Graduate Collage

The Impact of Foreign Direct Investment on Economic Growth in Sudan: An Econometric Approach

A Dissertation submitted in partial fulfillment for the requirements of the M.Sc degree in Econometrics and Social Statistics

By:

Mai Mohammed Mustafa Ejami

Supervised By:

Dr. Hisham Mohamed Hassan

February, 2025

قال تعالى:

وقل ربِّ زدني علما

صدق الله العظيم

سورة طه الآية ١١٤

DEDICATION

This Dissertation is dedicated to the university of Khartoum, Faculty of Economics and social studies in Sudan. You made it all worthwhile.

to my family and friends who gave me the support and knowledge.

ACKNOWLEDGEMENTS

I would like to use this opportunity to thank my supervisor Dr. Hisham Mohamed Hassan, who always made out time from their busy schedules to read this work thoroughly. Their sound advice, gentle corrections and encouragement brought much hope, even when it seemed impossible to go on. I am eternally grateful.

I am also grateful to my wonderful family and friends who walked with me through it all. Our unwavering love and unflinching support gave me reason to go on.

I equally acknowledge all my teachers who helped me into the person I am today. I will continue to make you proud.

Table of Content

TITLE
الآية	I
DEDICATION	Ii
ACKNOWLEDGMENT	Iii
TABLE OF CONTENT	iv
TABLE OF ABBREVIATIONS	V
LIST OF TABLES	Vi
LIST OF FIGURES	Viii
ABSTRACT IN ENGLISH	xi
ABSTRACT IN ARABIC	Xii
CHAPTER ONE : INTRODUCTION
1.1. BACKGROUND	1
1.2. PROBLEM STATEMENT	3
1.3 JUSTIFICATION	4
1.4. OBJECTIVE	4
1.5 THE HYPOTHESES	5
1.6 STUDY ORGANIZATION	6
CHAPTER TWO: THEORETICAL AND LITERATURE FRAMEWORK
2.1 INTRODUCTION	7
2.2 THEORETICAL BACKGROUND	9
2.3 THE RELATIONSHIP BETWEEN FDI AND ECONOMIC GROWTH: PREVIOUS STUDIES	10
2.4 The role of Trade Liberalization on economic growth	16
2.5 LITERATURE REVIEW	19
2.6 Previous Studies: Sudan	21
CHAPTER THREE : FDI IN SUDAN
3.1 FDI in Sudan	22
3.1.1 Introduction	22
3.1.2 Foreign Direct Investment by Sector	28
3.2 Challenges Facing FDI in Sudan	30
3.3 Recommendations for Mitigating Challenges	31
CHAPTER FOUR: METHODOLOGY
4.1 INTRODUCTION	32
4.2 Data Sources and variables:
4.3 BASIC MODEL	34
4.4 Stationarity Tests Augmented Dickey-Fuller and PhillipsPerron	35
4.5 ARDL Approach to Cointegration	36
4.6 cointegration and Error Correction Representation	37
4.7 Bounds Testing Approach	38
4.8 Granger Causality Test	39
4.9 Diagnostic Tests for Residuals and Parameter Stability	40
CHAPTER FIVE: EMPIRICAL RESULTS OF THE MODEL ESTIMATION
5.1 DESCRIPTIVE STATISTICS AND GRAPHICAL ANALYSIS	43
5.2 CONCLUSION AND POLICY IMPLICATIONS	63
CHAPTER SEX: CONCLUSIONS AND RECOMMENDATIONS
6.1 SUMMARY OF FINDINGS	64
6.2 Study Recommendations	65
6.3 CONCLUSION	68
REFERENCES	72

Table of Abbreviations

Abbreviation	Meaning
FDI	Foreign Direct Investment
GDP	Growth Domestic Product
R&D	Research and Development
ADF & PP	Augmented Dickey Fuller
PP	Phillips-Perron
PLC	Product Life Cycle Theory
IMF	International Monetary Fund
UNCTAD	United Nations Conference on Trade and Development
GMM	General Method of Moment
FDI	Foreign Direct investment
WTO	World Trade organization
WDI	World Bank Database
ARDL	Autoregressive Distributed Lag

List of tables:

Title	Page no
Figure 1:3 : Sectors attracting Foreign Direct Investment FDI in Sudan	26
Table 2:5: Descriptive statistics for study variables	34
Table 3:5: Results of unit root tests	41
Table 4:5: Results of Granger Causality Tests	44
Table 5:5: Results of F-Bounds Test	49
Table 6:5: Results of Levels Equation	50
Table 7:5: Results of ARDL Error Correction Regression	52
Table 8:5: Serial Correlation Test for Residuals	58
Table 9:5: Heteroskedasticity Test for Residuals	59
Table 10:5: Ramsey RESET Test	60
Table 11:5: Stationary Test for Residuals	63

List of figures:

Title	Page no.
Figure 1:5: Evolution of variables over the time period	36
Figure 2:5: Normality Test for Residuals	56
Figure 3:5: CUSUM and CUSUM of Squares test for ARDL Model	59

Abstract

Despite the growing importance of (FDI) as a driver of economic growth, the relationship between (FDI) and economic performance in Sudan has not been adequately explored. This study examines the influence of Foreign Direct Investment (FDI) on economic growth in Sudan from 1980 to 2020, employing an Autoregressive Distributed Lag (ARDL) model within an econometric framework. The ARDL approach is utilized due to its ability to capture both short- and long-term dynamics.

This research aims to fill this gap by analyzing the long- and short-run effects of FDI on Sudan’s GDP growth, considering other key macroeconomic variables, such as the sectoral distribution of FDI inflows, and their correlation with key economic indicators, including GDP growth, employment, and technological advancement. Furthermore, the study highlights the importance of favorable policies and institutional frameworks in maximizing the benefits of FDI.

The results suggest a significant positive impact of FDI on Sudan’s economic growth, with the potential for substantial contributions from improvements in infrastructure, technology transfer, and job creation.

The long-term relationship between GDP, FDI, inflation (INF), and openness (OPEN) in Sudan, as revealed by the level equation, shows a complex interplay of factors influencing economic growth. The results show that any deviation from the long-run equilibrium relationship between the variables is corrected by approximately 2.96% each year.

Short-term results suggest that FDI has an immediate but variable effect on economic growth, with fluctuations influenced by political and economic conditions. Additionally, the main results indicate that while FDI has a positive impact on GDP growth, this effect is more pronounced during periods of economic stability. External shocks, such as economic sanctions and political instability, however, dampen the potential benefits. Furthermore, investment in technology-driven sectors appears to enhance productivity more effectively than investment in resource-based sectors.

The research findings provide valuable insights and recommendations for policymakers seeking to boost Sudan’s economic growth through Foreign Direct Investment (FDI).

المستخلص

على الرغم من الأهمية المتزايدة للاستثمار الأجنبي المباشر كمحرك للنمو الاقتصادي، إلا أن العلاقة بين الاستثمار الأجنبي المباشر والأداء الاقتصادي في السودان لم يتم استكشافها بشكل كافٍ. تبحث هذه الدراسة في تأثير الاستثمار الأجنبي المباشر (FDI) على النمو الاقتصادي في السودان خلال الفترة من 1980 إلى 2020 باستخدام نموذج الانحدار الذاتي الموزع (ARDL) ضمن إطار الاقتصاد القياسي. يتم استخدام نهج ARDL نظرًا لقدرته على التقاط الديناميكيات القصيرة والطويلة المدى.

ويهدف هذا البحث إلى سد هذه الفجوة من خلال تحليل آثار الاستثمار الأجنبي المباشر على المدى الطويل والقصير على نمو الناتج المحلي الإجمالي في السودان، مع الأخذ في الاعتبار متغيرات الاقتصاد الكلي الرئيسية الأخرى مثل التوزيع القطاعي لتدفقات الاستثمار الأجنبي المباشر، وارتباطها بالمؤشرات الاقتصادية الرئيسية مثل نمو الناتج المحلي الإجمالي، والتوظيف، والتقدم التكنولوجي. علاوة على ذلك، تسلط الدراسة الضوء على أهمية السياسات والأطر المؤسسية المواتية لتعظيم فوائد الاستثمار الأجنبي المباشر.

وتشير النتائج إلى وجود تأثير إيجابي كبير للاستثمار الأجنبي المباشر على النمو الاقتصادي في السودان، مع إمكانية تقديم مساهمات كبيرة من التحسينات في البنية التحتية، ونقل التكنولوجيا، وخلق فرص العمل.

تُظهر العلاقة طويلة المدى بين الناتج المحلي الإجمالي والاستثمار الأجنبي المباشر والتضخم والانفتاح في السودان، كما كشفت عنها معادلة المستوى، تفاعلًا معقدًا بين العوامل المؤثرة على النمو الاقتصادي. وأظهرت النتائج أن أي انحراف عن علاقة التوازن طويلة المدى بين المتغيرات يتم تصحيحه بنسبة 2.96% تقريباً كل عام.

وعلى المدى القصير، تشير النتائج إلى أن الاستثمار الأجنبي المباشر له تأثير فوري ولكنه متغير على النمو الاقتصادي، حيث تتأثر الفوائد بدرجات متفاوتة وفقًا للظروف السياسية والاقتصادية. كما تُظهر النتائج الرئيسية أن الاستثمار الأجنبي المباشر يؤثر إيجابيًا على نمو الناتج المحلي الإجمالي، ولكن يكون تأثيره أكثر وضوحًا في فترات الاستقرار الاقتصادي، بينما تؤدي الصدمات الخارجية مثل العقوبات الاقتصادية وعدم الاستقرار السياسي إلى تقليل الفوائد المحتملة. علاوة على ذلك، فإن الاستثمار في القطاعات التكنولوجية يعزز الإنتاجية بشكل أكثر فعالية مقارنة بالاستثمارات القائمة على الموارد.

وتقدم نتائج البحث رؤى وتوصيات لصانعي السياسات الذين يهدفون إلى تعزيز النمو الاقتصادي في السودان من خلال الاستثمار الأجنبي المباشر.

Chapter 1

Introduction

Background:

Foreign Direct Investment (FDI) refers to any investment made by an individual or firm from one country into another. Specifically, FDI involves a foreign entity acquiring ownership or a controlling stake in a company within the host country or establishing new businesses there. Foreign Direct Investment (FDI) plays a crucial role in a transparent and effective international economic system, serving as a significant driver of development (OECD, 2002). National policies and the framework for international investment are crucial for attracting FDI, particularly in developing countries, and for maximizing its benefits for development. Companies that receive Foreign Direct Investment (FDI) often provide employee training during their operations, which contributes to the development of human capital in the host country. Additionally, the profits generated from FDI contribute to corporate tax revenues in the host nation. As a result, many developing countries strive to create a favorable and attractive environment for foreign direct investment, based on the belief that FDI can significantly enhance their economic conditions.

Over the past twenty years, many countries around the world have experienced significant economic growth, largely due to an increase in foreign capital, particularly through Foreign Direct Investment (FDI). The proportion of net FDI in global GDP has increased fivefold in recent years. As a result, the effects and implications of FDI on economic growth have become an increasingly important topic of interestⁱ.

There is a common belief among policymakers that foreign direct investment (FDI) generates positive productivity effects for host countries. The primary avenues through which these benefits are achieved include the adoption of foreign technology and expertise. This can occur through licensing agreements, imitation, employee training, the introduction of new processes and products by foreign companies, and the establishment of linkages between foreign and domestic firms. These advantages, along with direct capital investment, suggest that FDI can play a significant role in modernizing a national economy and promoting economic development. However, the empirical evidence regarding the presence of such positive productivity externalities is not very encouraging.ⁱⁱ

The Sudan, as one of the developing countries, had suffered for a long period of accumulated foreign debts and their interest arrears, thus, Sudanese government, to reduce the impact of these debts and other external obligations, has opened the door for the foreign investors ⁱⁱⁱ. To encourage FDI, Sudan included in its Investment Law the following policies: i all the investments established in the Sudan, shall enjoy just and fair treatment; ii the State shall ensure to the Foreign Investors a treatment similar to that of the national investors and iii the invested funds shall not be subject to any arbitrary measures, or any discriminative decisions. Moreover, Sudan has implemented significant measures to enhance its economic performance, including the liberalization of trade. Many economists believe that by opening up trade, Sudan can achieve substantial development through both exports and imports. The country will also pursue foreign markets to promote its exports and acquire hard currencies, which will further support its developmental efforts. Additionally, Sudan imports various investment goods, such as equipment, machinery, and transportation means, which are essential for development projects. This research aims to investigate the relationship between FDI and economic growth in Sudan from 1980 to 2020^iv.

1.2 The Problem Statement:

Numerous studies have demonstrated that foreign direct investment (FDI) fosters economic growth by enhancing various forms of capital, including research and development (R&D). FDI can stimulate industrial production, enhance human capital, and promote collaboration through technology transfer, as well as its effects on the host country. This research aims to analyze and quantify the nature of FDI’s impact—whether positive or negative—on economic growth in Sudan between 1980 and 2020. Numerous empirical studies have suggested that FDI has a positive impact on economic growth. The study seeks to address the following two questions:

1-What is the relationship between FDIFDI and economic growth in Sudan?

2-Has FDI positively enhanced economic growth of the Sudan during the period 1980-2020?

1.3 Justification of the Study:

Foreign direct investment FDI is widely recognized as a crucial driver of economic growth, particularly in developing countries. It facilitates the transfer of technology, enhances productivity, and creates employment opportunities. However, the impact of FDI on economic growth is not uniform across all countries and periods. In the case of Sudan, a country with a complex socio-political landscape and significant economic challenges, the relationship between FDI and economic growth remains underexplored and contentious.

From 1980 to 2020, Sudan experienced various phases of political instability, economic sanctions, and internal conflicts, which have significantly influenced its economic environment. Despite these challenges, Sudan has attracted FDI, particularly in the oil sector, which has been a major contributor to its GDP. However, the overall impact of FDI on Sudan’s economic growth during this period is not well-documented or understood^v.

The existing literature provides mixed findings on the effectiveness of FDI in promoting economic growth in Sudan. Some studies suggest that FDI has had a positive impact by bringing in capital, technology, and expertise, while others argue that the benefits have been limited due to poor governance, inadequate infrastructure, and an unstable political climate. Additionally, the concentration of FDI in specific sectors, such as oil, raises questions about the sustainability and inclusiveness of the growth generated by these investments^vi.

Given these complexities, there is a need for a comprehensive analysis of the impact of FDI on Sudan’s economic growth over the past four decades. This study aims to fill this gap by examining the trends, determinants, and effects of FDI on Sudan’s economic performance from 1980 to 2020. By doing so, it seeks to provide insights into how FDI can be leveraged more effectively to promote sustainable and inclusive economic growth in Sudan^vii.

1.4 The Objectives:

The overall objective of this research is to determine the impact of FDI on economic growth. The specific objectives are the following:

1. To Analyze the Trends of FDI in Sudan 1980-2020

Examine the historical trends and patterns of FDI inflows into Sudan over the four-decade period.
Identify the key sectors that have attracted the most FDI and the reasons behind these trends.

2. To Assess the Determinants of FDI in Sudan

Investigate the factors that have influenced FDI inflows into Sudan, including political stability, economic policies, infrastructure, and natural resources.
Evaluate the role of government policies and international agreements in shaping FDI trends.

3. To Evaluate the Impact of FDI on Sudan’s Economic Growth

Analyze the relationship between FDI and key economic indicators such as GDP growth, employment, and technological advancement.
Determine the extent to which FDI has contributed to economic diversification and development in Sudan.

4. To Examine the Sectoral Impact of FDI

Assess the impact of FDI on different sectors of the Sudanese economy, with a particular focus on the oil sector.
Explore the potential benefits and drawbacks of sector-specific FDI concentration.

5. To Identify Challenges and Opportunities for FDI in Sudan

Identify the main challenges faced by foreign investors in Sudan, including political, economic, and infrastructural barriers.
Highlight potential opportunities for attracting and retaining more FDI in the future.

6. To Provide Policy Recommendations

Based on the findings, provide actionable policy recommendations to enhance the positive impact of FDI on Sudan’s economic growth.

1.5 The Hypotheses:

In order to address the research problem in an organized and systematic manner, the following research hypotheses have been formulated:

There is no significant relation between GDP, Inflation, Exchange rate and the Trade Openness with FDI
FDI inflows had an insignificant effect on economic growth over the studied period.

1.6 Study Organization:

This research has been divided into Sex chapters; chapter one contains Introduction, the problem statement and justification, the objectives, hypotheses, and organization of the study.

Chapter two contains literature review, theoretical studies, and an overview of FDI in Sudan. Chapter three contains the methodology; chapter four contain the model specification while chapter five represents the empirical results of the model estimation. Finally, chapter sex provides a summary of findings, recommendations and conclusion that can be drawn from the results.

Chapter 2

Theoretical and Literature Framework

2.1 Introduction

Development theories suggest that developing countries are often trapped in vicious cycles of poverty, characterized by low incomes, savings, and investments. Foreign Direct Investment (FDI) refers to cross-border investments made by a resident entity in one economy—the direct investor—aiming to establish a lasting interest in an enterprise—the direct investment enterprise—located in a different economy. FDI typically involves a long-term relationship between the direct investor and the enterprise, with the direct investor exerting significant influence over the management of the enterprise.

It is often argued that FDI can help countries alleviate poverty by increasing the volume and efficiency of investments, allowing domestic investment to surpass domestic savings. Additionally, FDI is believed to bring capital for productive development to the host country, along with a substantial transfer of technical and managerial knowledge and skills.

Moreover, FDI indirectly contributes to poverty reduction through economic growth, which leads to improved living standards. This growth is brought about by an increase in GDP, advancements in technology and productivity, and enhancements in the overall economic environment.

Although many theories suggest that foreign direct investment (FDI) has a positive impact on economic growth, there are arguments indicating it may also hinder the growth prospects of a host economy. One significant issue is the crowding-out effect on domestic investment, which occurs when local businesses abandon their investment plans to avoid competing with more efficient foreign companies. As a result, the resources released do not get redirected to activities where local enterprises have a competitive advantage (UNCTAD, 1999, p. 171).

Additionally, there are concerns that transnational corporations may provide their foreign affiliates with inadequate or inappropriate technological capabilities. Furthermore, the outflows of earnings from FDI can negatively affect the balance of payments in the host country.

2.2 Theoretical Background

The early concept of Foreign Direct Investment (FDI) can be seen as an extension of classical theories of international trade, originally rooted in economics. The first significant attempt to explain FDI was based on Ricardo’s theory of comparative advantage.

Heckscher-Ohlin’s 1933 theory is one of the foundational concepts for understanding international capital movements in the context of trade, emphasizing the differences in resource endowments between countries. This theory builds upon David Ricardo’s idea of comparative advantage by predicting trade patterns and production based on the factor endowments of a trading region. Essentially, the model suggests that countries will export products that make use of their abundant and inexpensive factors of production, while they will import products that rely on their scarce factors.

However, FDI cannot be fully explained by Ricardo’s theory, as it is limited to two countries and two products, assuming perfect mobility of factors at the local level.

Theoretical studies on the impact of Foreign Direct Investment (FDI) on the economic development of host countries can be categorized into two types of theories. The first is the theory of economic modernization, which is grounded in neoclassical and endogenous growth theories. The second is the theory that focuses on the dependence of the economy on FDI.

The neoclassical theory of economic growth, developed by R. Solow (1956) and W. Rostow (1956), views FDI as a vital growth factor for developing countries. In Rostow’s model, FDI is seen as a source of capital and technology transfer essential for economic transformation. Solow emphasizes that the influx of foreign capital and technological progress are crucial variables for production growth and, subsequently, overall development.

Endogenous growth theories explain the positive influence of FDI on a country’s economic growth by highlighting the expansion of knowledge and the acquisition of new skills among the workforces. They also point out the introduction of alternative management methods and organizational mechanisms, which facilitate the rapid dissemination of technology and enhance the efficiency of local companies.

A well-known theoretical approach to understanding the origin and form of internationalization is the product life cycle theory, developed by Raymond Vernon in 1966. Vernon suggested that companies that are the first to innovate a product in their home markets often engage in foreign direct investment (FDI) to manufacture that product for consumption in international markets. The Product Life Cycle Theory PLC theory suggests that firms in the development of their output, they go through different stages. These stages of products started with introductory phase, then growth, followed by maturity and the last stage is decline phases. The length of time of each stage where the product remains in it, is subject to different factors. Thus, Vernon’s view is arguing that companies enter foreign direct investment in certain stages in the life cycle of a product they have pioneered. Many companies introduce old products to new markets by investing in other advanced countries, where local demand is large enough to absorb local production. As a result, FDI is considered locally market-oriented in the early stage; subsequently, they shift production to developing countries when product standardization and market saturation lead to price competition and cost pressures, and in this case the investment in developing countries, where a lower labor cost and higher demands, seems to be the best way to reduce costs. Thus, in the final stage of the product cycle, FDI will be export-oriented, primarily driven by labor cost considerations. At the stage where PCL is declining, the country that innovated the product becomes a net importer of the product. However, PLC theory fails to explain why it is profitable for a firm to undertake FDI at such times, rather than continuing to export from its home base or licensing a foreign company to produce its product^viii.

2.3 The relationship between FDI and economic growth: previous studies

Over the past two decades, the importance of Foreign Direct Investment (FDI) has increased significantly in the developing world, as the number of developing countries that have successfully attracted substantial and rising amounts of inward FDI has grown. Economic theory has identified several ways that indicate the FDI inflows might be beneficial to the host economy. Yet, the empirical literature has lagged and has had more trouble identifying these advantages in practice. Most prominently, many applied papers have looked at the FDI-GDP growth nexus, but their results have been far from conclusive.1 Despite the lack of strong conclusions, it is somewhat surprising that most countries continue to actively implement policies aimed at attracting more foreign direct investment (FDI). Mankiw, Romer, and Weil (1992) revised the original model and argued that excluding human capital accumulation from Solow’s model would result in a biased estimation of the coefficients related to saving and population growth. They contended that differences in income per capita across countries are influenced by variations in the saving rate, population growth rate, and labor productivity levels. The endogenous growth models, which began with Romer’s groundbreaking work in 1986, incorporated a theory of technological change into the production process. Helpman (2004) states that endogenous growth theory highlights two essential channels through which investment influences economic growth. The first is its effect on the variety of products available, and the second is its impact on the stock of knowledge accessible for research and development. Economic models of endogenous growth have been used to analyze the effect of foreign direct investment (FDI) on economic growth, particularly through technology diffusion (Barro, 1990; Barrell and Pain, 1997). Additionally, FDI can foster economic development by creating dynamic comparative advantages that lead to technological advancements (Balasubramanyam et al., 1996; Borensztein et al., 1998).Romer (1990) and Grossman and Helpman (1991) have refined Romer’s 1986 model, which assumes that endogenous technological progress is the primary driver of economic growth. Romer argues that FDI accelerates economic growth by enhancing human capital, the most crucial element in research and development efforts. In contrast, Grossman and Helpman (2004) argue that endogenous growth theory emphasizes two key channels through which investment affects economic growth. The first is its effect on the variety of products available, and the second is its impact on the stock of knowledge accessible for research and development. Economic models of endogenous growth have been used to analyze the effect of foreign direct investment (FDI) on economic growth, particularly through technology diffusion (Barro, 1990; Barrell and Pain, 1997). Additionally, FDI can foster long run economic growth by creating dynamic comparative advantages that lead to technological advancements (Balasubramanyam et al., 1996; Borensztein et al., 1998).

In contrast to the positive conclusions drawn by some researchers, Reis (2001) proposed a model that examines how Foreign Direct Investment (FDI) affects economic growth, particularly when investment returns can be repatriated. She argues that following the introduction of FDI, domestic firms in the research and development (R&D) sector may be replaced by foreign firms. This transition can decrease domestic welfare due to the outflow of capital returns to foreign entities. The impact of FDI on economic growth, according to this model, hinges on the relative strength of interest rate effects. Specifically, if the world interest rate exceeds the domestic interest rate, FDI will negatively affect growth; conversely, if the world interest rate is lower than the domestic rate, FDI can have a positive effect on growth. Additionally, Firebaugh (1992) identifies several reasons why FDI inflows might be less beneficial than domestic investment and could even be detrimental. Countries may derive less benefit from FDI compared to domestic investments for several reasons: multinationals are often less likely to contribute to government revenue, FDI tends to discourage local entrepreneurship, multinational companies are less inclined to reinvest profits, they are less likely to establish linkages with domestic firms, and they may adopt excessively capital-intensive production techniques. Moreover, FDI can be detrimental if it “crowds out” domestic businesses and fosters undesirable consumption patterns.^ix.

In a widely referenced study, Borensztein et al. (1998) investigated the impact of foreign direct investment (FDI) on economic growth using a cross-country regression framework. They analyzed FDI outflows from OECD countries to sixty-nine developing nations over the period from 1970 to 1989. Their findings indicate that FDI is a crucial mechanism for adopting new technologies, significantly contributing to growth, even more so than domestic investment. Additionally, they found a significant positive relationship between FDI and the level of human capital, indicating that FDI has a beneficial impact on economic growth. However, they caution that this higher productivity associated with FDI only occurs if the host country possesses a minimum threshold of human capital.

In a new growth framework, Bulasubramanyam examined the relationship between foreign direct investment (FDI) and economic growth within different trade policy regimes, specifically focusing on countries that promote exports and those that substitute imports. Using cross-sectional data from 46 developing countries spanning the years 1970 to 1985, their analysis supported Bhagwati’s hypothesis, which suggests that FDI enhances growth in countries that adopt export promotion policies. Li and Liu (2005) utilized both single-equation and simultaneous-equation system techniques to explore the endogenous relationship between FDI and economic growth. Analyzing a data panel from 84 countries over the period from 1970 to 1999, they discovered a positive effect of FDI on economic growth through its interaction with human capital in developing nations. However, they also identified a negative effect of FDI on growth related to the technology gap. Bengoa et al. (2003) examined the relationship between Foreign Direct Investment (FDI) and economic growth using panel data for 18 Latin American countries from 1970 to 1999. Their findings indicated that FDI has a positive and significant impact on economic growth in the host countries. Nonetheless, similar to most other studies, they concluded that the benefits of FDI for host countries depend on having adequate human capital, political and economic stability, and a liberalized market environment.

Several economists, including De Gregorio and Guidotti (1995), Alfaro et al. (2004), and Durham (2004), have noted the volatility of (FDI) and the financial adjustments required due to this volatility. These studies have generally argued that countries with well-developed financial markets can attract higher volumes of FDI inflows and enable host countries to benefit more extensively from them thanks to their ability to adapt to fluctuations in capital inflows.

In contrast, Carkovic and Levine employed the General Method of Moments (GMM) to investigate the relationship between (FDI) and economic growth. Using data from 1960 – 1995 across a large cross-country dataset, they found that FDI inflows do not have a direct influence on economic growth, nor do they impact growth through their effects on human capital. Similarly, Choe (2003) employed a panel Vector Autoregression (VAR) model to investigate the interaction between FDI and economic growth in 80 countries from 1971 – 1995. He identified evidence of a Granger causality relationship between FDI and economic growth in both directions, but the effects were stronger from economic growth to FDI rather than vice versa.

Bende et al. (2001) examined the impact of foreign direct investment (FDI) and its spillover effects on the economic growth of the ASEAN-5 countries between 1970 – 1996. They found that FDI accelerates economic growth both directly and through spillover effects. The study revealed a positive and statistically significant impact of FDI on economic growth for Indonesia, Malaysia, and the Philippines. In contrast, a negative relationship was identified for Singapore and Thailand. Similarly, Marwah and Tavakoli (2004) investigated the effect of FDI on economic growth in Indonesia, Malaysia, the Philippines, and Thailand using annual time series data from 1970 – 1998. Their findings indicated that FDI has a positive correlation with economic growth in all four countries.

Ombeswa Ralarala investigated the concept of Foreign Direct Divestment (FDD) in Sub-Saharan African countries using annual data from 1998 – 2018. To develop the FDD model, a panel autoregressive distributed lag (ARDL) approach was utilized. The findings from the panel ARDL long-run equation indicated that lending rates and urbanization have a negative and significant impact on (FDI). Additionally, the study found no significant influence of real gross domestic product (GDP) per capita on FDI. In contrast, trade openness demonstrated a significant positive effect on foreign direct investment. The authors recommended implementing policies aimed at increasing FDI by lowering borrowing costs, as this tends to encourage more foreign direct investment. They noted that real GDP per capita is limited in its usefulness for policy-making purposes in this context. Trade openness enhances a country’s access to the global market; therefore, promoting foreign trade through exporting complex and sophisticated products, engaging in trade liberalization, establishing free trade agreements, and creating open trade systems could contribute to an increase in foreign direct investment in the selected countries. Lastly, urbanization appears to deter foreign direct investment. Consequently, the authors suggest that countries should focus on investing in infrastructure and alleviating poverty in rural areas, transforming them into urban areas to mitigate the negative effects of urbanization on FDI.

Belesity Bekalu Ayenew (2022) conducted a study titled “The Effect of Foreign Direct Investment on the Economic Growth of Sub-Saharan African Countries: An Empirical Approach.”^x, This research examined the impact of foreign direct investment (FDI) on the economic growth of 22 Sub-Saharan African nations from 1988 to 2019. To analyze the short- and long-term effects of FDI on economic growth, the study utilized the PMG/ARDL model. Additionally, panel unit root tests and panel co-integration tests were employed to enhance the accuracy of the model’s estimation. The findings revealed that foreign direct investment has a positive and statistically significant long-term effect on economic growth; however, its impact in the short term is statistically insignificant. The study concludes that FDI plays an important role in promoting long-term economic growth. Therefore, it is recommended that Sub-Saharan African countries prioritize attracting foreign direct investment.

Mohammad et al. (2022) examined the long-run and short-run impacts of three different sources of financial development on foreign direct investment (FDI) inflows, while controlling for inflation rates, trade openness, and real growth rates in middle-income economies, including Sudan, from 1980 to 2020. The data were sourced from the World Development Indicators and the International Monetary Fund, employing an annual frequency over the period of 1980–2020. The researchers analyzed the data using descriptive statistics, correlation analysis, a cross-sectional dependency test, a first-generation unit root test, a cointegration test, optimal lag selection, and panel autoregressive distributed lag (ARDL) estimations. Their results revealed no cross-sectional dependency, with the dependent variable showing stationarity in first differences and the independent variables exhibiting stationarity at both levels and in first differences, thus validating the estimations based on the panel ARDL approach. Furthermore, cointegration was confirmed using the Pedroni and Westerlund methods. The optimal lag was determined based on the most frequently selected lag length among all 53 economies in the middle-income group. The Hausman test confirmed the consistency and efficiency of the dynamic fixed-effects model as the estimation method using the panel ARDL technique.

The findings concluded that the development of financial markets plays a significant role in enhancing FDI inflows in middle-income economies, both in the short and long run, supporting the resource-based theory for these economies. However, financial institutional development and overall financial development did not significantly contribute to attracting FDI in the target population, suggesting that the institution-based theory of financial development is not applicable to middle-income countries. Additionally, the study indicated that rising inflation levels could strongly deter foreign investors from investing in middle-income countries as host economies during the study period. Moreover, the researchers found that increasing trade openness can have a substantial impact on FDI inflows in middle-income economies in the long run, although its effect may be weaker in the short term. Finally, the real growth rate was found to significantly attract FDI inflows in middle-income economies from 1980 to 2020 in the long run.

Norbert Zavery Mwitta (2022), in his paper “Impact of Foreign Direct Investment on Economic Growth: Empirical Evidence from Tanzania, 1990-2020,” analyzed the effects of foreign direct investment (FDI) on Tanzania’s economic growth rate using the Vector Error Correction Model (VECM). The objective was to determine whether FDI inflows have a positive or negative influence on the long-term real GDP growth rate. The analysis revealed a statistically significant positive correlation between the real GDP growth rate and the FDI inflow-to-GDP ratio. Conversely, a negative correlation was found between the gross fixed capital formation to GDP ratio and the real GDP growth rate. The author attributed this negative correlation to the current state of public investment in the country. To foster sustainable and inclusive economic growth in Tanzania, the paper recommends that the government continue to refine its policies related to inward (FDI), public investment, and the export sector.

2.4 The role of Trade Liberalization on economic growth:

According to the international economic regime, trade liberalization is required as a prerequisite for more FDI inflows, however, in countries like Sudan which depends on tax and tariffs as main source of its revenues and national budget, this can be so difficult to be achieved ^xi. Although taxation levels are generally low, the tax regime is complex and characterized by a wide array of industry-specific corporate income rates and incentives. Corporate income tax rates called business profit tax are 0 per cent for agriculture, 10 per cent for industry and 15 per cent for most services. The corporate income tax on banks recently increased from 15–30 percent^xii. Mining and oil are taxed at 30 per cent and 35 percent, respectively. Moreover, UNCTAD’s Investment Policy Review 2015 addressed that Sudan has made efforts to diversify the economy and attract FDI into new industries, it also highlighted that more is needed to build transparent and predictable business environment^xiii. Thus, the relationship between trade openness and economic growth can be understood through the expansion of trade and investment opportunities that an open trade regime provides. This environment allows countries to specialize in and export products where they have a comparative advantage. It is well established that exports play a crucial role in driving economic performance. Many empirical studies suggest that exports are the primary channel through which trade liberalization impacts output levels and, ultimately, economic growth rates.

Additionally, liberalizing trade can enhance productivity and make goods more accessible within a country. On the import side, another aspect of trade openness —increased competition —puts pressure on domestic industries. As a result, domestic firms must improve their productivity to remain viable; those unable to adapt face heightened competition and may be forced to exit the market. Furthermore, trade liberalization enables companies to access high-quality components, parts, and machinery at lower prices, resulting in improved overall productivity^xiv.

To meet the requirements of the international economic regime, Sudan applied to join the World Trade Organization (WTO) in 1994, Sudan’s Working Party was established on 25 October 1994. The Working Party met for the fifth time in July 2021. However, Sudan has not yet completed the accession process, as it is subject to procedures and lengthy negotiations, and many reforms have been implemented in this regard.

2.5 Literature review

Khalafalla Ahmed 2013^xv investigated the impact of human capital on economic growth in Sudan from 1982 to 2009. It employed a simultaneous equation model that links human capital—specifically school attainment and investments in education and health—to economic growth, total productivity, foreign direct investment, and the Human Development Index. Utilizing the three-stage least squares technique, the empirical results revealed that the quality of education plays a crucial role in driving economic growth. Additionally, the health quality factor has a positive influence on economic growth, as expected. However, total factor productivity, which primarily reflects the state of technology, has an adverse effect on economic growth and human development, largely due to the prevalence of outdated and obsolete technology.

Osama examined the impact of foreign direct investment (FDI) on Sudan’s economic growth from 1990 to 2006. He argued that FDI had a positive and significant effect on the country’s economic growth during this period. Additionally, he found that the impact of FDI on growth was greater for human capital than for labor. His research indicated that FDI has a positive influence on economic growth in both the short term and the long term. Moreover, he noted that the interaction between FDI and human capital has a long-term effect on growth that is equivalent to that of the interaction between FDI and labor; however, in the short term, the impact of FDI on human capital is greater than its impact on labor.

Abdalla Sirag ^xvi investigated the impact of financial development and foreign direct investment (FDI) on economic growth in Sudan, utilizing annual data from 1970 to 2014. His analysis employed unit root and cointegration tests, both with and without accounting for structural breaks, along with the Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) techniques to estimate the long-run model. The results from the cointegration tests indicated a long-run relationship among the variables, even after adjusting for structural breaks. The findings demonstrated that both financial development and FDI have a positive and significant correlation with economic growth in Sudan. Notably, financial development was found to contribute more significantly to economic growth than FDI. Furthermore, the study revealed that FDI enhances economic performance through its positive impact on financial development. Interestingly, the effect of financial development on economic growth is further strengthened by FDI inflows.^xvii.

Yassin Elshain Yahia^xviii examined the impact of foreign direct investment (FDI) inflows on domestic investment in Sudan from 1976 to 2016. He applied the autoregressive distributed-lag bounds test for cointegration and Granger causality. The empirical results indicate that FDI has a crowding-out effect on domestic investment in Sudan, confirming the existence of a cointegration relationship between the two variables. The study found significant associations between economic growth, exchange rates, macroeconomic stability, and natural resource rents with domestic investment in both the short and long run. In contrast, FDI emerged as a long-term determinant of domestic investment. Additionally, the error correction model showed that the system corrects previous period disequilibrium at an annual rate of 35%. The Granger Causality results reveal unidirectional causal flows from FDI, exchange rates, macroeconomic stability, natural resource rents, and trade openness to domestic investment. The study suggests several policy measures to promote macroeconomic stability, including controlling inflation, maintaining a flexible exchange rate to foster economic growth, and developing effective strategies to attract Foreign Direct Investment (FDI) that can generate technological advancements and market share spillovers.

Elwasila S. E. Mohamed ^xix analyzed the impact of external debt on Sudan’s economic growth. The study used gross domestic product (GDP) as the dependent variable to measure economic growth, with the ratio of external debt to exports, the exchange rate, and foreign direct investment (FDI) as explanatory variables. Annual time series data from the period 1969 to 2015 were employed.

For the analysis, the study utilized several econometric techniques, including the Augmented Dickey-Fuller (ADF) unit root test for stationarity, the Johansen cointegration method, and the Vector Error Correction Model (VECM). The cointegration test revealed the presence of a long-run equilibrium relationship among the variables under study. Findings from the VECM revealed that external debt, as represented by the external debt-to-exports ratio, has made a positive contribution to the Sudanese economy. In contrast, both the exchange rate and foreign direct investment were found to have an adverse effect on GDP growth, aligning with the results of most empirical macroeconomic studies. The study recommends that the government prioritize macroeconomic and price stability, while maintaining access to foreign loans to compensate for low domestic savings levels, thereby facilitating long-term economic growth. Additionally, there is a need to stabilize the exchange rate and to further investigate the perceived impact of FDI on economic growth, particularly in terms of enhancing human capital and promoting institutional development.

2.6 Previous Studies: Sudan

Hisham Mohamed and Omer Ali ^xx conducted a study on the determinants of foreign direct investment (FDI) in Sudan, covering the period from 1970 – 2010. They examined several factors that could influence FDI, including market size, inflation rate, exchange rate, indirect taxes, trade openness, and investment incentive policies. To analyze the short- and long-run dynamics of these FDI determinants, the study employed cointegration and error-correction techniques. The Johansen cointegration test revealed four cointegrating relationships among the series, indicating a long-run relationship among the FDI determinants. The results from the long-run FDI equation suggested that FDI flows in Sudan are significantly affected by market size, inflation rate, exchange rate, and investment incentive policies. The error-correction term indicates that approximately 17% of the total disequilibrium in FDI flows is corrected annually. Additionally, the Granger causality results demonstrated a unidirectional causality where the exchange rate, investment incentive policy, and market size all lead to changes in FDI.

Issam ^xxi analyzed the impact of foreign direct investment (FDI) on economic growth in Sudan from 1982 to 2007. The study aimed to investigate how to attract sufficient FDI flows to close the saving-investment gap necessary for sustaining economic growth. The hypothesis was that sustained efforts to promote political and macroeconomic stability, coupled with essential structural reforms and alignment with emerging markets, would encourage FDI inflows into various sectors in Sudan. The key findings of the study indicate that FDI positively correlates with economic growth in Sudan. There is clear evidence of a one-way causality from FDI to economic growth throughout the entire period studied. This means that FDI has a significant positive effect on GDP growth by promoting exports, improving the balance of payments, creating job opportunities, and enhancing labor quality and production efficiency. However, the study also highlights the challenge of constructing accurate and comparable measures of FDI data by sector in Sudan, which is a common issue faced by many developing countries over several decades^xxii.

Mohammed Elhaj conducted an empirical study to test the “giving hope hypothesis.” This hypothesis proposes that foreign direct investment (FDI) not only increases domestic investment in recipient countries but also fosters a more optimistic business environment, which can stimulate growth in domestic investments. To validate this hypothesis, the study analyzed time series data from Sudan covering the period from 1980 to 2013. The empirical analysis utilized co-integration and Vector Error Correction Model (VECM) techniques. The findings revealed a complementary relationship between FDI and domestic investment in Sudan, supporting the hope hypothesis. Additionally, the other variables included in the model showed the expected signs^xxiii.

Chapter 3

Descriptive Analysis of FDI in Sudan

3.1 FDI in Sudan

3.1.1 Introduction

Although it might be argued that Sudan has many centrifugal factors which may discourage foreign direct investment, such as economic and political instability, as well as undeveloped nature of physical infrastructure and the lack of adequate qualified and efficient manpower, Sudan’s economy witnessed apparent FDI flows after signing Peace Agreement 2005 ^xxiv. It has been noted that privatizations and the opportunities created by the government for foreign investors in the lucrative and expanding oil, telecommunications, and banking sectors contributed to the peak in 2006. According to the Bank of Sudan statistics in 2010, 78.6% of registered capital inflows going to the service sector, 12% going to industrial sector, 6% to transport and 3% divided between exports & imports sector and agriculture and livestock sector.

During that period, foreign direct investment (FDI) in Sudan’s oil sector reached its peak. The Bank of Sudan did not have detailed data on the oil sector, other than what was provided by the Ministry of Energy. According to the Ministry, foreign investments in the oil sector amounted to $19.7 billion. By December 2009, repayments totaled $10.8 billion. As a result, net foreign investments can be estimated at approximately $8.9 billion. Additionally, the registered capital supporting companies in the oil sector was approximately $246 million. Therefore, the total net foreign investment in the oil sector is estimated to be approximately $9.1 billion.

3.1.2 Foreign Direct Investment by Sector:

Foreign Direct Investment in Sudan can be divided by sector of investment into extractive or oil sector and non-oil sector. However, the oil sector was dominant as compared to other sectors during most time of this study timeframe.

Regarding the extractive sector, it could be argued that the options and alternatives for investors are rather limited in terms of where to invest in a foreign location. They tend to invest where resources are relatively abundant and a profitable venture is envisioned. Investments in the extractive sectors, such as petroleum and mining, by emphasizing the locational-specific factor of production at the resource location, may undermine many other negative elements in the host country’s investment climate. Moreover, oil and energy, by nature, are international strategic resources for which nations strive and compete. The oil sector in Sudan is not an exception to the rule^xxv.

The dimension toward FDI in Sudan during the study period was that the lucrative investment opportunities in the oil sector stimulated FDI despite not only the unfavourable investment climate but also the general hostile international/Western attitude towards firms investing in Sudan, especially in the oil sector^xxvi.

The non-oil sector in Sudan has attracted fewer investors compared to the oil sector, with most investments coming from Arab countries. One of the main reasons for this trend appears to be related to the challenges faced by Arab investors in developed countries, particularly following the events of September 11, 2001. Interestingly, relations between wealthy Arab nations and the capitalist world have been strained during the two significant periods of foreign investment inflows into Sudan. The first strain arose from the oil embargo and rising prices after the Arab-Israeli War in 1973, while the second occurred after the events of September 11.^xxvii.

Several important motivations may drive relationships between Sudan and Arab countries. These include their geographical proximity, shared language and culture, and the strong connections formed between Sudanese workers abroad and investors or employers in Arab nations, particularly those in the Gulf Cooperation Council (GCC) states. Additionally, Sudan is viewed as a robust opponent of the United States, distinguishing it from many other regimes in the region. Another significant reason for these ties may be the historical concern among Arab countries, especially wealthier GCC members, to secure alternative sources of essential foodstuffs. With rising food prices and vulnerabilities stemming from reliance on traditional suppliers, Sudan is recognized for its vast and diversified agricultural resources. The country is regarded as one of three nations globally expected to play a key role in enhancing global food security^xxviii. Due to several discouraging factors, including high risks and significant investment requirements in the agricultural sector, investments in this area have remained low. Nevertheless, it is noteworthy that over 80 percent of approved agricultural projects are owned by Arab investors, with approximately 70 percent of these originating from GCC countries. Additionally, Sudan’s issues with transparency, corruption, and other related factors create an environment that may be attractive to certain firms that can navigate and capitalize on such conditions. Some investors in non-oil sectors seem to be more focused on seeking profitable short-term deals rather than making long-term investments. Furthermore, public discontent has been evident regarding several privatization deals, with some of these deals facing criticism in parliament. Consequently, the government has had to intervene in certain cases by purchasing shares, nullifying agreements, and addressing discrepancies related to some investors^xxix.

Despite the importance of the agricultural sector, it suffers from bottlenecks and drawbacks that have affected its performance adversely. The lack of infrastructural development in roads, transport linking production areas to products markets had their roles in not being a target of foreign investors towards this sector^xxx.

Figure1:3 :Sectors attracting Foreign Direct Investment FDI in Sudan

key sectors attracting Foreign Direct Investment FDI in Sudan^xxxi:

Oil and Gas

The oil and gas sector has been the cornerstone of FDI in Sudan. The discovery of oil in the late 1990s transformed the economic landscape, attracting significant investments from countries such as China, Malaysia, and India. Key points include:

– Major Investors: China National Petroleum Corporation CNPC, Petronas Malaysia, and ONGC Videsh India are among the largest investors.

– Infrastructure Development: Investments have been directed towards the development of oil fields, pipelines, and refineries.

– Economic Impact: The oil sector has contributed significantly to GDP, export revenues, and government income. At its peak, oil accounted for about 70% of Sudan’s GDP.

Agriculture

Sudan’s vast arable land and favorable climate conditions make agriculture a key sector for FDI. The country has attracted investments from Gulf countries and other regions focusing on various agricultural projects:

– Key Crops: Wheat, sorghum, cotton, and gum arabic are major crops attracting investment.

– Investment Projects: Large-scale agricultural projects, such as the Kenana Sugar Company, have received substantial foreign investment.

– Economic Contribution: Agriculture remains a vital part of Sudan’s economy, providing employment and contributing to food security.

Mining

The mining sector, particularly gold mining, has seen increased foreign investment in recent years. Sudan is rich in mineral resources, including gold, silver, chrome, asbestos, manganese, gypsum, mica, zinc, and iron:

– Gold Mining: Companies from Canada, Australia, and China have invested in gold mining operations.

– Other Minerals: Investments are also being made in the extraction of other minerals, contributing to export revenues and economic diversification.

– Regulatory Environment: The government has implemented policies to attract foreign investment in mining, including tax incentives and streamlined licensing processes.

Telecommunications^xxxii

The liberalization of the telecommunications sector has attracted significant FDI, leading to improved infrastructure and services:

– Major Players: Companies like Zain Kuwait and MTN South Africa have made substantial investments in Sudan’s telecommunications infrastructure.

– Technological Advancements: Investments have led to the expansion of mobile networks, internet services, and digital technologies.

– Economic Impact: The telecommunications sector has contributed to economic growth by improving connectivity and enabling new business opportunities.

Infrastructure

Investment in infrastructure is crucial for Sudan’s economic development. Foreign investments have been directed towards various infrastructure projects, including:

– Transportation: Development of roads, railways, and ports to improve connectivity and facilitate trade.

– Energy: Investments in power generation and distribution, including renewable energy projects such as solar and wind.

– Water and Sanitation: Projects aimed at improving access to clean water and sanitation facilities.

Manufacturing

The manufacturing sector, though less developed, has potential for growth and has attracted some foreign investment:

– Key Areas: Food processing, textiles, and construction materials are areas of interest for foreign investors.

– Economic Diversification: Investment in manufacturing is essential for diversifying Sudan’s economy and reducing dependence on oil and agriculture.

Foreign Direct Investment has been instrumental in the development of key sectors in Sudan, including oil and gas, agriculture, mining, telecommunications, infrastructure, and manufacturing. By addressing the challenges faced by foreign investors and creating a more favorable investment climate, Sudan can continue to attract and benefit from FDI, contributing to sustainable economic growth and development^xxxiii.

3.2 Challenges Facing FDI in Sudan:

Although Sudan has great investment potentials in different sectors such as agriculture, extractive recourses, and animal wealth, it also faces different kind of obstacles that negatively affect the achieved level of investments. These include: ^xxxiv

Political Instability

Sudan has experienced significant political instability over the years, including civil wars, coups, and ongoing conflicts. This instability creates an unpredictable environment for investors, making it difficult to plan long-term investments.

Regulatory and Legal Issues

The regulatory framework in Sudan can be complex and inconsistent. Investors often face challenges related to:

– Bureaucracy: Lengthy and complicated administrative procedures can delay projects.

– Legal Uncertainty: Frequent changes in laws and regulations can create uncertainty for investors.

– Property Rights: Issues related to land ownership and property rights can be a significant barrier.

Economic Sanctions

Sudan has been subject to international economic sanctions, particularly from the United States. These sanctions have restricted access to international financial markets and limited the ability of foreign companies to operate in Sudan.

Infrastructure Deficits

The lack of adequate infrastructure, including transportation, energy, and telecommunications, poses a significant challenge for foreign investors. Poor infrastructure can increase operational costs and reduce the efficiency of business operations.

Corruption

Corruption is a pervasive issue in Sudan, affecting various levels of government and business. Investors may encounter demands for bribes or face unfair competition from companies with close ties to government officials.

Currency and Financial Risks

Sudan has faced significant currency instability and inflation. The fluctuating value of the Sudanese pound and difficulties in repatriating profits can deter foreign investment. Additionally, access to foreign exchange can be limited.

Security Concerns

Ongoing conflicts and security issues in certain regions of Sudan can pose risks to foreign investments. Companies may need to invest in additional security measures to protect their assets and personnel.

Social and Cultural Barriers

Foreign investors may also face challenges related to cultural differences and social norms. Understanding and navigating these differences is crucial for successful business operations.

3.3 Recommendations for Mitigating Challenges

1. Engage with Local Partners: Collaborating with local businesses and stakeholders can help navigate regulatory and cultural challenges.

2. Conduct Thorough Due Diligence: Understanding the local market, legal environment, and potential risks is essential before investing.

3. Advocate for Policy Reforms: Working with industry groups and international organizations to advocate for regulatory and legal reforms can help create a more favourable investment climate.

4. Invest in Risk Mitigation: Implementing strategies to manage political, economic, and security risks can help protect investments.

By addressing these challenges, Sudan can create a more attractive environment for foreign investors, ultimately contributing to economic growth and development.

Chapter 4

Methodology

ARDL Steps, with Equations and Hypothesis

4.1 Introductions:

Macroeconomic indicators of an economy are considered as the major pull factors of FDI inflows to a country. The analysis of the various theoretical rationale and existing literature provides a base in choosing the right combination of variables that explains the effects of the flows of FDI in economic growth in Sudan. In order to have the best combination of variables for the effects of FDI, the different alternative combination of variables was identified and then will be estimated in this chapter^xxxv.

This chapter gives an outline of research methodologies that will be followed in the study. Also, provides information in volatility measurement and the instrument that was used for data collection, and it also describes the methods that will be used to analyze the data^{^xxxvi}.

4.2 Data Sources and variables:

The variables used in the study are FDI, GDP, Inflation, Exchange Rate, and Trade Openness. The methodology of the study will use: first, Augmented Dickey Fuller and Phillips-Perron ADF & PP tests for testing unit roots, then by applying Johansen approach the co-integration will be tested to determine the long run relationship among variables. The error-correction model will be used to estimate the short run dynamic causality relationship.

The data used in this study was collected from different sources including

1. World Bank:

– The World Bank provides comprehensive data on FDI inflows, economic indicators, and other relevant statistics for Sudan. You can access data on FDI as a percentage of GDP, net inflows, and more.

2. International Monetary Fund IMF:

– The IMF offers data on international financial statistics, balance of payments, and economic performance indicators. This can be useful for understanding the broader economic context in which FDI occurs.

3. CEIC Data:

– CEIC Data provides detailed historical data on FDI in Sudan, including quarterly and annual figures. This source can help you track FDI trends and analyze their impact over time.

4. Central Bank of Sudan:

– The Central Bank of Sudan publishes reports and data on FDI, economic performance, and financial statistics. These reports can provide insights into the regulatory environment and economic policies affecting FDI.

5. United Nations Conference on Trade and Development UNCTAD:

– UNCTAD offers data on FDI flows, investment trends, and economic development indicators. Their World Investment Reports can be particularly useful for comparative analysis and understanding global FDI trends.

6. Sudan Bureau of Statistics:

– The national statistical agency provides data on various economic indicators, including GDP, employment, and sectoral performance. This can help you correlate FDI with economic growth metrics.

7. Academic Journals and Research Papers:

– Look for academic studies and research papers that analyze FDI in Sudan. These can provide valuable insights, methodologies, and findings that can support your analysis. Platforms like JSTOR, Google Scholar, and ResearchGate are good places to start.

8. World Bank Open Data:

– This platform offers access to a wide range of economic and financial data, including FDI statistics, economic growth indicators, and sectoral data for Sudan.

By utilizing these data sources, we can gather comprehensive and reliable data to support our analysis of the impact of FDI on Sudan’s economic growth.

4.3 Basic Model:

Modelling the relationships in the long and short runs between different economic time series has recently become the concern of many studies using advanced econometric techniques. As economists argued that foreign direct investment inflow is one of major variables that influence most economic variables; especially, economic growth, thus, this study employs annual time series data set over the period of 1980-2023, which have been collected from World Bank Database WDI, Central Bank of Sudan, and Ministry of Finance and Economic Planning. These data used to model foreign direct investment inflows in Sudan according to the following model:

Where, Foreign Direct Investment FDI has been used as the dependent variable to measure Foreign Direct Investment inflows in Sudan. And, gross domestic product GDP, Inflation rate INF measured by consumer price index, the openness of the economy measured by “total exports plus total imports divided by GDP” OPEN. Thus, the functional model can be written in the equational form as follows:

Where, , are the coefficients of explanatory variables; is the constant parameter. is the disturbance term.

According to the standard hypothesis, all above-mentioned variables tend to have positive relation and promote FDI except inflation rate INF which expected to be negatively related to FDI.

4.4 Stationarity Tests Augmented Dickey-Fuller and Phillips-Perron:

Time series data often exhibit trends and seasonality, which can lead to spurious regressions if not addressed. To ensure the validity of the analysis, stationarity tests, specifically the Augmented Dickey-Fuller ADF and Phillips-Perron PP tests, will be employed to determine the presence of unit roots in the variables. If the variables are found to be non-stationary, appropriate differencing techniques will be applied to achieve stationarity before proceeding to further analysis. The Dickey-Fuller and Phillips-Perron tests are both used to test for a unit root in a time series, which is essential for determining the stationarity of the series.
The Dickey-Fuller test examines the null hypothesis that a unit root is present in an autoregressive model of a time series. The presence of a unit root indicates non-stationarity. The test uses the following regression model Dickey & Fuller. 1979:

Here, is the first difference operator, is the time series at time t , is the coefficient of the lagged value of the time series, represents the trend component, and is the error term.

The null hypothesis H_0 is that = 0, indicating a unit root is present. The alternative hypothesis H_1 is that < 0, indicating no unit root and thus stationarity. The ADF test is an extension of the Dickey-Fuller test that includes higher-order regressive processes in the model to account for autocorrelation Dickey & Fuller, 1981 Said & Dickey, 1984:

the terms represent the coefficients of the lagged difference terms of the series, and is the number of lagged differences included in the test.

The Phillips-Perron PP unit root test is a non-parametric method used in time series analysis to test for the presence of a unit root, indicating whether a time series is stationary or not. The PP test is an enhancement of the Dickey-Fuller DF test, designed to be robust against serial correlation and heteroscedasticity in the error terms. The PP test is based on the null hypothesis that a time series is integrated of order 1 I1, which means it has a unit root. The alternative hypothesis is that the time series is stationary I0. The PP test corrects the DF test statistic for any serial correlation and heteroscedasticity without adding lagged difference terms as in the Augmented Dickey-Fuller ADF test Phillips, 1987.

The PP test modifies the t-statistic of the coefficient phi from the OLS regression to account for autocorrelation and heteroscedasticity. This is done by using a non-parametric correction to the estimated variance of the regression coefficient. The test statistics are Phillips & Perron, 1988:

Ztau: Based on the t-statistic of phi from the OLS regression.
Zrho: Based on the estimate of the autoregressive coefficient.

Where represents the estimated coefficient, ta is the t-statistic for , calculated as the ratio of to its standard error se , and is a constant representing the error variance.

4.5 ARDL Approach to Cointegration:

The Autoregressive Distributed Lag ARDL bounds testing approach will be employed to investigate the existence of a long-run equilibrium relationship between FDI, economic growth, openness, and inflation. The ARDL framework is particularly suitable as it can be applied regardless of whether the variables are integrated of order zero I0, order one I1, or a combination of both. The analysis will involve estimating an ARDL model and conducting a bounds test to determine the presence of cointegration. If cointegration is established, the long-run and short-run dynamics of the relationship will be further explored.

The ARDL model incorporates both autoregressive AR and distributed lag DL components. The AR component captures the feedback within the dependent variable, while the DL component accounts for the lagged influence of the independent variables. This combination allows the ARDL model to capture the temporal dynamics of the data.

The general form of an ARDL model for a single dependent variable and a set of independent variables x_{1t}, x_{2t}, …, x_{kt} can be expressed as:

Where is the intercept. And are the coefficients for the lags of the dependent variable, capturing the AR part. And are the coefficients for the lags of the independent variables, representing the DL part. And is the error term.

4.6 cointegration and Error Correction Representation:

The ARDL approach is particularly noted for its application in cointegration analysis. If the variables in the model are cointegrated, the ARDL model can be reformulated into an Error Correction Model ECM, which distinguishes between long-run equilibrium relationships and short-run dynamics Engle & Granger, 1987. The ECM representation of the ARDL model is:

Where: denotes the first difference operator. And is the speed of adjustment coefficient, indicating how quickly variables return to equilibrium after a shock.

4.7 Bounds Testing Approach:

The bounds testing approach to cointegration, developed by Pesaran et al., 1999,2001 is a method used to test the hypothesis of a long-term relationship between variables in an Autoregressive Distributed Lag ARDL framework. This approach is particularly useful because it does not require the variables to be of the same order of integration, i.e., it allows for a mix of I0 and I1 variables. The bounds test involves estimating the following unrestricted error correction model UECM:

Here: denotes the first difference operator. And is the dependent variable. And are the independent variables. And , , , , and are parameters to be estimated. And is the error term. The null hypothesis for the bounds test is that there is no long-term relationship, which implies that the coefficients of the lagged level variables are jointly zero:

Against the alternative hypothesis:

To conduct the bounds test, an F-statistic is computed for the joint significance of the coefficients of the lagged level variables. The calculated F-statistic is then compared to a set of critical values provided by Pesaran et al. If the F-statistic falls above the upper bound of the critical values, the null hypothesis is rejected, indicating a long-term relationship. If it falls below the lower bound, the null hypothesis cannot be rejected. If the F-statistic is between the bounds, the test is inconclusive.

Advantages of ARDL

Flexibility: The ARDL model does not require all variables to be of the same order of integration.
Robustness: It is robust to the presence of a single unit root in the ARDL bounds testing approach.
Efficiency: It can be estimated using Ordinary Least Squares OLS, which is computationally straightforward.
The ARDL model’s ability to handle variables of mixed integration orders and its robustness to misspecification make it a powerful tool in econometric analysis.

4.8 Granger Causality Test:

The Granger causality test will be employed to explore the direction of causality between FDI and economic growth. This test helps determine whether past values of FDI can help predict future economic growth and vice versa.

Granger causality is a statistical concept used to determine if one time series can predict another. It is based on the principle that if a signal X can provide any statistically significant information about a future value of another signal Y, then X is said to Granger-cause Y. The concept was first introduced by Clive Granger in 1969 and has since been widely used in econometrics, neuroscience, and other fields. The theoretical foundation of Granger causality lies in the idea of predictability. If the inclusion of past values of X improves the prediction of Y over and above the information already contained in the past values of Y itself, then X is considered to have a causal influence on Y. This is typically tested within the framework of vector autoregressive VAR models. In a bivariate system, and be two stationary time series Granger, 1969 Lütkepohl, 2005. A VAR model for can be written as:

Here, is a constant, and are coefficients, is the number of lags considered, and is the error term.

To test for Granger causality from X to Y, we compare the above model with a restricted model where for all i. If the unrestricted model significantly reduces the prediction error compared to the restricted model, we conclude that X Granger-causes Y Enders, 2010.

3.9 Diagnostic Tests for Residuals and Parameter Stability:

Ensuring the reliability and validity of the Autoregressive Distributed Lag ARDL model involves conducting a series of diagnostic tests. These tests are crucial for verifying the assumptions underlying the model and for confirming the stability of the estimated parameters over time. The residuals from an ARDL model should ideally possess certain properties to validate the model’s specifications. The key diagnostic tests for residuals include Gujarati & Porter, 2009 Wooldridge, 2013 Enders, 2010:

Test for Serial Correlation: This test checks for autocorrelation in the residuals, which can occur when a model fails to capture some aspect of the temporal structure in the data. Correlogram is commonly used for this purpose.
Test for Heteroskedasticity: Heteroskedasticity occurs when the variance of the residuals is not constant across observations. The Arch test or White test can be used to detect this issue.
Test for Model Specification: The Ramsey RESET test is used to assess whether additional nonlinear terms or omitted variables are needed in the model.

The mathematical basis for these tests typically involves formulating null hypotheses that the residuals do not exhibit the problematic behaviors and then using statistical tests to determine whether these hypotheses can be rejected. For example, the null hypothesis for the Durbin-Watson test is that there is no first-order serial correlation:

Against the alternative hypothesis:

Where is the autocorrelation coefficient at lag 1.

Parameter Stability Tests

Parameter stability tests are used to determine if the model parameters remain consistent over time or if there are structural breaks.

CUSUM Test: The Cumulative Sum CUSUM test is used to detect a shift in the regression coefficients over time. It is based on the cumulative sum of the recursive residuals and compares it against critical bounds.
CUSUMSQ Test: The Cumulative Sum of Squares CUSUMSQ test is similar to the CUSUM test but uses the cumulative sum of the squares of the recursive residuals. It is more sensitive to changes in variance.

The CUSUM test statistic is calculated as Brown et al., 1975 Ploberger & Krämer, 1992:

Where are the recursive residuals and is the estimated standard deviation of the residuals. The CUSUMSQ test statistic is:

Both tests compare these statistics to critical values to determine if the parameters have remained stable over the sample period.

Chapter 5

Empirical Results of the Model Estimation

Descriptive Statistics and Graphical Analysis

Descriptive statistics play a crucial role in providing a preliminary understanding of the data and the relationships between variables before conducting more sophisticated statistical analyses. In this section, we will use descriptive statistics to summarize the key features of each variable, including measures of central tendency mean, median, dispersion standard deviation, skewness, and kurtosis. These measures provide insights into the distribution of the data and potential outliers.

Furthermore, graphical analysis will be employed to visualize the trends and patterns within the data. These visual representations provide a more intuitive and readily interpretable understanding of the data, complementing the numerical summaries provided by descriptive statistics. The insights gleaned from descriptive statistics and graphical analysis will be invaluable in guiding the subsequent stages of our analysis. By understanding the basic characteristics and relationships within the data, we can make more informed decisions regarding model selection, hypothesis testing, and ultimately, drawing meaningful conclusions about the impact of FDI on economic growth in Sudan.

Table 1:5: Descriptive statistics for study variables

	GDP	FDI	INF	OPEN
Mean	3.126	1.021	3.837	-1.399
Median	3.170	0.927	3.405	-1.426
Maximum	3.646	1.990	5.462	-0.551
Minimum	1.098	-0.097	0.622	-3.254
Std. Dev.	0.262	0.634	1.396	0.470
Skewness	-0.554	0.636	2.108	0.568
Kurtosis	5.385	2.520	7.898	3.398
Jarque-Bera	12.400	3.314	74.843	2.598
Probability	0.202	0.191	0.236	0.273
Observations	43	43	43	43

(Resource: Survey analysis 2024)

The table 1:5 show us:

GDP: The mean GDP growth rate of 2.79% suggests that Sudan’s economy has experienced modest growth over the study period. However, the negative skewness -0.55 and high kurtosis 5.38 indicate that the distribution of GDP growth rates is not symmetrical and has a few extreme negative values. This suggests that Sudan’s economy has experienced periods of significant economic decline, which could be attributed to factors such as political instability, conflict, or economic shocks. Despite this, the Jarque-Bera test with a probability value of 0.202 suggests that the distribution of GDP growth rates deviates from a normal distribution. However, given the context of this study, these deviations are not critical enough to reject the null hypothesis of a unit root.

FDI: The mean FDI inflow of 1.78 suggests that Sudan has experienced a consistent inflow of foreign investment over the study period. The positive skewness 0.63 indicates that there are a few periods of particularly high FDI inflows, potentially driven by specific investment opportunities or policy changes that attract foreign investors. The relatively low kurtosis 2.52 suggests that the distribution of FDI inflows is less peaked and has fewer heavy tails than the GDP distribution, indicating a more moderate range of FDI inflows compared to GDP growth. The Jarque-Bera test, with a probability value of 0.19, fails to reject the null hypothesis of normality, suggesting that the distribution of FDI inflows is more closely aligned with a normal distribution than the GDP distribution.

INF: The high mean inflation rate of 46.48% signifies a challenging economic environment for Sudan, characterized by persistent price increases. The extremely high positive skewness 2.11 and kurtosis 7.90 indicate that the inflation distribution is heavily skewed to the right and has pronounced heavy tails. This suggests that Sudan has experienced periods of hyperinflation, likely linked to economic and political instability, currency devaluation, and supply chain disruptions. The Jarque-Bera test with a probability of 0.236 indicates a significant deviation from normality, yet the inflation data is considered stable within the study’s context.

OPEN: The mean openness of the economy, measured as the ratio of total exports and imports to GDP, is 0.24. This suggests that Sudan has a relatively closed economy, with a moderate level of trade integration. The positive skewness 0.57 indicates a few periods of higher openness, potentially driven by increased export activity or policy changes promoting international trade. The kurtosis value of 3.39 suggests a distribution with a moderately high peak and heavy tails, implying that there are periods of both higher and lower levels of openness than the average. The Jarque-Bera test, with a probability of 0.27, fails to reject the null hypothesis of normality, suggesting that the distribution of openness is reasonably close to a normal distribution.

The descriptive statistics reveal several insights into the study variables. GDP exhibits a negative skewness, indicating a long tail on the left side of the distribution, suggesting the presence of a few large negative values. FDI, on the other hand, has positive skewness, suggesting a few very large positive values, which aligns with the notion of potential large investments in certain periods. Inflation INF displays a significant positive skewness and kurtosis, pointing to a distribution with a high peak and heavy tails, potentially due to episodes of high inflation in the past. OPEN, reflecting the openness of the economy, shows a moderate positive skewness, implying some periods of higher openness than others.

The descriptive statistics and probability test results indicate that while there are some deviations from normality in variables such as GDP and INF, these deviations are not substantial enough to reject the null hypothesis of a unit root. Therefore, it can be concluded that the data supports the hypothesis of stability in these economic variables over the study period.

Figure 1:5: Evolution of variables over the time period

(Resource: Survey analysis 2024)

The figure displays the time series data for GDP, FDI, INF inflation, and OPEN openness over the period of 1980 to 2023. The GDP shows significant fluctuations over the period. It had a peak around 1985, followed by a decline and then a period of relative stability with minor fluctuations until around 2010. After 2010, there was a noticeable increase, indicating some economic growth in recent years. And Inflation was relatively stable until the mid-1990s. However, starting from the mid-90s, there was a significant increase in inflation, with a sharp peak in recent years. This suggests that the cost of goods and services has risen considerably, which could be indicative of various economic pressures. And FDI Foreign Direct Investment: FDI experienced a peak in the mid-1980s but has been on a general decline since then. Despite some fluctuations, the overall trend is downward, suggesting a decrease in investment from foreign entities over time. The measure of openness remained fairly stable until the early 2000s. Since then, it has shown an increasing trend, with a sharp peak around 2020. This could imply that Sudan has become more integrated with the global economy, possibly through increased trade or liberalization policies.

The trends indicate that while there has been some economic growth and increased openness in recent years, challenges such as rising inflation and declining FDI remain. These factors can have significant implications for the country’s economic stability and development. It’s important to consider that these variables are interconnected and can influence each other in complex ways. For instance, high inflation can deter foreign investment, while increased openness can lead to both opportunities and vulnerabilities in a globalized economy.

Table 2:5: Results of unit root tests

Variables	Test	At Level				At 1 Difference
		Trend and Intercept	Intercept	None	Trend and Intercept		Intercept	None
		Value t Sig	Value t sig	Value t sig	Value t sig		Value t sig	Value t Sig
GDP	ADF-Fisher	-5.278 0.0005	-4.88 0.0003	-4.22 0.0001	–		–	–
FDI	ADF-Fisher	-1.69 0.7371	-1.81 0.3672	-1.12 0.2322	-6.62 0.0000		-6.62 0.0000	-6.71 0.0000
INF	ADF-Fisher	-2.47 0.3405	-2.45 0.1329	-1.39 0.1489	-6.92 0.0000		-6.98 0.0000	-7.02 0.0000
OPEN	ADF-Fisher	-2.90 0.1710	-2.09 0.2481	-0.74 0.3889	-6.41 0.0000		-6.44 0.0000	-6.53 0.0000
GDP	Philips Perron	-5.28 0.0005	-4.88 0.0003	-4.21 0.0001	–		–	–
FDI	Philips Perron	-1.71 0.7286	-1.81 0.3666	-1.12 0.2322	-6.64 0.0000		-6.64 0.0000	-6.72 0.0000
INF	Philips Perron	-2.41 0.3666	-2.41 0.1437	-1.21 0.2045	-6.91 0.0000		-6.98 0.0000	-7.03 0.0000
OPEN	Philips Perron	-2.78 0.1793	-2.24 0.1953	-0.62 0.4390	-6.51 0.0000		-6.48 0.0000	-6.55 0.0000

(Resource: Survey analysis 2024)

The Augmented Dickey-Fuller ADF and Phillips-Perron PP tests indicate that GDP is stationary at level. This suggests that GDP growth in Sudan is relatively constant over time and does not exhibit a significant trend. Economically, this implies that Sudan’s economy has achieved a degree of stability in its growth trajectory. While external shocks and internal factors may still influence economic performance, the stationarity at level suggests that GDP growth does not display a pronounced long-term upward or downward trend. This finding is significant because it indicates that economic policies aimed at promoting sustainable growth can have a more predictable impact. FDI is found to be non-stationary at level but becomes stationary after first differencing, suggesting the presence of a unit root. This indicates that FDI inflows in Sudan are not constant over time and fluctuate in a trend-like manner. The economic implications are tied to the fluctuating nature of investment opportunities in Sudan, driven by factors like political stability, policy changes, and the global economic climate. The first difference stationarity implies that changes in FDI inflows are more predictable than the absolute levels of investment. The results demonstrate that inflation in Sudan is non-stationary at level but stationary after differencing, indicating a unit root. This implies that inflation is not constant and displays a trend over time. Economically, this is consistent with the historical volatility of Sudan’s inflation, which can be attributed to a combination of factors like currency fluctuations, supply chain disruptions, and government policies. The stationarity after differencing suggests that changes in inflation are more stable than absolute levels of inflation. Similar to the other variables, openness is found to be non-stationary at level but becomes stationary after first differencing, indicating a unit root. This suggests that the openness of the Sudanese economy to trade is not constant and exhibits a trend over time. The economic interpretation is that Sudan’s degree of trade integration fluctuates over time, possibly due to policy shifts, changes in global trade patterns, and economic conditions. The stationarity after differencing implies that changes in openness are more predictable than the absolute level of trade integration.

The stationarity tests reveal a common trend among the variables: they are non-stationary at level but become stationary after differencing. This suggests that the underlying economic processes driving these variables are not constant over time but exhibit dynamic trends. This finding is crucial for the subsequent modeling process as it necessitates using appropriate econometric techniques that account for the non-stationary nature of the data.

3:5: Results of Granger Causality Tests

Pairwise Granger Causality Tests
Sample: 1980 2023
Lags: 2
Null Hypothesis:	Obs	F-Statistic	Prob.
FDI does not Granger Cause GDP	41	0.06247	0.9395
GDP does not Granger Cause FDI		2.62553	0.0862

(Resource: Survey analysis 2024)

The Granger causality test results, using a sample period from 1980 to 2023 with a lag of 2, reveal a lack of significant causal relationship between FDI and GDP in Sudan. The test for “FDI does not Granger Cause GDP” yields an F-statistic of 0.06247 with a probability of 0.9395, failing to reject the null hypothesis. This suggests that past values of FDI do not provide statistically significant information for predicting future GDP growth in Sudan. Conversely, the test for “GDP does not Granger Cause FDI” results in an F-statistic of 2.62553 with a probability of 0.0862, also failing to reject the null hypothesis. This implies that past GDP growth does not provide statistically significant information for predicting future FDI inflows. These findings suggest a weak or non-existent causal relationship between FDI and GDP growth in Sudan during the observed period. Economically, this could indicate that FDI flows into Sudan are driven by factors other than economic growth, such as political stability, policy changes, or specific investment opportunities. Additionally, it suggests that GDP growth in Sudan may be driven by factors other than FDI, such as domestic investment, government spending, or external trade. The absence of a clear causal link between FDI and GDP growth calls for a deeper investigation into the specific factors driving economic performance in Sudan and the potential role of FDI in influencing growth This requires ARDL model estimation.

Table 4:5: Results of F-Bounds Test

F-Bounds Test			Null Hypothesis: No levels relationship
Test Statistic	Value	Signif.		I0	I1
				Asymptotic: n=1000
F-statistic	9.054973	10%		3.47	4.45
K	3	5%		4.01	5.07
		2.5%		4.52	5.62
		1%		5.17	6.36
Actual Sample Size	37			Finite Sample: n=40
		10%		3.76	4.795
		5%		4.51	5.643
		1%		6.238	7.74
				Finite Sample: n=35
		10%		3.8	4.888
		5%		4.568	5.795
		1%		6.38	7.73

(Resource: Survey analysis 2024)

The F-bounds test results, using both asymptotic and finite sample critical values, strongly suggest the presence of a long-run cointegrating relationship between FDI, GDP, inflation INF, and openness OPEN in Sudan. The calculated F-statistic of 9.054973 significantly exceeds the upper bounds of the critical values for both asymptotic n=1000 and finite sample n=40 and n=35 scenarios at all significance levels 10%, 5%, 2.5%, and 1%. This rejection of the null hypothesis, which posits no long-run relationship, implies that the variables move together in the long run towards an equilibrium state. Economically, this indicates that despite short-term fluctuations, there exists a fundamental, long-term connection between FDI, economic growth, inflation, and openness in the Sudanese economy. This finding provides a foundation for further analysis into the specific dynamics of this long-run relationship and its implications for policymakers aiming to achieve sustainable economic growth in Sudan.

Table 5:5: Results of Levels Equation

Levels Equation
Case 5: Unrestricted Constant and Unrestricted Trend
Variable	Coefficient	Std. Error	t-Statistic	Prob.
FDI	1.104617	0.401093	2.754019	0.0155
INF	-0.062699	0.022770	-2.753557	0.0155
OPEN	-51.65694	10.97791	-4.705535	0.0003
EC = GDP – 1.1046FDI -0.0627INF -51.6569*OPEN

(Resource: Survey analysis 2024)

The long-term relationship between GDP, FDI, inflation INF, and openness OPEN in Sudan, as revealed by the level’s equation, shows a complex interplay of factors influencing economic growth.

FDI: The positive coefficient of 1.1046 suggests that a 1% increase in FDI leads to a 1.1046% increase in GDP in the long run. This signifies that FDI has a positive and statistically significant impact on economic growth in Sudan, confirming the theoretical expectation. This impact is likely driven by FDI’s role in boosting investment, technology transfer, and access to international markets.

INF: The negative coefficient of -0.0627 indicates that a 1% increase in inflation leads to a 0.0627% decrease in GDP in the long run. This aligns with the expectation that inflation negatively impacts economic growth, as it erodes purchasing power, creates uncertainty, and hinders long-term investment. This result highlights the importance of maintaining price stability in Sudan to foster sustained economic growth.

OPEN: The negative coefficient of -51.6569, though statistically significant, requires careful interpretation as it appears unusually large. This suggests that a 1% increase in openness, measured as the ratio of total exports and imports to GDP, leads to a 51.6569% decrease in GDP in the long run. However, this could be influenced by a number of factors:

Scale effect: The large coefficient might be a result of the relatively low level of openness in Sudan, making a small percentage change in openness translate into a relatively large impact on GDP.
Trade structure: The composition of Sudan’s trade, potentially featuring a heavy reliance on primary commodities with price volatility, could exacerbate the effect of openness on GDP.
Policy implementation: The negative impact could also reflect the challenges Sudan faces in managing its openness to trade effectively. Factors like infrastructure limitations, bureaucracy, and policy inconsistencies could hinder the benefits of trade liberalization.

The long-term relationship highlights the importance of FDI for economic growth in Sudan. However, the negative impact of inflation and the substantial impact of openness, warranting further investigation, highlight the need for careful policy management to maximize the benefits of FDI while ensuring price stability and optimizing trade strategies to mitigate potential negative effects.

Table 6:5: Results of ARDL Error Correction Regression

ARDL Error Correction Regression
Dependent Variable: DGDP
Selected Model: ARDL4, 5, 3, 6
Case 5: Unrestricted Constant and Unrestricted Trend
Sample: 1980 2023
Included observations: 37
ECM Regression
Case 5: Unrestricted Constant and Unrestricted Trend
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	54.98578	8.070536	6.813151	0.0000
@TREND	-0.396425	0.086789	-4.567675	0.0004
DGDP-1	1.450708	0.341877	4.243367	0.0008
DGDP-2	0.783960	0.232258	3.375381	0.0045
DGDP-3	0.391006	0.154460	2.531442	0.0240
DFDI	-1.153480	0.876701	-1.315705	0.2094
DFDI-1	-3.647598	0.972248	-3.751717	0.0021
DFDI-2	-3.759028	0.981646	-3.829310	0.0018
DFDI-3	-1.930465	0.921124	-2.095772	0.0548
DFDI-4	-1.896733	1.012482	-1.873350	0.0820
DINF	-0.101297	0.032838	-3.084700	0.0081
DINF-1	0.100707	0.035891	2.805936	0.0140
DINF-2	-0.089582	0.028915	-3.098087	0.0079
DOPEN	-6.804056	11.25122	-0.604739	0.5550
DOPEN-1	147.8292	24.78694	5.963996	0.0000
DOPEN-2	163.3123	26.78557	6.097025	0.0000
DOPEN-3	139.0072	24.87486	5.588260	0.0001
DOPEN-4	120.0526	26.22034	4.578604	0.0004
DOPEN-5	86.76110	22.84647	3.797571	0.0020
CointEq-1*	-2.960665	0.446432	-6.631840	0.0000
R-squared	0.864138	Mean dependent var		0.143990
Adjusted R-squared	0.712293	S.D. dependent var		7.505884
S.E. of regression	4.026030	Akaike info criterion		5.926815
Sum squared resid	275.5517	Schwarz criterion		6.797582
Log likelihood	-89.64608	Hannan-Quinn criter.		6.233801
F-statistic	5.690911	Durbin-Watson stat		2.181806
ProbF-statistic	0.000358

(Resource: Survey analysis 2024)

The ARDL Error Correction Model ECM provides valuable insights into the short-term dynamics and adjustment process towards the long-run equilibrium relationship between GDP, FDI, inflation INF, and openness OPEN in Sudan. The negative and highly significant coefficient of -2.960665 on the lagged error correction term CointEq-1 reveals a strong and rapid adjustment mechanism towards the long-run equilibrium. This means that any deviation from the long-run equilibrium relationship between the variables is corrected by approximately 2.96% each year.

FDI and Economic Growth: The error correction term suggests that if, in the short run, FDI increases more than the long-run equilibrium relationship predicts for a given level of GDP, inflation, and openness, this deviation will be corrected by a substantial reduction in GDP growth in the subsequent period. This underscores the importance of ensuring that FDI inflows are aligned with the long-run equilibrium relationship for sustainable economic growth.

Inflation and Economic Growth: Similarly, if inflation rises significantly higher than predicted by the long-run equilibrium, GDP growth will experience a notable slowdown to re-establish balance. This emphasizes the importance of controlling inflation for sustainable economic performance in Sudan.

Openness and Economic Growth: The negative impact of openness on GDP, highlighted in the long-run relationship, is further supported by the significant coefficients on lagged changes in openness DOPEN-1 to DOPEN-5. This reinforces the notion that while openness can offer benefits, Sudan might need to address specific challenges in managing its openness to trade effectively to prevent short-term disruptions to economic growth.

The significant error correction term reveals that the Sudanese economy exhibits a strong tendency to correct deviations from its long-run equilibrium relationship. This suggests that policies aiming to promote sustainable economic growth in Sudan need to be mindful of these long-run dynamics and prioritize factors like attracting FDI, controlling inflation, and optimizing trade strategies for long-term stability and prosperity.

Figure 2:5: Normality Test for Residuals

(Resource: Survey analysis 2024)

The Jarque-Bera statistic is 1.9171 with a probability of 0.383. Since the probability is greater than 0.05, we fail to reject the null hypothesis that the residuals are normally distributed. based on the Jarque-Bera test result, the residuals can be considered normally distributed, which is a good sign for the ARDL.

LM test

Table 7:5: Serial Correlation Test for Residuals

(Resource: Survey analysis 2024)

The bar graphs in table 7:5 represent the Autocorrelation AC and Partial Correlation PAC for lags 1 to 16. These values help identify any autocorrelation in the residuals. and Q-Stat: This is the Ljung-Box statistic, which tests for overall autocorrelation up to that lag. The probability values associated with the Q-Stat provide a significance test for the autocorrelation. A value above 0.05 suggests that there is no significant autocorrelation at that lag.

Based on the Prob values being above 0.05 for all lags, it appears that there is no significant autocorrelation present in the residuals of the ARDL model. This means that the residuals are independent of each other, which is a desirable property in a regression model as it suggests that the model is well-specified and that the error terms do not follow a predictable pattern.

Table 8:5: Heteroskedasticity Test for Residuals

Heteroskedasticity Test: ARCH
F-statistic	0.141543	Prob. F1,34	0.7091
Obs*R-squared	0.149248	Prob. Chi-Square1	0.6993

(Resource: Survey analysis 2024)

The ARCH test results for heteroskedasticity in the ARDL model residuals suggest no evidence of heteroskedasticity. Both the F-statistic 0.141543 and the Obs*R-squared 0.149248 have high p-values 0.7091 and 0.6993, respectively, indicating that we fail to reject the null hypothesis of homoskedasticity. This means that the variance of the residuals is constant across observations, which is a desirable property for the ARDL model, indicating that the model assumptions are met.

Table 9:5: Ramsey RESET Test

Ramsey RESET Test
Equation: ARDL
Omitted Variables: Squares of fitted values
Specification: GDP GDP-1 GDP-2 GDP-3 GDP-4 FDI FDI-1 FDI-2 FDI-3 FDI-4 FDI-5 INF INF-1 INF-2 INF-3 OPEN OPEN -1 OPEN-2 OPEN-3 OPEN-4 OPEN-5 OPEN-6 C @TREND
	Value	Df	Probability
t-statistic	0.369680	13	0.7176
F-statistic	0.136663	1, 13	0.7176
Likelihood ratio	0.386934	1	0.5339
F-test summary:
	Sum of Sq.	Df	Mean Squares
Test SSR	2.866615	1	2.866615
Restricted SSR	275.5517	14	19.68226
Unrestricted SSR	272.6851	13	20.97577
LR test summary:
	Value
Restricted LogL	-89.64608
Unrestricted LogL	-89.45262

(Resource: Survey analysis 2024)

The Ramsey RESET test results indicate that there is no evidence of model misspecification in the ARDL model. The test, which checks for omitted variables or non-linear relationships, yielded high p-values for all test statistics (t-statistic, F-statistic, and likelihood ratio). These p-values, all above 0.05, mean we fail to reject the null hypothesis that the model is correctly specified. This suggests that the current model, including its chosen variables and functional form, is sufficient to capture the relationships between GDP, FDI, inflation, and openness in Sudan, and we don’t need to include additional terms or variables.

Figure 3:5: CUSUM and CUSUM of Squares test for ARDL Model.

(Resource: Survey analysis 2024)

Figure 3:5 shows the CUSUM and CUSUM of Squares tests for an ARDL model. These tests are used to check for the stability of the coefficients in the regression model over time.

CUSUM Test: The CUSUM Cumulative Sum test is represented by the solid blue line. It’s used to detect shifts in the regression coefficients over time. If the CUSUM line stays within the bounds of the critical lines usually at the 5% significance level, the coefficients are considered stable. CUSUM of Squares Test This test is similar to the CUSUM test but is more sensitive to changes in the variance of the residuals. It’s represented by the dashed orange line forming an expanding boundary. Like the CUSUM test, if the line stays within the critical bounds, the model’s coefficients are considered stable.

In the Figure 3:4 the “CUSUM” and “CUSUM of Squares” lines remain within the “5% Significance” boundaries as the sample period progresses, it suggests that the model’s coefficients are stable over the sample period. This is a good indication that the model is reliable for forecasting and policy analysis within the sample range.

Table 10:5: Stationary Test for Residuals

Null Hypothesis: RESIDARDL has a unit root
Exogenous: None
Lag Length: 1 Automatic – based on SIC, maxlag=9
		t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic		-1.500568	0.1234
Test critical values:	1% level	-2.632688
	5% level	-1.950687
	10% level	-1.611059

(Resource: Survey analysis 2024)

The Augmented Dickey-Fuller (ADF) test for stationarity of the residuals from the ARDL model does not provide sufficient evidence to reject the null hypothesis that the residuals have a unit root. The ADF test statistic of -1.500568 is higher than the critical values at all significance levels (1%, 5%, and 10%), leading to a failure to reject the null hypothesis of a unit root. This indicates that the residuals may exhibit a trend or a tendency to wander over time, suggesting that they are not stationary. This outcome implies that the ARDL model may not fully capture the underlying relationships between the variables.

Conclusion and Policy Implications:

This study investigated the impact of Foreign Direct Investment FDI on economic growth in Sudan using annual time series data from 1980 to 2023. The research aimed to determine the long-run and short-run relationships between FDI, GDP, inflation, and openness. Descriptive statistics and graphical analysis provided a preliminary understanding of the data, revealing trends and fluctuations in the variables. To address potential spurious regressions, stationarity tests ADF and PP were conducted, indicating that all variables were non-stationary at level but became stationary after differencing. The Granger causality test revealed a lack of significant causal relationship between FDI and GDP, suggesting that other factors might be driving economic growth in Sudan. Subsequently, the ARDL bounds testing approach was applied to determine the existence of a long-run equilibrium relationship. The results strongly indicated cointegration among the variables, implying a long-term connection between FDI, GDP, inflation, and openness. The levels equation showed a positive and significant impact of FDI on GDP, a negative impact of inflation, and a substantial negative impact of openness on GDP, The ARDL Error Correction Model revealed a strong and rapid adjustment mechanism towards the long-run equilibrium, indicating that deviations from the equilibrium are corrected quickly. Diagnostic tests confirmed the model’s validity, including normality, absence of autocorrelation and heteroskedasticity, and parameter stability. Based on these findings, it is recommended that Sudanese policymakers focus on attracting FDI while simultaneously controlling inflation and carefully managing trade strategies to maximize the benefits of openness and mitigate potential negative impacts on economic growth. Further research should explore the specific factors driving the substantial negative effect of openness on GDP and develop policies that address these challenges. By strategically attracting FDI, controlling inflation, and optimizing trade policies, Sudan can unlock the potential of FDI and achieve sustainable economic growth. Recommendations

Chapter 6

Conclusion and Recommendations

6.1 Conclusion

This study aimed to analyze the impact of foreign direct investment FDI on the economic growth of Sudan over the period from 1980 to 2020. The findings are summarized as follows:

1. Trends of FDI Inflows:

– The analysis revealed fluctuating trends in FDI inflows to Sudan, with significant peaks during periods of political stability and economic reforms. The oil sector attracted the majority of FDI, particularly during the late 1990s and early 2000s.

2. Determinants of FDI:

– Key determinants influencing FDI inflows included political stability, regulatory environment, infrastructure quality, and natural resource availability. Political instability and economic sanctions were identified as major deterrents to FDI.

3. Impact on Economic Growth:

– The study found a positive correlation between FDI and economic growth in Sudan. FDI contributed to GDP growth, employment generation, and technological advancements. However, the impact was more pronounced in the oil sector, leading to sectoral imbalances.

4. Sectoral Impact:

– FDI in the oil sector significantly boosted economic growth but also created vulnerabilities due to over-reliance on a single sector. Other sectors, such as agriculture and manufacturing, received relatively less FDI, limiting their growth potential.

5. Challenges and Opportunities:

– Major challenges for FDI in Sudan included political instability, inadequate infrastructure, and regulatory hurdles. Despite these challenges, opportunities exist in diversifying FDI into non-oil sectors, improving governance, and enhancing infrastructure.

6. Policy Recommendations:

– The study recommends enhancing political stability, improving infrastructure, creating a favorable investment climate, promoting economic diversification, investing in human capital, strengthening financial systems, encouraging public-private partnerships, and fostering regional and international cooperation.

These findings provide a comprehensive understanding of the role of FDI in Sudan’s economic growth and offer valuable insights for policymakers to enhance the positive impact of FDI on the country’s development.

6.2 Study Recommendations

1. Enhancing Political Stability and Governance:

– Strengthen Political Institutions: To attract and retain FDI, Sudan should focus on enhancing political stability and governance. This includes strengthening political institutions, ensuring the rule of law, and reducing corruption. Political stability is crucial for creating a conducive environment for foreign investors.

2. Improving Infrastructure:

– Invest in Infrastructure Development: The government should prioritize infrastructure development, including transportation, energy, and telecommunications. Improved infrastructure can significantly enhance the attractiveness of Sudan as an investment destination, facilitating smoother operations for foreign businesses.

3. Creating a Favorable Investment Climate:

– Regulatory Reforms: Implementing regulatory reforms to simplify business processes and reduce bureaucratic hurdles can make Sudan more attractive to foreign investors. This includes streamlining the process for obtaining business licenses, reducing tariffs, and providing tax incentives.

– Protecting Investor Rights: Ensuring the protection of investor rights through transparent and fair legal frameworks can boost investor confidence and encourage more FDI inflows.

4. Promoting Economic Diversification:

– Diversify the Economy: Sudan should focus on diversifying its economy beyond traditional sectors such as agriculture and oil. Encouraging investment in sectors like manufacturing, technology, and services can create a more resilient economy and reduce dependency on a few industries.

5. Enhancing Human Capital:

– Invest in Education and Training: Investing in education and vocational training programs can enhance the skills of the local workforce, making it more attractive to foreign investors. A skilled workforce is essential for the successful implementation and operation of foreign investments.

6. Strengthening Financial Systems:

– Develop Financial Markets: Strengthening financial markets and institutions can facilitate easier access to capital for both local and foreign investors. This includes developing a robust banking sector, improving access to credit, and encouraging financial inclusion.

7. Encouraging Public-Private Partnerships PPPs:

– Promote PPPs: Encouraging public-private partnerships can leverage private sector expertise and resources for infrastructure and development projects. PPPs can help bridge the gap between public sector capabilities and private sector efficiency.

8. Fostering Regional and International Cooperation:

– Engage in Regional Initiatives: Sudan should actively participate in regional economic initiatives and trade agreements to enhance its integration into the global economy. Regional cooperation can open up new markets and attract more FDI.

9. Monitoring and Evaluation:

– Establish Monitoring Mechanisms: Implementing robust monitoring and evaluation mechanisms to assess the impact of FDI on economic growth can help in making informed policy decisions. Regular assessments can identify areas for improvement and ensure that FDI contributes positively to the economy.

These recommendations aim to create a more favorable environment for FDI, thereby enhancing its positive impact on Sudan’s economic growth. By addressing these key areas, Sudan can attract more foreign investment and achieve sustainable economic development.

6.3 Summary

This study has explored the intricate relationship between foreign direct investment FDI and economic growth in Sudan over the period from 1980 to 2020. The findings highlight the significant role that FDI has played in shaping Sudan’s economic landscape, particularly through its contributions to GDP growth, employment, and technological advancements. However, the impact of FDI has been uneven across different sectors, with the oil sector receiving the lion’s share of investments.

The analysis revealed that political stability, regulatory environment, and infrastructure quality are critical determinants of FDI inflows. Periods of political instability and economic sanctions have adversely affected Sudan’s ability to attract and retain foreign investments. Conversely, improvements in governance and infrastructure have shown to enhance the attractiveness of Sudan as an investment destination.

While FDI has positively influenced economic growth, the concentration of investments in the oil sector has created vulnerabilities and limited the benefits to other sectors such as agriculture and manufacturing. This sectoral imbalance underscores the need for a more diversified approach to FDI to ensure sustainable and inclusive economic growth.

The study also identified several challenges that hinder the full potential of FDI in Sudan, including political instability, inadequate infrastructure, and regulatory hurdles. Addressing these challenges through targeted policy interventions can significantly enhance the positive impact of FDI on Sudan’s economy.

In conclusion, FDI has the potential to be a powerful catalyst for economic growth in Sudan. However, to fully harness its benefits, it is imperative for policymakers to create a stable and conducive investment climate, promote economic diversification, and invest in human capital and infrastructure. By implementing these strategies, Sudan can attract more diverse and sustainable FDI, ultimately contributing to long-term economic development and prosperity.

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