication and inflation in Ghana. Using the NARDL, the author found that there is an asymmetric effect of price of crude on inflation and welfare indicators. In other countries, Asymmetric test using Wald Statistics revealed evidence of asymmetries in all the cases implying that positive and negative shocks of the same magnitude did not have equal impact on agricultural commodity prices. Olasunkanmi and Oladele (2018) applied the NARRDL to analyze prices of agriculture commodity and oil price shock and found significant positive oil price changes in all cases with the expected positive sign. Melichar and Atems (2019) unveiled asymmetric responses in agricultural commodity prices to crude oil markets using non-linear models. The authors provided evidence of heterogeneity after changes in U.S. energy policy in 2016, with a strong correlation between crude oil and agricultural commodities prices. Cheng and Cao (2019) confirmed the fact that there is a nonlinear causal association between crude oil and agricultural commodity markets. Živkov et al. (2019a) showed that there is strong spillover effect from crude oil to barley, corn, and soybean in the longer time horizons. They showed that crude oil impact agricultural commodities in the periods of increased market turbulence. Also, Živkov et al. (2019b) shed light on the low coherency in the short-run and high coherency in the long run between crude oil and agricultural commodity markets.

In spite of the numerous studies, studies on emerging oil-producing countries like Ghana are limited (for example, Czech & Imbeah, 2019; Damba et al., 2021; Nchor et al., 2016). Most of the studies analyses available have been mainly focused on economies with long history of crude oil production, whereas studies on emerging oil economies from SSA such as Ghana are limited. Thus, these studies stress on advanced exporters and producers of crude oil which include countries of the Gulf Cooperation Council, the OPEC countries and the ASEAN-5. However, there are scanty studies carried out on Ghana relating prices of crude oil to agricultural commodities’ market. Our study fills this literature gap by analyzing the effect of crude oil prices fluctuations on the agricultural commodity markets in Ghana. Using an oil-emerging producer country like Ghana allows you to account for the variability of a peculiar character of that country. Empirical analyses of the effect of crude oil price fluctuations on agriculture commodity markets in Ghana is important since it is estimated that about 34.7% of the population of Ghana rely on agriculture for employment and livelihood (GSS, 2022). This empirical analysis on the vulnerability of a sector of Ghana’s economy to global oil price variability is necessary since Ghana has become a net oil exporter but does not influence global crude oil prices. Also, Ghana relies so much on revenue from petroleum for undertaking many economic activities including financing of free basic and secondary education, road construction, building of schools and hospitals, and many others.

Methodology

Analytical Approach

Global oil price variability on macroeconomic indicators is likely to differ in both magnitude and signs (Baek et al., 2019; Rafiq et al., 2016). Thus, this study is focused on determining if world crude oil price fluctuations have asymmetrical effects on agricultural activities of Ghana. To this end, this paper employs the non-linear autoregressive distributed lag (NARDL) approach developed by Shin et al. (2014) to analyze the short-run and long-run relationships among the macroeconomic indicators using Stata version 14. The NARDL approach offers estimates that are unbiased and a valid t-statistics in the long-run even when some regressors are endogenous.

Model Specification

Following other empirical studies such as Baek et al. (2019); Kriskkumar and Naseem (2019); and Umekwe and Baek (2017), this study models the empirical estimations of the fluctuations of global oil price on the agricultural sector output of the economy of Ghana as:

$G_{t} = γ_{0} + Oil_P_{t} + γ_{3} x' b_{t} + μ_{t}$ (1)

In addressing asymmetry of global prices of crude oil prices variations effectively, the study separates global crude oil price climbs from world crude oil price plummets stated in Equations (2) and (3) to negative changes and positive changes. We decompose negative and positive changes for oil price denoted by $Oil_P_{h}^{-}$ and $Oil_P_{h}^{+}$ as follows:

$NegOil_P_{t} = \sum_{h = 1}^{t} Δ lnOil_P_{h}^{-} = \sum_{h = 1}^{t} \max (Δ lnOil_P_{h}, 0)$ (2)

$PosOil_P_{t} = \sum_{h = 1}^{t} Δ lnOil_P_{h}^{+} = \sum_{h = 1}^{t} \max (Δ lnOil_P_{h}, 0)$ (3)

Where Pos $Oil_P$ measures only increases in world oil prices and $NegOil_P$ measures only decrease in global crude oil prices. Putting Equations (2) and (3) into Equation (1), the resulting model yields the following relationship:

$G_{t} = G (PosOi l_{P}, NegOi l_{P}, x' b)$ (3)

To demonstrate the NARDL modelling approach, the study expresses then the model in Equation (1) as:

$G_{t} = α + a_{1} PosOil_P_{t} + a_{2} NegOil_P_{t} + a_{3} x' b_{t} + ε_{t}$ (4)

This study constructs an NARDL conditional error correction model, explained in Equation (5):

$\begin{matrix} Δ G_{t} = & b + \sum_{i = 0}^{n} β_{1 i} Δ PosOil_P_{t - i} + \sum_{i = 0}^{n} β_{2 i} Δ NegOil_P_{t - i} \\ + \sum_{i = 0}^{n} β_{3 i} Δ x' b_{t - i} + a_{1} PosOil_P_{t} + a_{2} NegOil_P_{t} \\ + a_{3} x' b_{t} + Φ {ECM}_{t - i} + ε_{t} \end{matrix}$ (5)

Equation (5) indicates the NARDL model, Φ represents the speed of adjustment coefficient, ECM denotes the error correction mechanism and within a period capturing the speed at which disequilibrium in $G_{t}$ is corrected. Where $G_{t}$ is the dependent variables (agricultural activities), $PosOil_P_{t}$ is positive global oil price variability and $NegOil_P_{t}$ is negative global oil price variability, $x'{b_t}\$ are other control variables, $γ_{s}$ are beta coefficients, $t$ the time period, and $μ_{t}$ is the random disturbance error term.

When it is uncertain whether the variables are in the same order such as $I (0) I (1) or I (2)$ , Pesaran et al. (2001) established a bound checking technique to check the presence of relationship among the variables in the long-run in accordance with the F-test. This bound test is estimated as:

$H_{0} : β_{1} = β_{2} = β_{n} = 0$ : β₁ = β₂ = βn =0: that is the variables are not co-integrated

and

$H_{1} : β_{1} \neq β_{2} \neq β_{n} \neq 0$ : that is the variables are co-integrated

The co-integration test is determined using two bounds asymptotic critical values. All of the regressors are assumed to be $I (1)$ in the upper bound, and $I (0)$ in the lower bound.

Sources and Type of Data

This study uses secondary data from the Food and Agriculture Organization (FAO), the Bank of Ghana, and British Petroleum Statistical Review. The study period covers quarterly data series from the first quarter of 1980 to the fourth quarter of 2019. Data on agricultural commodities producer prices were sourced from the FAO while data on exchange rate, imports, and exports were sourced from the Bank of Ghana and data on crude oil prices were sourced from British Petroleum Statistical Reviews.

Results and Discussions

Summary Statistics

Table 1 provides the summary statistics of the variables used in these empirical analyses. It provides the number of observations, minimum, maximum, mean, and the standard deviation of all the variable used in the analyses of this study. The values of the agricultural commodities are priced in producer prices in US$. The lowest dispersion is 11.723 (export percentage of GDP) while the highest is 657.654 (exchange rate). The mean value of producer price of cassava is $104.133 per tonne while that of maize is $256.521 per tonne, that of cereals is $33.444 per tonne and that of cocoa is $860.481 per tonne. The agriculture producer price (the overall producer prices of agricultural commodities) has a mean value of $42.844 per tonne. The mean producer price of agriculture is smaller than that of cassava, cocoa, and maize and higher than that of cereals. This is because agriculture comprises many crops and live stocks which do not have higher prices relatively to cocoa, maize, and cassava. The mean price of crude oil is $62.004 per bbl.

Table 1.

Descriptive Statistics, 1980 to 2019.

Variable	Obs	Mean	Std. Devi.	Mini	Maxi
Oil price	160	62.004	31.282	19.944	126.449
Cassava PP	116	104.433	40.007	39	210.5
Maize PP	116	256.521	82.003	144.4	443
Cocoa PP	116	860.481	328.106	322.2	1813.1
Agric PP	116	42.844	42.961	1.73	140.94
Cereals PP	116	33.444	34.653	.52	107.22
Chicken PP (index)	116	57.208	38.178	3.41	127.02
Cattle PP (index)	116	54.424	40.576	4.05	127.02
Total meat PP (index)	116	61.698	34.328	23.54	127.02
Livestock PP (index)	116	59.201	35.535	16.84	127.02
Exch.rate	160	326.325	657.654	64.646	3520.499
Export (% of GDP)	160	25.692	11.723	3.338	48.802
Import (% of GDP)	160	35.990	17.040	2.982	67.246

Source. Author’s own compute (2022).

Chicken Producer Price Index (2014–2016 = 100) has a mean value of $57.208. that of cattle is 54.424 and that of livestock is $59.201. The mean producer price of meat (total) is $61.698. It is observed that chicken has the higher producer price index. The mean exchange rate (exch.rate) is 326.325 with a minimum value of 64.646 and a maximum value of 3520.499. For exports of goods and services (% of GDP), the mean value is 25.692% of GDP with the maximum value as 48.802% and a minimum value of 3.338%. Imports of goods and services (% of GDP) has a mean value of 35.990%.

Unit Root Test

According to Baek et al. (2019), the basic appeal of the ARDL method is that it is used if the covariates are $I (0)$ or $I (1)$ . However, ARDL cannot be employed if the independent variables are $I (2)$ and/or the regressands are $I (0)$ . Due to this, this study tests unit roots in the all variables. Appendix A provides Phillip Perron unit root results at levels and at first difference. In levels, the Phillip Perron tests give all the test statistics are $I (1)$ except for producer price of cassava, producer price of maize, and Real effective exchange rate. The rest are $I (1)$ with no $I (2)$ . These results from the unit root tests imply that this study can rely on the F-statistics to designate cointegration among the variables. The null hypothesis is rejected which indicates strong cointegration among the variables that are selected. The study therefore proceeds to estimate the Equations (4) and (5) in determining whether oil price variability has asymmetric effects on the indicators of the agricultural commodities of Ghana. In the first difference, maximum lag of two is imposed.

Cointegration

We analyzed long-run relationship employing ARDL bound testing approach which was proposed by Pesaran et al. (2001). Appendix B presents the results on the cointegration test. The calculated F-statistic along with the critical values are reported in Appendix B. When agriculture producer price is the dependent variable, the calculated F-statistic is 27.416. This value is higher than the upper bound critical value of 5.61 at the 1% level. The result suggests that the null hypothesis of no long-run relationship can be rejected. In this case we go ahead to estimate the error correction model within the ARDL framework.

Commodity Prices of Crude Oil and Cocoa

From Figure 4, crude oil price (International Brent) in US$ per barrel and cocoa prices (International Cocoa Prices) in US$100 per tonne are relatively unstable and fluctuate. The prices increase and decrease in 2020. However, the indicative price of petrol, measured in GHC per liter is relatively stable.

Figure 4.

Prices of some commodities in Ghana.

From Figure 5, it is observed that crude oil price (Brent dated), measured in US$ per barrel, and agriculture producer price index (measured in US$ per tonne) move in the same direction until 2015. As crude oil price increases, prices of agricultural commodities increase. This trend in the figure is not surprising since crude oil price causes inflation in general prices of goods and services. This is very typical in economies where the dependency on crude oil is very high.

Figure 5.

Crude oil price and agricultural commodity price index.

However, after 2015, crude oil price and agriculture producer prices move in the opposite direction. This could be due to Ghana’s over reliance on crude oil. It is observed that, relatively, as crude oil prices increase, producer price of agriculture decreases. This up and down movement of crude oil price could affect the agricultural sector.

Effect of Crude Oil Prices on Agricultural Commodities Prices

In evaluating the effect of global oil price variability on the vulnerability of the economics of the agricultural sector in Ghana, this study examines the effect of crude oil prices on producer prices of agricultural commodities. The empirical study results are presented in both Tables 2 and 3.

Table 2.

Oil Price and Prices of Food Commodities.

	Agric PP	Cassava PP	Maize PP	Cocoa PP	Cereal PP
Long run estimates
PosOil_P	0.0208 (0.033)	0.5462 (0.4713)	3.0808*** (1.0638)	7.3763*** (2.2601)	0.0176 (0.134)
NegOil_P	−0.0189 (0.0346)	−0.23936 (0.469)	−2.4148** (1.0559)	−4.8197** (2.1235)	−0.0125 (0.1285)
Exch.rate	0.00059	0.7537**	0.0567	−2.6035**	−0.0678
	(0.0005)	(0.3194)	(0.599)	(1.259)	(0.0766)
Export	0.00036	−0.00214	−0.0659	3.2887***	−0.029
	(0.0003)	(0.13623)	(0.3336)	(0.853)	(0.0380)
Import	0.1429 (0.138)	−4.6524* (2.4701)	−7.7073 (5.2807)	4.2846 (11.797)	0.9235 (0.7034)
Short run estimates
$Δ PosOil_P_{t}$	0.0098 (0.0303)	1.4957*** (0.4589)	5.5448*** (1.004)	9.3018*** (2.0603)	0.2728** (0.129)
$Δ PosOil_P_{t - 1}$	0.003 (0.024)	0.4992 (0.361)	0.6219 (0.776)	3.3227* (1.6741)	0.0278 (0.106)
$Δ NegOil_P_{t}$	−0.00029 (0.001)	−3.4724*** (0.6904)	−3.8363** (1.47857)	−1.1459 (3.1742)	−0.0082 (0.1966)
$Δ NegOil_P_{t - 1}$	−0.00024 (0.001)	−0.293 (0.771)	−0.9017 (1.516)	−0.4713 (3.1886)	−0.0924 (0.1954)
$Δ E x c h . r a t e_{t}$	0.0041*** (0.0006)	0.413 (0.293)	1.3516** (0.629)	10.358*** (1.348)	−0.0564 (0.084)
$Δ Exch . rat e_{t - 1}$	0.00022 (0.0006)	0.4071 (0.3053)	1.20779* (0.6648)	2.7941* (1.6678)	0.0417 (0.0859)
$Δ Expor t_{t}$	0.10399 (0.077)	−3.785*** (1.295)	−6.0252** (2.744)	21.117*** (5.7641)	−0.2517 (0.3651)
$Δ Expor t_{t - 1}$	0.0258 (0.1304)	1.958 (2.196)	0.1599 (4.679)	−20.438* (10.350)	0.4375 (0.6596)
$Δ Impor t_{t}$	0.1325 (0.1299)	−0.485 (2.083)	−2.48394 (4.439)	−26.461*** (10.070)	1.8154*** (0.596)
$Δ Impor t_{t - 1}$	−0.0305 (0.0744)	−0.9865 (0.9809)	−1.3998 (2.0943)	6.664 (4.533)	−0.4179 (0.3428)
$e c_{t}$	−1.074***	−0.437***	−0.236***	−0.3449	−3.7E–09*
	(0.120)	(0.0917)	(0.0830)	(0.0764)	(1.95E–09)
_cons	−0.721 (0.8188)	−2090.7** (874.663)	−363.637 (1684.035)	17765.02*** (4579.14)	86.553 (224.67)
N	113	113	113	113	113

Source. Authors’ own compute (2022).

Note. Standard errors are in brackets.

***

represents 1%, ** represents 5%, and * represents 10%.

Table 3.

Oil Price and Animal Products Prices.

	Chicken	Cattle	Livestock	Total meat
Long run estimates
PosOil_P	0.293* (0.154)	0.306* (0.162)	0.248** (0.113)	0.229** (0.113)
NegOil_P	0.323** (0.158)	0.350** (0.166)	0.252** (0.115)	0.238** (0.115)
Exch.rate	0.006 (0.100)	–0.008 (0.066)	0.007 (0.073)	0.014 (0.046)
Export	0.355 (0.821)	0.622 (0.867)	0.047 (0.595)	0.126 (0.600)
Import	0.348 (0.537)	0.178 (0.562)	0.339 (0.388)	0.284 (0.389)
Short run estimates
$Δ PosOil_P_{t}$	0.292** (0.120)	0.299** (0.126)	0.291*** (0.087)	0.256*** (0.088)
$Δ PosOil_P_{t - 1}$	0.025 (0.102)	0.023 (0.107)	−0.046 (0.075)	−0.041 (0.075)
$Δ NegOil_P_{t}$	0.328*** (0.101)	0.345*** (0.106)	0.242*** (0.073)	0.244*** (0.073)
$Δ NegOil_P_{t - 1}$	−0.008 (0.120)	−0.015 (0.126)	0.076 (0.088)	0.067 (0.088)
$Δ Exch . rat e_{t}$	0.107 (0.127)	−0.090 (0.306)	0.271*** (0.092)	0.208 (0.211)
$Δ Exch . rat e_{t - 1}$	0.023 (0.127)	−0.108 (0.305)	0.030 (0.095)	−0.033 (0.213)
$Δ Expor t_{t}$	0.255 (0.488)	0.385 (0.513)	0.002 (0.353)	0.069 (0.355)
$Δ Expor t_{t - 1}$	−0.159 (0.675)	−0.252 (0.711)	0.101 (0.493)	0.044 (0.496)
$Δ Impor t_{t}$	0.547 (0.389)	0.443 (0.409)	0.409 (0.282)	0.353 (0.283)
ΔImportt−1	−0.061 (0.429)	−0.045 (0.450)	−0.183 (0.310)	−0.169 (0.312)
ect	−1.062*** (0.155)	−1.084*** (0.156)	−1.068*** (0.150)	−1.075*** (0.150)
_cons	−4.850 (321.507)	−5.342 (338.009)	23.610 (232.487)	48.448 (233.748)
N	113	113	113	113

Source. Author’s own compute (2022).

Note. Standard errors are in brackets.

***

represents 1%, ** represents 5%, and * represents 10%.

Table 2 shows that increase in crude oil prices increases agricultural commodities prices. The magnitude and significant of both the long-run and the short-run differ from each other across the commodities that are considered in this study. For instance, in the short-run, crude oil prices have significant effect on all the disaggregated commodities except for the overall index.

Increase in crude oil price by $1 significantly (at least at the 5% significant level) increases the prices of cassava, maize, cocoa, and cereals by $1.496, $5.545, $9.302, and $ 0.273 respectively. Whereas, decrease in crude oil price by $1 significantly decreases prices of cassava and maize and insignificantly decreases the cocoa, cereals, and agriculture producer price index. In the long-run, US$1 upsurge in crude oil price would increase producer price of maize by US$3.08 and it is significant at 1% (p-value = .005) while a reduction in crude oil price would reduce the producer prices of maize by US$2.41 and it is significant at 5% (p-value = .025). However, crude oil prices have insignificant impact on the overall index of the producer price of agriculture for both short-run and long-run. The short-run results are like the long-run results.

These empirical results indicate that agricultural commodities prices in Ghana are influenced by crude oil price fluctuations. That is, different effects of positive and negative changes in crude oil prices show that there are asymmetric effects on producer prices of agricultural commodities. Such findings are very necessary revealing that agricultural commodities price indices increase during rises in global crude oil prices and the impact is less when there is a decrease in global crude oil price as evident in Figure 5. The results of this study affirm other studies such Zafeiriou et al. (2018) who found that oil prices have effect on agricultural commodities. The differences in the magnitudes of this study validate Vo et al. (2019) who found oil price changes and its interaction with agricultural commodities do not contribute the same effect. Other studies such as Umar et al. (2021) and Rezitis (2015) also confirm the result of this study. Conversely, Burakov (2016) indicated that commodity agricultural goods prices are not sensitive to oil prices changes in Russia. The reason for the different effect of global crude oil price on agricultural commodities is that some of the agricultural commodities are not conventional export commodities in Ghana.

Table 3 presents the results of the effect of crude oil price variability on the prices of animal products. The results show that fluctuations in crude oil prices cause prices of animal products to rise as well. However, there is weak correlation between crude oil prices and prices of animal products. For instance, in the long-run, increase in crude oil price causes the price of chicken, cattle, livestock, and aggregate meat price to increase by $ 0.293, $ 0.306, $ 0.248, and $ 0.229 respectively. These are significant at 10% and 5% levels. This weakness indicates the reason for increase in prices of animal product irrespective of the variability of price of crude oil in Ghana. This implication is that fluctuation in crude oil prices does not influence animal product prices.

In the short-run, $1 decrease in crude oil price significantly (1% level) causes the price of chicken, cattle, livestock, and aggregate meat price to increase by $ 0.328, $ 0.345, $ 0.242, and $ 0.244 respectively. Also, $1 increase in crude oil price increase the price of chicken, cattle, livestock, and aggregate meat by $ 0.292, $ 0.299, $ 0.291, and $ 0.256 respectively. These are significant at 10% and 5% levels. Increase in crude oil price as well causes prices of animal products to increase. Thus, both increase and decrease in crude oil price cause prices of animal product to increase with a minimal impact. For the short-run, the magnitude and signs are not different from the long-run estimates.

Northern Ghana has the highest animal production in Ghana where there is no oil exploration or production. Furthermore, there are many nomadic activities in Ghana. Many of these nomads come from Mali, Niger, Burkina Faso, and others to bring their animals to Ghana for grazing and trading on foot. These nomadic activities could affect the prices of animal products rather than crude oil prices fluctuations. These results contradict that of Roman et al. (2020) who find long run relationship between oil and meat prices. Umar et al. (2021) and Rezitis (2015) also confirm the result of this study. On the contrary, Burakov (2016) indicated that commodity agricultural goods prices are not sensitive to oil prices changes. However, it must be noted that dissimilar study periods and different analytical approaches are adopted.

Conclusions and Policy Implications

This study utilizes NARDL to analyze global crude oil price fluctuations on the vulnerability of the producer prices of agricultural commodities including producer prices of food commodities and producer price index of animal products in Ghana. Quarterly time series data are sourced from different institutions and organization spanning from First Quarter in 1980 to Fourth Quarter in 2019. The effects of fluctuations in crude oil prices on different agricultural commodity indicators have been established to vary in both magnitude and signs.

The study concludes from the findings that increase in prices of global crude oil increases the producer price of various agricultural food commodities while decrease in crude oil prices decreases food commodity prices. The magnitude and significant of both the long-run and the short-run differ from each other across the commodities that are considered in this study. However, global crude oil prices variability does not have significant impact on the overall index of the producer price of agriculture in both the short-run and long-run. The short-run results are like the long-run results. Again, the unpredictability in global crude oil prices have almost the same magnitude and significant effects on the index of producer prices of animal products. For animal products producer price index, there is no asymmetrical effect. Both the long run and short run effects of increase and decrease in crude oil prices are very similar to each other.

Finally, the study recommends noting the asymmetric effect of global crude oil prices variability on the sectors of the economy, is important, especially when formulating policies on food commodity prices. Evidently, Ghana’s agricultural sector is vulnerable to unpredictable changes in global crude oil prices. Facing a scenario like this, institutions of government in Ghana have to make policies that minimize the impact of changes in global oil prices variability on the sectoral agricultural product. Since agricultural commodities prices in Ghana change reflect oil prices, other government policies such as the One District One Factory (1D1F) and Planting for Food and Jobs must be taken seriously and implemented to curb the effects of crude oil price variabilities on food commodities. This policy will help increase food availability and abundance which might help stabilize prices based on laws of demand and supply. Additionally, policies that relate mitigating changes in prices of animal products should be based on other issues such as encouraging local animal production and controlling nomadic activities rather than variabilities in global crude oil prices.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Clement Oteng

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Vulnerability of the Agricultural Commodity Markets in Ghana to Global Oil Price Fluctuations: An Empirical Analysis

Abstract

Plain Language Summary

Keywords

Introduction

Ghana Petroleum Sector

Literature Review

Methodology

Analytical Approach

Model Specification

Sources and Type of Data

Results and Discussions

Summary Statistics

Unit Root Test

Cointegration

Commodity Prices of Crude Oil and Cocoa

Effect of Crude Oil Prices on Agricultural Commodities Prices

Conclusions and Policy Implications

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability Statement

References