Foreign Direct Investment, Technological Gap and Total Factor Productivity in Sub-Saharan African Economies

Foreign Direct Investment, Technological Gap and Total Factor Productivity in Sub-Saharan African Economies

¹Adeleye Ebenezer OLONILUYI*, ²Julius Oyebanji IBITOYE, ³Adegbola OJAROTADE

¹Department of Economics, Ekiti State University, Ado-Ekiti, Nigeria

²Department of Economics, Federal University Oye-Ekiti Ekiti State, Nigeria

³Department of Economics, University of Ilesa, Osun State, Nigeria

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90400285

Received: 27 March 2025; Accepted: 05 April 2025; Published: 11 May 2025

ABSTRACT

The relevance of foreign direct investments (FDIs) in increasing technical know-how, managerial expertise, and increasing the level of technological availability, and increasing the level of productivity can never be over emphasized. The study adopted the system Generalized Method of Moment (sys-GMM) to account for the issues of endogeneity, the system GMM model to investigate the relationship among technology diffusion variables (foreign direct investment and import of capital goods and services), technology gap (relative backwardness as a proxy by distance from technology frontier and income gap IGAP), natural resources endowment (NRED) and total factor productivity (TFP) in Sub-Sahara Africa countries as specified in the model, while trade openness (TOP) and population growth (POPGW) are the control variables in the model between 1996 and 2022; the data for this study were sourced from the World Bank development indicator and the Central Banks of some selected countries. The result from the model reveals that FDI, NRED, and IGAP are positively related to TFP growth in Sub-Sahara Africa economies. The coefficients are positive and significant at 1% and 5% levels of significance for FDI and NRED respectively but very low,

Key Words: foreign direct investments, technological gap, total factor productivity, trade openness, resources endowment

INTRODUCTION

Technological progress has been identified as one of the major drivers of productivity growth (Solow, 1956), and developing economies that have been able to close the technology gap between them and technology frontier countries have transformed themselves into high-income in a historically short period (Fekitamoeloa, 2018).

Technology transfer/diffusion plays a central role in the process of economic development as it assists in the delivery of new technologies to the place of their needs or relevance. This is in contrast to the traditional growth framework, where technological change was left as an unexplained residual. The recent growth literature (Dohlman, 2009, Oronti, Adewusi, and Megbowon, 2012) highlighted the dependence of growth rates on the state of domestic technology relative to the rest of the world. Thus, growth rates in developing countries are, in part, explained by a ‘catch-up’ process in the level of technology. In other words, a typical model of technology diffusion, explains the rate at which an economic growth of a backward country depends on the extent of adoption and implementation of new technologies that are already in use in leading countries (Personne, and Susan, 2009).

Technology diffusion takes place through a variety of channels that involves the transmission of ideas and new technologies, importation of high-technology products, adoption of foreign technology, and acquisition of human capital through various means which are important conduits for the international diffusion of technology. Foreign direct investment is also considered to be a major channel for the diffusion of advanced technologies from developed nations to developing countries. Foreign direct investment companies are among the most technologically advanced firms, accounting for a substantial part of the world’s research and development (R&D) investment. Some research on economic growth (Findlay, 1978, Wang, 1990) has highlighted the role of foreign direct investment in the technological progress of developing countries. Findlay (1978) postulates that foreign direct investment increases the rate of technical progress in the host country through a ‘contagion’ effect from the more advanced technologies, used by foreign firms. Wang (1990) incorporated this idea into a model that is more in line with the neoclassical growth framework, by assuming that the increase in ‘knowledge’ applied to production is a function of foreign direct investment (FDI).

Africa remains the region with the lowest level of productivity globally and there have been many reasons attributed to this unenviable position. This ranges from low human capital interest, (UNDP, 2016) to poor or weak institutions (UNCTAD, 2003a; Havnevik et al., 2007) and limited share in the global production value chain (World Investment Report 2013; UNIDO, 2015). Also, an important understanding of the growth of productivity in the region is to relate its evolution to both inflows of FDI and Africa’s position in the adoption and diffusion of technology from frontier regions (Global economic prospects, 2018). FDI inflow to developing countries has grown dramatically in the last two decades, Africa’s share of global FDI has remained small and even this small share is concentrated in the extractive industry.

The existing literature had contributed to knowledge in terms of the effect of FDI on productivity/economic growth in Africa. It has been noted that some scholars, such as Kumar and Pradhan (2002) and Sylwester (2005), asserted that FDI has differential effects in different regions. Despite these divergent results, they have not been able to fully capture the overall effect of technology diffusion on productivity growth in African economies especially in resources endowed Sub-Saharan African countries.

This study is important and unique for many reasons. Firstly, it has extended previous studies (Kumar, and Pradhan, 2002; Sylwester, 2005; Aregbesola, 2014; Olawumi, and Olufemi, 2016; and Christopher and Prosper, 2018) by investigating the combined effect of the import of goods and services, FDI, and technology gap, on productivity growth across Sub-Saharan Africa countries, using two measure of backwardness (distance from technology frontier and income gap) adapted from Christopher and Prosper (2018). Secondly, it employed foreign direct investment and import of goods and services to proxy for technological diffusion.

LITERATURE REVIEW

The role of technology in the economic growth process had been widely examined from different perspectives since the development of the technological gap approach by Posner (1961), Gomulka (1971), Cornwall (1976, 1977), and others, (see Fagerber, 1987, 1988b, Fagerber and Verspagen, 2002). This approach emphasized that the international economic system is characterized by differences in technology levels and trends. These differences can only be overcome through radical changes in technology, economic growth, and social structures. Neoclassical theories traditionally measure the level of technological development of a country primarily on the relationship between capital and labour cost. For instance, firms are given ample opportunities to select the mix of factors to use at long-run production choice and cost by applying the marginal decision. Technical change in this framework is a shift of the production function resulting from some unexplained exogenous innovation, or firms optimizing research and development (R&D) choices with predictable outcomes.

Saha, S. K. (2024) adopts the system GMM to determine the role that foreign direct investment (FDI) plays on the level of labor productivity (LP) and productive capacity index (PCI) for 88 countries from 2000 to 2018. The study finds that FDI had a positive impact on LP, the findings also show that the internal mechanism of PCI assist to moderate the influence of FDI on LP.

Essel, (2023), adopts a robust dynamic panel system of generalized method of moment (GMM) to examine the relationship that exist between foreign FDI and the level of productivity and also check the possibility of spill over moving from FDI to the level of productivity of selected 19 industries in Ghana from 2010 t0 2020. The study finds a negative relationship between technological spill-over and total factor productivity, while FDI also shows a positive relationship.

Asongu and Odhiambo, (2023) utilize the generalized method of moment (GMM) to examine the relevance from information technology to FDI and factor productivity in 25 countries in Sub-Sahara African countries. The study finds deficiency in the level of productivity among the countries and a high potential for information technology. Information technology also modulates FDI which consequently influences total factor productivity.

Amidi and Hishan, (2022) adopt the Boolean research technique to review 1 existing articles relating to relevance of the FDI on the society from five perspective of technology, productivity, economy, environment, and energy. The study finds a positive impact of FDI on workers productivity, FDI increases the level of emission in the middle-income countries but reduces emission in the high-income countries. The African countries also witnessed economic growth from FDI. FDI also has significant impact on the level of wood productivity.

Oloniluyi (2019), observed a significant positive relationship between foreign direct investment, resource endowment, and trade openness with total factor productivity in the Southern region of Africa, which is attributed to the high commodity prices and development of regional infrastructure by the regional economic communities and boosting the regional terms of trade to attract foreign investors to the region.

Zidouemba, & Elitcha, (2018), established a significant negative relationship between resources rent and Total Factor Productivity Growth Rate (TFPGR). The results validate the assertion of  the resources curse. Boghean and State (2015) examine the relationship between foreign direct investments and hourly productivity from 200 to 2012 for European Union. The study finds a strong connection between of foreign direct investments outflows and productivity zones. On the other hand, the study finds the absence of relationship between foreign direct investments inflows and average labor productivity.

 Xu (2000), find that FDI is more productive when the host country has a minimum stock of human capital, Wang and Wong (2009), assert that the negative relationship between FDI and TFP growth will decrease in absolute value and turn positive with an increase in human capital development in the developing countries. from the reviewed literature it was discovered that there several factors determining the interactions between foreign direct investment and total factor productivity growth which includes proximity of the host countries to technology frontier countries; level of human capital development which may in turn to increase the absorptive capacity; natural resources endowment.

Borensztein, et. al, (1998), hypothesized that generating technology diffusion from the developed countries to developing nations through foreign direct investment raises economic growth faster in the host country depending on their absorptive capabilities. Dosi (1997), sees policies for technological and industrial development as the interplay among Innovative opportunities; the incentives to exploit those opportunities; the capabilities of the agents to achieve success conditional on their perception of both opportunities and incentives; and the organizational arrangements and mechanisms through which technological advances are search for and implemented.

METHODOLOGY

Types and Sources of data

The empirical test was carried out using a panel of 48 sub-Saharan African countries between 1996 and 2022; the data for this study were sourced from the World Bank development indicator website and the Central Banks of some selected countries. Data on Total Factor Productivity (TFP) was computed following the Solow Residual as the ratio of output to the weighted average of capital and labour input,

\[
SR(t) = \frac{\partial Y/\partial t}{Y} – \left( \alpha \cdot \frac{\partial K/\partial t}{K(t)} + (1 – \alpha) \cdot \frac{\partial L/\partial t}{L(t)} \right)
\]

 Therefore, for the purpose of this research work,

\[
TFP = \frac{GDP_t}{K^\alpha + L^{1 – \alpha}}
\]

Where GDP_t is Total Production in an economy; K is Capital in the production proxy by gross capital formation and L represent labour also proxy by labour force, as written by (Obaidullah 2015); Technology Gap or Relative Backwardness were measured using Distance from Technological Frontier (DISTF) and Income Gap (IGAP). Distance from technological frontier (DTF) is the ratio of technology level in the leader to the technology level of the host countries which is proxy by ratio of labour productivity of the frontier to the labour productivity of the host countries while income gap is the ratio of income of the leader country to the income of the host countries,\[\ln\left( \frac{Y_{\text{max},it}}{Y_{it}} \right)\] .

That is, ratio of GDP per capita of the leader’s country to GDP per capital of the host country as measure by (Ashraf & Herer 2014, as cited in Christopher and Prosper, 2018); also, Trade Openness (TOP) were calculated as the ratio of import-export to GDP

\[
TOP = \frac{\text{Import} + \text{Export}}{GDP_t}
\]

The study geographically selected 48 countries of sub-Saharan Africa. According to the United Nations, (2010) sub-Saharan African countries consist of all African countries that are fully or partially located south of the Sahara. The study excluded the Sahara or Northern African countries which are partly members of League of Arab countries. These countries are called countries of the Maghreb and also part of the Middle East.

Estimation techniques

Following the nature of this research work, the system Generalized Method of Moment (sys-GMM) were employed as estimation technique to account for the issues of endogeneity, the system GMM model is designed as follow:

\[
TFP_{it} = \beta_{0it} TFP_{it-1} + \beta_{1it} FDI_{it} + \beta_{2it} POPGW_{it} + \beta_{3it} TOP_{it} + \beta_{4it} DTF_{it} + \beta_{5it} IGAP_{it} + \beta_{6it} NRS_{it} + \varepsilon_{it}\tag{1}
\]

\[
\Delta TFP_{it} = \Delta \beta_{0it} TFP_{it-1} + \Delta \beta_{1it} FDI_{it} + \Delta \beta_{2it} POPGW_{it} + \Delta \beta_{3it} TOP_{it} + \Delta \beta_{4it} DTF_{it} + \Delta \beta_{5it} IGAP_{it} + \Delta \beta_{6it} NRS_{it} + \varepsilon_{it}\tag{2}
\]

The System Generalised Method of Moment (GMM) estimation procedure is the preferred choice over other alternative panel-data estimation techniques such as instrumental variable panel-data fixed (or random) effects (IV FE/RE) and 2SLS panel FE/RE.

EMPIRICAL FINDINGS

Table 1.1: Panel Unit Root Test

Source: Author’s computation, 2018

Table 1.1, present the panel unit root test result for Sub-Saharan Africa countries. The result indicates that at 1% level of significance all series are not stationary at level but stationary at first difference. This implies all variables are integrated of order one, I(1).

Table 1.2: the result of system-GMM model estimation with TFP growth as the dependent variable

Notes: p-value are in parenthesis. *, **, *** indicate statistical significance at 10%, 5%, and 1% level respectively

Source: Author’s computation, 2018

Table 1.2 summarized and presented the results on the relationship among technology diffusion variables (foreign direct investment and import of capital goods and services), technology gap (relative backwardness as a proxy by distance from technology frontier and income gap IGAP), natural resources endowment (NRED) and total factor productivity (TFP) in sub-Sahara African countries as specified in the model, while trade openness (TOP) and population growth (POPGW) are the control variables in the model.

The result from the model reveals that FDI, NRED, and IGAP are positively related to TFP growth in Sub-Sahara African economies. The coefficients are positive and significant at 1% and 5% levels of significance for FDI and NRED respectively but very small, suggesting that the effect of one on the other is minute. While IGAP establishes an insignificant positive relationship with TFP growth, this implies that IGAP is not fit enough in measuring the TFP growth in the region.

The positive interaction among these variables in the region was owing to the adoption of diverse developmental and trade policies by different African countries span through 1995-2016. Most of these African countries have made efforts to improve the investment environment over the years, for instance, reducing taxes, establishing an Investment Promotion Agency (IPA) to better assist foreign investors, and abolishing FDI-related restrictions. Some oil-producing countries in Africa also seek to improve their policies to benefit more from FDI in the oil industry. Furthermore, increased attention has been paid to many less develop Africa countries’ policy initiatives at the bilateral, regional and multilateral levels to enhance international cooperation and integration in FDI-related matters. This improved the investment environment over the decade 2001- 2010 (UNCTAD, 2010). In 2006 the United Republic of Tanzania adopted under the guidance of UNCTAD its Blue Book which developed performance and client charters for the government agencies involved in monitoring the implementation of investment-related regulations and tax administration, with a special whistleblower facility with a telephone hotline to report corruption. Similarly, Burundi’s 2008 Investment Code was inspired by the Model Code of the East African Community and the COMESA framework. More so, some of these countries have introduced targeted sector-specific incentives. In 2003 Ethiopian energy sector investment code allowed wholly foreign-owned investment in the sector; the 2006 Equatorial Guinea hydrocarbon law introduced production sharing contracts awarded through competitive bidding; the 2006-2007 Lesotho budget measures support the textile industry and manufacturing sub-sectors; and in 2006 Swaziland curbed the participation of foreigners in small businesses, especially in the retail sector traditionally dominated by foreigners.

This result is in line with Hayat, 2018: Hayat, (2018) state that, the impact of FDI inflows on economic growth changes with the changes in the size of the natural resource sector. The estimated positive impact of FDI inflows on economic growth declines with the expansion in the size of natural resources. Beyond a certain limit, a further expansion in the size of the natural resource sector will lead to a negative effect of FDI on economic growth.

IMPT, DIST, POPGW, and TOP each have a significant negative relationship with TFP at 1%, 5%, 5%, and 10% respectively. DIST exhibit a significant negative impact on total factor productivity growth showing that the larger the distance of host countries to the technology frontier the lower the productivity growth rate, this is in agreement with the findings of Christopher and Prosper (2018). The wider the distance from the frontier country the lower the adoption of its technologies by the host countries, meanwhile, this will inversely affect the FDI in terms of total factor productivity.

The negative effect of the import of goods and services was boiled down to the fact that the majority of these countries cannot produce the required technologies embodied in capital goods locally, depending on the high importation of capital goods in diffusing the advanced technology from the frontier countries which required huge foreign exchange resource. Capital and intermediate goods are therefore perquisites inputs for economic growth, and if there are no sufficient foreign exchange resources to acquire the desired foreign technology needed, such an economy will be forced to operate below the optimal level and achieve a lesser growth rate. This assertion was rooted in Amsden (1998) who attributed the economic development of all late industrialized countries like Japan, Brazil, South Korea, Turkey, Mexico, and India to learning by doing through borrowing foreign technology rather than creating new products.

The model also gives a contrast result with the assumption of the new endogenous growth models with the notion that a positive relationship exists between trade openness and economic growth as a result of the international diffusion of advanced technologies (Coe & Helpman, 1995; Grossman & Helpman, 1991a; Romer, 1994). A country with a higher degree of openness has a greater ability to use technologies generated in advanced economies, and this capability leads them to grow more rapidly than a country with a lower degree of openness

Population growth in the selected region exhibits a significant negative relationship with TFP growth. This is in line with Malthus’s publications on population which ensued an argument between its supporters and critics. Malthus supporters agitated for population control due to its negative impact on economic growth, while the critics believe that advancement in science and technology will ameliorate the gap, especially in the area of agriculture. The result implies that the Sub-Saharan African population growth rate is outweigh its productive capacity, in terms of technology advancement, the region’s population growth without commensurate with technology diffusion.

Furthermore, the result finds a shred of very significant evidence that natural resources endowment in Sub-Saharan African countries is positively associated with TFP growth. The estimate indicates that a unit increase in the natural resource endowed will lead to a 0.01 percentage (or a constant) increase in TFP growth with the inclusion of FDI into resource-endowed countries of Africa. This finding is in contrast with the work of Álvarez, and Labra, (2015), Der Ploeg (2011), and Sachs and Warner (1999) and consistent with Chambers and Guo (2007). Der Ploeg (2011) affirms that natural resource exploitation may negatively affect economic growth due to several social, environmental, and economic factors that would explain the so-called resource curse. It also confirms Asiedu’s observation in terms of foreign direct investment (FDI) into Sub-Saharan Africa (SSA), that the common perception is FDI is largely driven by natural resources and market size. This perception was consistent with the data from the World Bank 2004 that pinpoint the three largest recipients of FDI in Africa. “the three largest recipients of FDI are Angola, Nigeria, and South Africa from 2000 to 2002, these countries absorbed about 65 percent of FDI flows to the region” (World Bank, 2004b). FDI in resource-rich countries is concentrated in natural resources, and investments in such industries tend not to generate many positive spillovers (e.g. technological transfers, employment creation) that are often associated with FDI (Asiedu, 2004).

The significant positive relationship between FDI and TFP growth in this model can be explained by attending to the fact that natural resources activities are more capital intensive, which require little engagement of labour to produce the maximum output by the Multinational Corporations (Álvarez, & Fuentes, 2006; Arias, Atienza, & Cademartori, 2012; Álvarez, & Labra, 2015). Many West African countries receive much FDI in natural resource-based sectors, as they are rich in minerals, oil, and natural gas. Indeed, literature has shown that the need to get secure access to natural resources is one of the main motivations driving MNCs to Africa and its sub-regions, indicating one of the key characteristics of African countries in terms of natural resource endowment (Dupasquier and Osakwe, 2006, Asiedu, 2006).

TFP growth in Sub-Saharan African countries is characterized by a great desire which allows for rapid and dramatic change. The model established higher levels of immediate past year’s level of TFP are associated with current levels of TFP growth and this is significant at a 5 percent significance level. The result, however, suggests that although higher levels of immediate past TFP are positively and significantly associated with current levels of TFP growth, above a certain point, higher levels of past TFP act to increase the current level of TFP between 0.85-1.00 percent holding other factors constant. This relationship suggests that the marginal effect of past TFP exhibits an increasing return for current TFP growth in Sub-Sahara Africa countries.

Arellano bond test AR(1)p-value 0.0165 show there is first-order autocorrelation which is expected by the set-up of system GMM estimators. But AR (2) showed a non-significant p-value of 0.1823. Therefore, this study rejects the null hypothesis and accepts the alternative hypothesis. Meaning that The AR(2) test results indicate no second-order autocorrelation in the residuals. Finally, The Sargan test is based on the assumption that model parameters are identified via a priori restrictions on the coefficients and tests the validity of over-identifying restrictions (Sargan 1958). To accept the null hypothesis the p-value of the result must be superior to 0.05 and rejected. when it is inferior. Therefore, this model with a Sargan p-value (0.96) accepts the null hypothesis that all instruments are valid. This implies that the instruments are not correlated with the residual. This makes the instruments valid.

CONCLUSION AND POLICY RECOMMENDATIONS

Judging from this study, it is concluded that Sub-Saharan African economic progress cannot be detachable from foreign investment, natural resources endowment, and technological limitation from the technologically advanced frontiers countries. More so, the slow-paced of cash-up in Sub-Saharan Africa was attributed to the wider gap between some of the African countries and technology frontiers countries, combined with a high African population increase without a corresponding increase in productivity.

The system Generalized Method of Moment (sys-GMM) was utilized to consider the issues of endogeneity, the system GMM model was also adopted to investigate the relationship among technology diffusion variables (foreign direct investment and import of capital goods and services), technology gap  like relative backwardness as a proxy by distance from technology frontier and income gap IGAP, natural resources endowment (NRED) and total factor productivity (TFP) in sub-Saharan African countries as specified in the model, trade openness (TOP) and population growth (POPGW) are the control variables in the model. The result from the model reveals that FDI, NRED, and IGAP are positively related to TFP growth in Sub-Sahara African economies. The coefficients are positive and significant at 1% and 5% levels of significance for FDI and NRED respectively but very small, suggesting that the effect of one on the other is minimal. While IGAP establishes an insignificant positive relationship with TFP growth, this implies that IGAP is not fit enough in measuring the TFP growth in the region.

Despite the insight provided by this study to providing contributions on theoretical, empirical, and methodological levels. It offers an original contribution to the understanding of the determinants of total factor productivity (TFP) in Sub-Saharan Africa by emphasizing the interaction between foreign direct investment (FDI), trade openness, natural resource endowment, and technological distance from the global frontier. This cross-cutting approach thereby, enriches the growth debate in developing countries, which is often limited to one-dimensional perspectives. This is owing to the possibility of including institutional and structural factors, which are essential for understanding productive performance. Variables such as institutional quality, political stability, or administrative efficiency may also have been they strongly influence the effectiveness of FDI. Moreover, FDI is treated in an aggregated manner, without sectoral distinction, which prevents the identification of differentiated effects, foe example those between investments in extractive industries with limited local spillovers and those in industry or services that could generate more positive externalities. This therefore, can be considered in further studies.

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