Impact of Gulf Cooperation Countries’ Foreign Direct Investment on Sudan’s Agricultural Exports

Abstract

Agricultural foreign direct investment (AFDI) contributes to the long-term growth of developing countries. Sudan has rich agricultural resources with great potential for AFDI. However, so far, Sudan has not exploited this potential to attract investors from other countries, having less of a competitive advantage in agricultural production owing to local and international problems. In this study, we examined the effects of foreign investments of Gulf Cooperation Council (GCC) countries on agricultural exports in Sudan, in addition to other economic factors during 1990–2016 using the two-stage least squares (2SLS) model. The results showed that the investments of GCC countries in Sudan’s agricultural sector boosted agricultural exports. The exchange rate was found to be a key determinant factor of GCC countries deciding to invest in Sudan. We recommend that the Central Bank of Sudan encourage policies to stabilize the exchange rate to attract more agricultural investment from GCC countries.

1. Introduction

Agriculture is the most important sector in Sudan’s economy. On average, it has contributed 30% to the country’s gross domestic product (GDP) from 2000–2019 [1]. The sector employs about 70% of the country’s population and provides inputs to many major manufacturing industries [2]. The agriculture sector has historically generated the bulk of Sudan’s foreign exchange earnings through a diversified basket of exports, which can be broadly classified into three categories: field crops, livestock, and forest produce exports. The main field crop exports include sorghum, millet, cotton, sesame, and groundnut, while livestock exports include sheep, camels, and cattle. Most forest exports consist of Arabic gum.

Economic growth is fundamental to the development of every society in the world. Foreign direct investment (FDI), in addition to other factors such as exports, exchange rate, domestic savings, and trade, contributes to economic growth and development [3].

Insufficient investment in the agricultural sector of most developing countries has led to stagnation in production and lower productivity, creating a challenge for global agriculture in feeding the world’s population, which is expected to increase by more than one-third (i.e., about 2.3 billion people from 2009–2050) [4]. Moreover, most of the population growth will occur in countries where poverty and hunger are prevalent despite the availability of natural resources. Therefore, increased efficiency and sustainability in crop and livestock production systems is essential to meet this growing demand [5]. Agricultural investments can generate various development benefits; however, these benefits cannot arise automatically as some forms of large-scale investment carry risks for host countries, such as the consumption of resources and land and the lack of involvement of landowners in the investment projects. The active involvement of local farmers in managing their land and as equal trading partners has a positive and sustainable impact on local and social development [4].

There is a direct relationship between the volume of exports and FDI, exchange rate, real GDP [6,7,8,9], and economic growth [10]. Furthermore, exchange rate has a direct impact on the performance of exports [11].

FDI in the agricultural sector significantly affects the growth and diversification of exports [12,13]. Many economic and political factors, such as the size of the host country’s market, GDP per capita, the rate of growth of GDP, cultural similarities between host and investing country, availability of natural resources, exchange rate, and government regulations, enhance foreign investment opportunities in the agricultural and food sector [14].

Global investment decreased at a rate of 1% from USD 1.41 trillion in 2018 to USD 1.39 trillion in 2019. FDI flows in developing countries increased at a rate of 2% in 2018 to USD 706 billion compared to 2017, while FDI flows in developed countries decreased at a rate of 27% from USD 759 billion to USD 559 billion [15]. FDI in developing countries contributes to modern technology, economic growth, administrative and skill benefits, improving the efficiency of human resources, and increasing employment rates [16].

Sudan is a target for FDI, especially its agriculture sector. From 2000–2010, Sudan received USD 18 billion, representing 5% of the total capital inflow from foreign investment in Arab countries. These investments provided nearly 20,600 job opportunities, through which national employment reached 82%. From 2000–2016, 551 investment projects from Gulf Cooperation Council (GCC) countries were submitted to the Sudanese government in several sectors, such as agriculture, industry, and services. With 417 projects, Saudi Arabia was ranked first in terms of the number of projects approved, of which 30 were implemented, followed by the United Arab Emirates, Kuwait, and Qatar [17]. Sudan has rich agricultural resources; although these resources are not optimally exploited. The country suffers from low per capita income and high unemployment, which reached about 19.5% in 2018 [18]. Agricultural exports have faced several local obstacles, such as problems with production and productivity, affecting most of the commodity exports, instability in public and agricultural policies, and the linkage of exports with production surpluses with regard to many goods that affect the continuity of and commitments to the global market. The trade in Sudan suffers from poor infrastructure; limited capacity for air, land, and sea transport facilities; and high cost [19].

As foreign investments represent an important factor determining exports, as per the literature, we aimed to examine the effect of foreign investments of the GCC countries on agricultural exports in Sudan. The main contribution of the study is two-fold: focusing on FDI from GCC countries and explicitly recognizing and modeling the synergy between FDI and exports in the context of Sudan. This remainder of this paper is presented as follows. Section 2 introduces the research methodology, Section 3 explains the empirical model Section 4 represents the results, and Section 5 concludes the paper.

2. Materials and Methods

In this study, we used a trading indicator [20,21] to describe GCC foreign direct investment (GFDI) inflow into Sudan and the value of agricultural export (EXP). These indicators explain the performance for export/import coverage and agriculture trade. Additionally, an empirical model was used to determine the impact of several factors on agricultural exports from 1990–2016. In this study, secondary data for variables were included from different sources such as the Central Bureau of Statistics in Sudan, the annual reports of the Central Bank of Sudan, and the Ministry of Investment in Sudan.

2.1. Export/Import Coverage

This index is an alternative to the normalized trade balance, which informs whether a country’s imports are fully paid by exports in a given year. Economists expect that, eventually, the trade balance will be zero; thus, imports are financed by exports, but this may vary considerably over shorter periods. The values for this index range from 0 when there are no exports to +∞ when there are no imports. When the value equals 1, it means the full coverage of imports by exports [22].

$$\mathrm{Export}/\mathrm{Import} \mathrm{Coverage}=\frac{{\displaystyle \sum }{X}_j}{{\displaystyle \sum }{M}_i}$$

where ${\displaystyle \sum X_j}$ is the total of export flow and ${\displaystyle \sum M_i}$ is the total of import flow at the end period.

2.2. Agriculture Trade Openness

This is popular as a simple and intuitively appealing trade ratio measure of agriculture trade openness. It indicates the degree to which an economy is open to trade [22]. Agriculture trade openness is calculated by the following equation:

[(EXPORT + IMPORT)/GDP] × 100

where EXPORT is the agricultural exports of Sudan, IMPORT is the agricultural import of Sudan, and GDP is the agricultural gross domestic product of the Sudan.

2.3. Empirical Model

The two-stage least-squares (2SLS) technique was applied to estimate the two-way relationship between GCC FDI in the agriculture sector and agricultural exports from Sudan. The choice of the 2SLS technique was based on the nature of the relationship between exports and FDI. A two-way relationship arguably exists between exports and foreign direct investment [23,24,25]. As a country’s exports increase, more funds are available for improvements in its infrastructure, rendering the country more attractive for foreign investors who would also contribute to promoting its exports, mainly through technology and capital transfers.

Structural Equation

FDI into Sudan is influenced by the following factors:

L(GFDI_t) = α₀ + α₁ L(EXP_t) + α₂ L(INF_t) + α₃ L(EX_t) + α₄ L(TR_t) + α₅ L(GFDI_t–1) + u_t1

(1)

L(EXP_t) = β₀ + β₁ L(GFDI_t) + β₂ L(AGDP_t) + β₃ L(OP_t) + B₄ (EXP_t–1) + u_t2

(2)

The model consists of two structural equations. First, the equation for GCC investments in the agricultural sector in Sudan (GFDI_t) is explained by several independent variables: agricultural exports (EXP_t), exchange rate (EX_t), transport and communication/total GDP of Sudan (TR_t), and the first lag of GCC foreign investment (GFDI_t–1). Second, the equation for agricultural exports for Sudan as a dependent variable is explained by several variables, such as GFDI_t, real GDP in the agricultural sector of Sudan (AGDP_t), inflation rate in Sudan (INF_t), trade openness in Sudan (OP_t), and agricultural export lag (EXP_t–1). The inclusion of the lagged values of (GFDI_t–1) helps determine how previous GFDI inflows impact current inflows and termed the agglomeration effects [26]. Moreover, the inclusion of the lagged dependent variable in the regression model helps avoid misspecification and serial correlation [27]. All variables were measured in the Sudanese Pound (currency) SDG, adjusted for inflation to the base year 2010. Thus, both GCC investment (GFDI_t) and exports (EXP_t) were endogenous variables determined from within the model, while the rest of the variables were considered exogenous. Equations (1) and (2) are overidentified because they meet the following condition: ((number of exogenous variables) − (equation has p variables) > (number of equations or endogenous variables) − 1). The presence of the agricultural exports (EXP_t) as an endogenous variable to the right-hand side of Equation (1) appeared to be an issue, where the error term (ut1) was assumed to be uncorrelated with the exogenous variables, but it might have correlated with agricultural exports (EXP_t). This correlation led to the OLS estimator being inconsistent for (α). To obtain a consistent estimator, we assumed the existence, at least, of instrumental variables for Equation (2) that satisfied the assumption that E(EXP_t, u_1t) = 0.

Reduced form: A structural equation of simultaneous equations can be expressed through reduced-form equations. The reduced equation coefficients measure the effect of the change in the exogenous variable on the endogenous variable and return the consistent coefficients of the structural equation. The reduced form of the structural equation can be written for Equations (1) and (2):

L(GFDI_t) = α₀ + α₁ (β₀ + β₁ L(GFDI_t) + β₂ L(AGDP_t) + β₃ L(OP_t) + β₄ L(INF) + B₅ L(EXP_t–1) + u_t2) + α₂ L(EX_t) + α₃ L(TR_t) + α₄ L(GFDI_t–1) + u_t1

(3)

L(EXP_t) = β₀ + β₁ (α₀ + α₁ L(EXP_t) + α₂ L(EX_t) + α₃ L(TR_t) + α₄ L(GFDI_t–1) + u_t1) + β₂ L(AGDP_t) + β₃ L(OP_t) + β₄ (INF_t) + B₅ L(EXP_t–1) + u_t2

(4)

Using Equation (3) to obtain Equation (5)

L(GFDI_t) = π₀ + π₁ L(AGDP_t) + π₂ L(OP_t) + π₃ L(INF_t) + π₄ L(EX_t) + π₅ L(EXP_t–1) + π₆ L(TR_t) + π₇ L(GFDI_t–1) + ν_1t

(5)

by using Equation (4) to obtain Equation (6):

L(EXP_t) = λ₀ + λ₁ L(EX_t) + λ₂ L(TR_t) + λ₃ L(GFDI_t–1) + λ₄ L(AGDP_t) + λ₅ L(OP_t) + π₆ L(INF_t) + π₇ L(EXP_t–1) + ν_2t

(6)

where π_i (i = 0, 1, 2, 3, 4, 5, 6) are the coefficients of the reduced form (5), λ_i (i = 0, 1, 2, 3, 4, 5, 6, 7) are the coefficients of the reduced form (6), and ν_1t, ν_2t are the random errors.

When using the 2SLS method in simultaneous equations for model (1), a significant relationship with positive coefficients was indicated between the FDI (GFDI_t) and explanatory variables, except for exchange rate (EX_t), which was expected to have a negative coefficient. In model (2), a significant relationship with positive coefficients was indicated between agricultural exports (EXP_t) and the explanatory variables, except for inflation rate (INF_t), which was expected to have a negative sign. Eviews- version 9 was used to estimate both stages simultaneously.

3. Results

Table 1. Summary statistics of the variables.

Statistic	AGDP	EX	EXP	GFDI	INF	TR	OP
Mean	5.95	2.70	13.34	31.67	42.76	10.77	10.13
Maximum	9.38	6.18	19.51	119.05	181.47	14.49	17.43
Minimum	2.29	1.01	6.78	3.97	4.40	7.60	3.02
Std. Dev. *	2.24	1.53	3.59	31.46	49.91	1.84	4.03
Skewness	−0.17	1.26	−0.07	1.37	1.50	0.36	0.29
Kurtosis	1.97	3.45	1.94	4.07	4.10	2.41	2.16

* Std. Dev., standard deviation. Source: Computed by Eviews-9.

reports the summary statistics of all the variables used in the study. During the study period (1990–2016), on average, FDI (GFDI) inflows into Sudan represented SDG 31.67 million, the value of agricultural exports (EXP) was SDG 13.34 million, and the exchange rate (EX) was SDG 2.7 SDG (USD 1 = SDG 2.7). Most of the variables showed signs of positive skewness, except AGDP and EXP.

Figure 1 shows the total and agricultural trade openness in Sudan during 1990–2016. Trade openness explains how a country engages in international trade with both exports and imports [28]. Figure 1 shows that both the general and the agricultural trade openness fluctuated throughout the study period. The agriculture trade openness fluctuated because of fluctuations in the world market prices of agricultural export products, especially sesame, Arabic gum, and livestock, which represent the main exports of agricultural products for Sudan. The general trade openness initially fluctuated, decreased from 1990–2002, and increased from 2003–2016 with little fluctuation, during which trade openness in Sudan was affected by the change in oil production and political stability.

We applied the 2SLS method to examine the mutual effect between exports and foreign investment in Sudan and determine the influencing factors. Additionally, the Akaike information criterion and Schwarz information criterion were used to determine the lag length of time series. The unit root test of the study variables was tested, and no serial correlation in variables of study was found.

4. Discussion

The export/import coverage indicator explains how the value of Sudan’s agricultural exports covered its agricultural imports during the study period (1990–2014). The findings showed that agricultural exports were able to cover imports during the study period, except in 2008, 2010, and 2011. In 2008, the deficit was due to a decline in the prices of agricultural exports, resulting from a decline in demand and the global economic recession [29]. In 2010 and 2011, the difference was mainly due to the increase in wheat prices on the global markets. In addition to the increasing quantity of wheat imported, most Sudanese agricultural exports during these two years were produced under rainfall cultivation; exports were affected by fluctuations in the annual rainfall [16].

The study results show that an exchange rate with a negative sign most significantly impacts foreign investment from GCC countries in Sudan (

Table 2. Results of 2SLS estimation for GFDI equation and EXP equation.

	L(GFDI)			L(EXP)
Variables	Coeff.	t	p-Value	Variables	Coeff.	t	p-Value
C	−18.54	−0.597	0.55	C	−31.95	−2.00	0.058 *
L(EXP)	−1.02	−0.683	0.50	L(GFDI)	1.75	1.039	0.31
L(INF)	−0.24	−1.314	0.20	L(AGDP)	7.46	2.45	0.02 **
L(EX)	−19.29	−2.557	0.01 **	L(OP)	6.57	4.10	0.00 ***
L(TR)	10	2.44	0.02 **	L(EXPt–1)	4.92	−1.530	0.14
L(GFDIt–1)	0.12	0.566	0.57
R2	0.60			R2	0.61
F	6	Sig(F)		F	9.8	Sig(F)
D.W	1.5	0.00 ***		D.W	1.91	0.00 ***

*, **, *** significant at 0.1, 0.05, and 0.01 levels, respectively. Source: Calculated by Eviews-9.

). This implies that an inverse relationship exists between exchange rate and GCC investment flows in the agricultural sector in Sudan. Moreover, the regression results indicate that a 1% increase in the exchange rate in Sudan led to a 19.29% decrease in GCC investments in the agricultural sector in the form of Sudan’s GFDI inflows. This result confirms that investors prefer countries with a lower exchange rate, as it makes the country’s exports cheaper and, hence, other things equal, more competitive in importers’ eyes. Some important implications can be drawn for Sudanese exports. Exchange rate policy reform is highly needed; a carefully managed and unified exchange rate can provide a much needed boost to Sudan’s agriculture, which is expected to lead economic growth in the country. For too long, Sudan’s exchange rate policy has been plagued by the existence of multiple exchange rates: the official central bank’s rate, a customs rate, and a parallel (black market) rate, with the latter of the three always leading the pack. The results in Table 2 show that when the share of transport and communication in total GDP (TR) had a positive sign, foreign investment from GCC countries in Sudan was significantly impacted. The regression results indicate that a 1% increase in the exchange rate in Sudan led to a 10% increase in GCC investments in the agricultural sector in the form of Sudan’s GFDI inflows. This result supports the opinion that improvements the infrastructure is required to inflow investment, especially as transportation is one of the determinants of foreign direct investment. This result is consistent with those of other similar studies. For example, one study found some of the economic determinants of FDI in Sudan [13]. The inflation rate does not have a significant value, which indicates that inflation does not directly affect GCC countries in Sudan. Moreover, Table 2 indicates the existence of a statistically significant relationship between agricultural exports and other explanatory variables, such as real GDP in the agricultural sector (AGDP) and trade openness (OP) in Sudan. Conversely, we found that GFDI had a positive effect, but it was not significant. While studies have found varied results on the direct effect of FDI on exports, some have found a negative correlation between the two [23,24,25,30], whereas others have found a positive correlation [10,14,31,32].

Furthermore, the results from the regression indicate a significant relationship between GDP in the agricultural sector and agricultural exports, where a 1% increase in GDP within the agricultural sector led to an increase in agricultural exports by 7.46%. This implies that when all other factors are held constant, real GDP within the agricultural sector is the most important factor for increasing Sudan’s agriculture exports. The coefficient of trade openness (OP) variable implies that agricultural exports increase by 6.57% as Sudan’s OP increases by 1%. This result confirms the hypothesis of a positive correlation between agricultural exports and Sudan’s agriculture trade openness. The results in Table 2 do not suggest the existence of a statistically significant relationship between GCC investments in the agricultural sector in Sudan’s GFDI and agricultural exports.

Generally, the model is good for forecasting according to diagnostic tests of the model, such as the normality test, serial correlation problem between errors, and heteroskedasticity (

Table 3. Model diagnostics.

Test for	Test Name/Statistic	H0	Decision Rule *	Decision
Normality	Jarque-Bera	Residuals are normally distributed	p-value = (0.215) [0.721]	Do not reject H0
Serial correlation	serial correlation LM tests	No serial correlation	p-value = (0.677) [0.9479]	Do not reject H0
Heteroscedasticity	Breusch-Pagan-Godfrey	Residuals are homoscedastic	p-value = (0.8227) [0.122]	Do not reject H0

* p-values within ( ) are for EXP equation and within [ ] for GFDI equation.

).

5. Conclusions

FDI in the agricultural sector significantly contributes to the growth of developing countries in the long run [33,34]. Although Sudan has rich agricultural resources, it has faced local and international problems with regard to agricultural exports. In this study, we examined the effect of the foreign investments of GCC countries on agricultural exports in Sudan, along with other economic factors, during the study period (1990–2016), using the 2SLS method. The study results showed that while GCC investments in Sudan’s agricultural sector have a positive impact on agriculture exports, that impact did not prove to be significant. The exchange rate is the main determinant of GCC countries’ investment in Sudan, along with improvements in the transport and communication sector in Sudan. The real GDP for the agricultural sector and trade openness in Sudan are the factors that affect agriculture export the most.

We recommend that the Sudanese government focus on increasing agriculture GDP and promoting agriculture trade openness to encourage investments in the agricultural sector and agriculture export. Moreover, improving infrastructure will lead to more investment inflow in the agricultural sector. In addition, the Central Bank of Sudan should encourage policies to stabilize the exchange rate to attract more investment from GCC countries in the agricultural sector. In this regard, well-designed and carefully managed and unified exchange rates need to be adopted. Setting reasonable interest rates and applying good and transparent management of the country’s foreign reserves are integral parts of such policy reforms. Finally, the limitation of this study is that we used aggregated data for traded agricultural products with no distinction between products. Future studies should also consider other data.