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The Study on the Impact of Liberia’s Exports and Imports on Its Economic Growth

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Open Journal of Business and Management, 2020, 8, 2649-2670 ISSN Online: 2329-3292 ISSN Print: 2329-3284 The Study on the Impact of Liberia’s Exports and Imports on Its Economic Growth Faliku S. Dukuly*, Kun Huang* School of Economics and Management, Shanghai Maritime University, Shanghai, China How to cite this paper: Dukuly, F. S., & Huang, K. (2020). The Study on the Impact of Liberia’s Exports and Imports on Its Economic Growth. Open Journal of Business and Management, 8, 2649-2670. Received: September 5, 2020 Accepted: November 20, 2020 Published: November 23, 2020 Copyright © 2020 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). Open Access Abstract Liberia is labeled at the peak considered as one of the poorest countries in the world. Therefore, Liberia needs to take an effective trade policy approach to promote both domestic and international trade facilitation if it is to achieve sustainable and further economic growth. International trade is the engine for economic development, and it has become one of many economic discussions not only among West African States and member countries but globally that Liberia is no exception to since exports-trade leads to GDP growth and economic development. As a result of frequent trade deficits and Liberia’s economic reliance on extractive commodities for trade in agricultural goods, the study sought to analyze the role of exports-trade on economic growth and development with regard to Liberia. The study was conducted using secondary data generated from the World Bank Development Indicators (WBDI) for the period 2000-2019. The study employed a time series regression model of the Ordinary Least Squares (OLS) and technique by Stock and Wilson (1988) to analyze Liberia’s trade performance using macroeconomic indicators/variables that have an effect on economic growth, such as, Exports, Foreign Direct Investment (FDI), Population growth, Imports, Gross Fixed Capital Formation, (GFCF) and Gross Domestic Product (GDP) as the key indicators of analysis. The regression results obtained from the study on the Ordinary Least Squares tests show a linear association and a straight-line relationship among the variables, namely: export, foreign direct investment, population and economic growth in Liberia. With the estimated results, import has a negative impact and relationship with Liberia’s GDP growth. The effect of export was positive and highly statistically significant. Keywords Liberia, Economic Growth, Exports, Export-Led Growth, Imports, International Trade *Faliku S. Dukuly: M. Econs; Kun Huang: Ph.D. DOI: 10.4236/ojbm.2020.86164 Nov. 23, 2020 2649 Open Journal of Business and Management F. S. Dukuly, K. Huang DOI: 10.4236/ojbm.2020.86164 1. Introduction In West Africa, along the Coast is Liberia. Founded in 1847, Liberia is the oldest African Republic with a current population of 5,066,949 million people according to the World Population Review (2020b). Between 2003 and 2013, Liberia’s per capita gross domestic product (GDP) grew steadily. The consolidation of peace and political stability combined with robust external assistance, rising foreign direct investment (FDI), and private sector-led growth in the context of a sound macroeconomic framework was fundamental to Liberia’s economic recovery due to the 14 years civil conflict. GDP expanded at an annual average rate of 6.2 percent between 2003 and 2013, but due to Liberia’s high fertility rate, per capita GDP grew at a more modest of 3 percent per year and indicated by the World Bank Group (2018). Liberia’s economy grew by an estimated 2.5 percent in 2017, as increased mining sector output compensated for the weak performance of other sectors. Meanwhile, this resulted to a decline in domestic revenues generation and rising mandatory expenditures for the government. Due to poor governance and weak institutions, most investment focuses on the extractive industries, and firms in other sectors face major obstacles to doing business. Liberia shares borders with its neighboring Côte d’Ivoire, Guinea, and Sierra Leone while Côte d’Ivoire, and Guinea have successfully improved their business environments within the region that has also enabled Liberian traders to participate in cross-border trade. According to World Bank country report for 2018, Liberia faces complex development challenges, including a highly concentrated export structure, a narrow revenue base, a heavy reliance on foreign aid, a structural fiscal deficit, and an increasing dependence on commodities (food) imports. Transitioning to an economic model in which GDP growth reliably generates broad-based improvements in filling the poverty gap and social development indicators will require building human capital through education, boosting productivity, accelerating jobs creation by investment in industrial base agriculture and increase agribusiness and argo-processing opportunities among rural Liberians traders and then gradually open doors to real time cross-border trade. Many of these challenges are also reflected in Liberia’s national strategic vision, “Liberia Rising 2030” and addressing them will require well-designed, tightly coordinated, and properly sequenced policy interventions that will strengthen socioeconomic resilience, enhancing the quality of governance, and expanding institutional capacity. International trade plays a fundamental role in diversifying any modern economy to emerge especially for Least Developed Countries. With export trade, it can be viewed as an engine of economic growth; an increase in the demand for domestic exportable products will certainly lead to increase in the overall nation output. Export growth countries can take the benefit from economies of scale. Further, increased exports can provide foreign exchange that can be used for 2650 Open Journal of Business and Management DOI: 10.4236/ojbm.2020.86164 F. S. Dukuly, K. Huang import of new technology from developed economies Rani (2018). Researchers and policymakers still believe that the open economies grow fast- er than that of the closed economy Rani (2018). According to the International Monetary Fund IMF, it was observed in 2011 that real global economic output sharply declined from 4.1percent in 2011 to 2.4 percent in April to 2013. This shrinkage in nationwide output has worsened living standards especially among the poorest people in the world as poverty levels have also deteriorated IMF (2013). Historically, Firestone Tire and Rubber Company has been one of the engines behind Liberia’s economy success, maintaining one of the largest rubber plantations in the world. This resulted to bilateral trade floor between Liberia and the Western World with Liberia having comparative advantage over agricultural produce in the exchange for finished goods that has broadened Liberian foreign earnings through export trade in raw agriculture commodities such as natural rubber, iron ore, cocoa, diamond, gold, and etc. According to Firestone Tire and Rubber Company-Liberia, a total of USD $1.3 billion was directly invested between 2004 and 2017, mainly through a combination of wages, salaries, healthcare, education, taxes to the Government, rubber purchased from Liberian (small) rubber farmers, and payments for goods and services (Firestone, 2019). These investments and among others are in addition to purchases of equipment and manufactured goods used in daily operations. Firestone Liberia continues to work towards returning the farm to full operational condition in the decades following 14 years of civil wars in Liberia (Firestone, 2019). Liberia as a West African Nation is rich with mineral resources but yet remain poor in its human capital, and as well as lack infrastructural development in all sectors making it one of the poorest countries in the world according to World Population Review (2020a) and with a GNI per capita of $710. Most of the Least Developed countries in Sub-Sahara Africa in recent years have focused on foreign aid as a backbone for economic growth and development with less attention given to exports oriented industries that could increase national output and create employment for citizens. This study tends to fill the gap in the economic growth-determinants literature by examining the link between economic growth, imports, and export. Also, promoting inclusive growth has been one of the main recent challenges in developing economies. Maximizing the benefit from foreign trade and closing the gap on import concentrated marked can help to promote pro-poor growth for least developed economies. In this vein, this study can be distinguished from previous studies by investigating the impact that imports and exports have on economic growth and development in Liberia. 2. Review of Current Literature Empirical researches on the role of exports in achieving sustainable economic 2651 Open Journal of Business and Management F. S. Dukuly, K. Huang DOI: 10.4236/ojbm.2020.86164 growth and developments have been carryon with findings showing a positive relationship between exports and economic growth and development. Exports and economic growth are found to have one-way causality relationship in Indonesia and Singapore and two-way causality relationship in Indonesia and the Philippines according to (Ismail, 2003). Real income and real export growth are found to be co-integrated in Bangladesh (Love, 2005). Also, there is a unidirectional causality relationship between export growth and GDP growth in Denmark, Ireland, Italy, New Zealand, Belgium, Spain, Iceland, and Sweden; while on the other hand there is a bidirectional causality relationship export growth and GDP growth in Austria, Japan, France, Greece, Norway, Mexico, and Poland (Konya, 2006). There is a bidirectional causality relationship between export growth and real GDP growth in both short run and long run in Turkey for the period 1980-2007 (Taban, 2008). Many countries of the world choose to trade with one another simply because trade has become advantageous to the growth of modern economy. Not only that but also, international trade has become even more beneficial to every nation whether rich or poor; it now serves as an engine for the global economy. Over the years, several theories have been developed to explain the justification for international trade and its link with economic growth and development. These include absolute advantage theory (Smith, 1776); comparative advantage theory (Ricardo, 1817); product life cycle and national competitive advantage theory (Porter, 1990). All these theories advocate free and unfettered trade among nations. Countries should abandon autarky (self-sufficiency) and embrace free trade. Srinivasan (1999) submits that trade liberalization (moving the economy to free trade from autarky) has positive growth effects on nation’s economy (Cole, 2012), there is an avalanche of evidence (both theoretical and empirical) in the extant literature to show that exports are correlated with economic growth and development of countries developing and developed alike (Jung, 1985), (Levin, 1997), (Al-Yousif, 1999), (Giles, 2000), (Ullah, 2009), (Tabari, 2010) and (Rahmaddi, 2011). This export-growth nexus has been described by scholars as export-led growth (ELG) strategy/hypothesis (Love, 2005), (Ahmed, 2008) and (Sentsho, 2012). ELG as Export Led-Growth is an economic development strategy in which export and foreign trade in general play a central role in a country’s economic growth and development (Sentsho, 2012). In spite of the positive association between exports and economic growth in the developing countries, there are also evidences in the literature which negate this association. For instance, (Jaffee, 1985) questions whether a dependence on exports to lead the economy will result in constant long-term economic growth in lesser developed countries (LDCs), due to the volatility and unpredictability in the world market. (Hsiao, 1987) employing Granger tests causality, shows that there is no causal relation between exports and GDP for four Asian newly industrializing econo- 2652 Open Journal of Business and Management DOI: 10.4236/ojbm.2020.86164 F. S. Dukuly, K. Huang mies, except Hong Kong, where unidirectional causality run from GDP to exports. Also, (Bahmani-Oskooee, 1991) who similarly used Ganger concept of causality and did not find a strong evidence to support the ELG hypothesis in less developed countries (LDCs). Liberia as a country emerging from a prolong war, and also labeled as one of the poorest countries in the world, international trade has impacted the nation’s growth in one way or the other. The country’s merchandise exports increased from negligible levels before the Second World War to US $400 m. Looking back in 1974, stimulated by strong demand in the world economy for iron ore and supported by continuing exports of natural rubber and other primary agricultural commodities. In 1974 exports were equivalent in value to 87 percent of GDP. Exports then stagnated in volume terms, in the late 1970s as a result of the global recession. In the 1980s the volume of exports declined and created some setbacks that affected the Liberian’s economy. Nevertheless, the dominance of the export sector continued: exports were equivalent to 58 percent of GDP in 1980 and 50 percent in 1989. In July of 2009, A case study was conducted whereas the case analysis uses Malawi in a paper published by the United States Department of Agriculture titling “Trade and Development -When Exports Lack Diversification” reported that a country that earns most of its foreign exchange from export concentration can be heavy exposure to volatility. In most cases, the higher the volatility, the riskier the security. The econometric results from that suggest that the decline in Malawi’s gross domestic product (GDP) at such a time when tobacco exportation is falling can be almost three times greater than the increase in GDP when exports are rising. For example, variability in cocoa beans exports could lead to slower economic growth for a country that is heavily relying on cocoa beans or cash-crop exportation because GDP falls by a relatively large amount in response to a given decrease in exports, while recovering little during an upswing in exports. However, an increase in cocoa beans for such a country could achieve greater GDP growth from variability in export earnings. Since trade promotes economic development, countries must link each other in forming economic cooperation that can bring about sustainable economic growth and development. The adoption of international trade can serve as a useful stage to help every country accomplish its goal be it least developed or underdeveloped, wealthy or poor. International trade engagement can be very rewarding and advantageous to nations and their economic growth. I certainly also support (Love, 2005) base on the fact that export-led growth is an economic development strategy in which export and foreign trade in general play a central role in a country’s economic growth and development. For example, in West Africa, ECOWAS member country such as Nigeria is likely to have an abundance of some unique and different types of resources as 2653 Open Journal of Business and Management F. S. Dukuly, K. Huang DOI: 10.4236/ojbm.2020.86164 compared to others with fewer and wider population gap. In economic reasoning, such a distribution ratio could be advantageous in trade facilitation and economic growth due to multiple factors. With the support of existent literature, the evidence is clear in both (theoretical and empirical research) that exports trade in relative commodities is correlated with economic growth and in the development of countries (Al-Yousif, 1999). However, there are still some critical issues that are yet to be addressed when considering Liberia. In recent times, not too much research has been carryon or done to see the relationship or direct effects of Exports and GDP growth in Liberia as compared to other places. Notwithstanding, Andrews (2015) investigated the relationship between Exports, Imports, and Economic Growth in Liberia: Evidence from that study shows some favorable results using Causality and Cointegration Analysis. In his study, the results confirm the bi-directional causation between GDP and Imports and unidirectional causation between exports and GDP and exports and imports. Moreover, his analysis does not suggest that Liberia is driven by exports alone but rather a mixture of exports and imports commodities across sectors. Uddin (2009) investigate the causally between export and GDP growth of Bangladesh taking import, remittance as a control variable; using annual data from 1976 to 2005. Their findings show limited support in favor of export-led growth hypothesis and for imports, remittance, and GDP. Also, Akhter (2017) investigates the relationship between import, export, and GDP growth. This study concluded that the impact of exports on economic growth is positive. An opposite scenario is also found in the case of import. All these researches investigated the relationship between export, import, and GDP growth by taking of different control variables like import and remittance. But no evidence found in the literature that considered gross fixed capital formation that happens to be a key component or element of GDP prediction. It is in this vein that based on current literature review that this research is going to provide further evidence about the relationship between export and GDP growth and alongside with other variables like Gross Fixed Capital Formation, (GFCF) showing at what level does gross fixed capital formation contributes to the economic growth of Liberia and at the same time focusing on the impact of Liberia’s exports on its economic growth. 3. Data Sources and Model Specification The required data is collected from the World Bank Indicators (WBI) online database. Liberia has limited access to online data sources therefore it is difficult for researchers to find country data apart from those published in yearly reports and news articles. Since the World Bank and the International Monetary Fund, (IMF) has access to credible data sources, the researcher extracted the data from the World Bank online database Indicators. The research uses data from 2000 to 2654 Open Journal of Business and Management DOI: 10.4236/ojbm.2020.86164 F. S. Dukuly, K. Huang 2019 to employed a time series regression model of the Ordinary Least Squares (OLS) and technique by Stock and Wilson (1988) to analyze Liberia’s trade performance using macroeconomic indicators/variables that has effect on economic growth such as, Exports, Foreign Direct Investment (FDI), Population, Imports, Gross Fixed Capital Formation, (GFCF) and gross domestic product (GDP) as key indicator of analysis. The study used annual time series data on the variables for the period 2000 to 2019 as seen in Table 1 on the next page. The data needs were identified based on the objectives of the study. Hence, the year 2000 and also 2003 can be regarded as a significant year since it was the period when the country was emerging from the heat of the 14 years conflict, (war). Model Presentation We formulate the equation as: Yt= mX + b + εt where: Yt: Dependent variable Xt: Independent variable b: Constant term t: Time trend εt: is the random error term, which is the difference between the actual value of a dependent variable and its predicted value Econometrically, the model for this research analysis will be stated as; Y =β0 + β1 X t + β2 X t + β3 X t + β4 X t + β5 X t + εt where Yt is known as the Dependent variable, β0 is known as the Intercept while β1, β2, … and β5 are known as the Coefficients. Thus; Y = RGDPt Table 1. Variables and interpretation. Variable Description Data Source Dependent Variable RGDP per capital growth rate Annual percentage growth rate of GDP per capita (%) World Bank Independent Variables Exports of goods and services Annual percentage growth rate of exports of goods and services World Bank Imports of goods and services Imports of goods and services (% of GDP) World Bank Population growth rate Annual population growth rate (%) World Bank Gross fixed capital formation Gross fixed capital formation (% of GDP) World Bank Foreign direct investment Foreign direct investment, net inflows (% of GDP) World Bank Source: Author’s Computation (2020). 2655 Open Journal of Business and Management F. S. Dukuly, K. Huang DOI: 10.4236/ojbm.2020.86164 β0 = Intercept β1 = EXPt β2 = IMPt β3 = GFCFt β4 = FDI β5 = POPt εt = Error term The above equation can be expressed in a linear function given an empirical version of the research model as: RGDPt = β0 + β1 + EXPt + β2 + IMPt + β3 + GFCFt + β4 + FDIt + β5 + POPt + εt 4. Methodology Damodar N. Gujarati (2009) made it known that a model is simply a set of mathematical equations. If the model has only one equation, that means it is a single equation model but if the model has more than one equation, then it is classified as multiple-equation model. Therefore, the econometric method to be used in this research analysis is the Ordinary Least Square (OLS) regression method. This helps the researcher to estimate a favorable parameter of economic relationship mentioned in this study. Based on the analysis of the regression result, the R-squared (R2) is giving as 0.973303452 and Adjusted R-squared (Adj-R2) is 0.963768971. Statistically, the data model shows a positive trend and significant correlation between gross domestic product (GDP) and the explanatory variables. The result also shows negative relationship with Imports less than the theoretically expected value and also predicates a positive relationship between foreign direct investment (FDI) and GDP growth. Not only that, but also, the model predicates a positive relationship between population growth and GDP; while in the same vein there is a negative correlation between gross fixed capital formation (GFCF) and GDP growth. 5. Data Analysis and Interpretation 5.1. Introduction In the previous chapter on page 2655, a model was specified to find out the effect of Export, Import, Gross Fixed Capital Formation, Foreign Direct Investment and Population on Gross Domestic Product in Liberia. To examine this effect, data were analyzed using the Statistical Package for Social Sciences (SPSS), Version 20. 5.2. Data Presentation Table 2 below shows the data on Gross Domestic Product (GDP) Y, Export (EXP) X1, Import (IMP) X2, Gross Fixed Capital Formation (GFCF) X3, Foreign Direct Investment X4, Population (POP) X5, from 2000 to 2019. 2656 Open Journal of Business and Management F. S. Dukuly, K. Huang Table 2. Regression coefficients. Coefficients Model Unstandardized Coefficients B Std. Error Standardized Coefficients t Beta (Constant) Gross Fixed Capital Formation Population 1 Import −2,786,085,636.505 369,614,983.555 −10,456,460.947 12,199,827.432 −0.038 1316.933 −2,567,433.478 75.402 919,646.526 0.888 −0.157 −7.538 −.857 17.465 −2.792 Export 0.672 0.260 0.161 2.581 Foreign Direct Investment 0.060 0.059 0.049 1.026 a. Dependent Variable: Gross Domestic Product. Source: Author’s Computation using SPSS Version 20. Sig. 0.000 0.406 0.000 0.014 0.022 0.322 Collinearity Statistics Tolerance VIF 0.949 0.738 0.602 0.490 0.845 1.053 1.355 1.660 2.043 1.184 5.3. Testing for Multicollinearity We test for multicollinearity i.e. if the independent variables can be written as a linear combination of each other. Two values are used to check for the presence of multicollinearity. These are Tolerance and the Variance Inflation Factor (VIF). Tolerance is an indicator of how much of the variability specified independent variable is not explained by the other independent variables in the model and it is calculated as 1 − R squared for each variable. The VIF is the inverse of the Tolerance. Also, in testing for multicollinearity, the values less than 0.1 and VIF values greater than 10 indicate the presence of multicollinearity. Considering the Collinearity Statistics in Table 2 below, all the Tolerance values are greater than 0.1 and all the VIF values are less than 10. This indicates the absence of multicollinearity see in Table 2. 5.4. Evaluating the Model 1) Checking for Variation Explained by the Independent Variables From Table 3 below, the value of R square is 0.973 which means that 97.3% of the total variation in Gross Domestic Product is accounted for by the inclusion of the five independent variables (Export, Import, Gross Fixed Capital Formation, Foreign Direct Investment and Population) in the fitted model. RGDPt = −2786085636.505 + 0.672EXPt − 2567433.478IMPt −10456460.947GFCFt + 0.060FDIt +1316.933POPt + εt 2) Checking for Model Significance To assess the statistical significance of the result, we consider the Analysis of Variance (ANOVA) table. The null hypothesis tested here is that the multiple R in the population equals 0. That is, H0:R = 0. From the ANOVA table below (Table 4), the F calculated is equal to 102.082 and has a p-value of .000 (as seen in the Sig. Column below). Since the p-value is less than 0.05 (p < 0.05), I reject the null hypothesis and conclude that the model is statistically significant. DOI: 10.4236/ojbm.2020.86164 2657 Open Journal of Business and Management F. S. Dukuly, K. Huang Table 3. Model summary. Model 1 R 0.987a R Square 0.973 Adjusted R Square 0.964 Std. Error of the Estimate 191,935,223.832 a. Predictors: (Constant), Foreign Direct Investment, Gross Fixed Capital Formation, Import, Population, Export; b. Dependent Variable: Gross Domestic Product. Source: Author’s Computation using SPSS Version 20. Table 4. Analysis of variance. ANOVAa Model Regression Sum of Squares 18,803,147,883,745,018,000.000 df Mean Square F 5 3,760,629,576,749,003,300.000 102.082 Sig. 0.000b 1 Residual 515,747,822,065,559,550.000 14 36,839,130,147,539,968.000 Total 19,318,895,705,810,575,000.000 19 a. Dependent Variable: Gross Domestic Product; b. Predictors: (Constant), Foreign Direct Investment, Gross Fixed Capital Formation, Import, Population, Export. Source: Author’s Computation using SPSS Version 20. 5.5. Evaluating Each Independent Variable 1) Contribution of the Variables Considering the column for Beta under Standardized coefficients in Table 2 where the values for each of the variables have been converted to the same scale to enable comparison, we can check and compare the individual contribution of the independent variables. From the Beta column we see that population has the largest beta coefficient (0.888), followed by Export (0.161), then Import (−0.157), then Foreign Direct Investment (0.049) and lastly, Gross Fixed Capital Formation (−0.038). The implication is that population made the strongest unique contribution to explaining the dependent variable, when the variance explained by all other variables in the model is controlled for while Gross Fixed Capital Formation made the least unique contribution to explaining the dependent, when the variance explained by all other variables in the model is for. 2) Significance of the Betas Considering the Sig. column in Table 2, we check to see if the unique contribution of the independent variables is statistically significant. The p-values for Population, Import and Export are less than 0.05, indicating that they make statistically significant unique contributions to the prediction of the GDP. 3) Regression Equation and Interpretation Considering the Unstandardized Coefficients in Table 2, we can find the regression equation. From the table, the regression equation is: RGDPt = −2786085636.505 + 0.672EXPt − 2567433.478IMPt −10456460.947GFCFt + 0.060FDIt +1316.933POPt + εt That is, DOI: 10.4236/ojbm.2020.86164 2658 Open Journal of Business and Management

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