This study seeks to examine the determinants of export sophistication in sub-Saharan Africa (SSA), with a specific focus on evaluating whether foreign direct investment (FDI), in combination with human capital (HC) and institutional quality, enhances export sophistication.
The study uses the system generalised method of moments on a balanced panel of 35 SSA countries for the period 2010–2023.
The results show that while FDI alone has no significant effect, its interaction with HC and institutional quality significantly boosts export sophistication. Gross domestic product (GDP) per capita and trade openness are also positively associated with export sophistication, while population has no effect. These findings suggest that the benefits of FDI depend on a country’s absorptive capacity.
The study contributes new empirical evidence to the limited literature on FDI and export sophistication in SSA. It underscores the need for deveoping policies that prioritise advanced education, encourage institutional reform and promote strategic trade to foster sustained structural transformation in the region.
The study contributes new empirical evidence to the limited literature on FDI and export sophistication in SSA and underscores the need for integrated policies that prioritise education, institutional reform and strategic trade to drive sustained structural transformation in the region.
1. Introduction
The importance of export sophistication in driving export growth and enhancing economic performance has been widely acknowledged in the literature (Hausmann et al., 2007; Besedeš and Prusa, 2011; Lin et al., 2017; Ngundu and Ngepah, 2019; Zhang and Xing, 2019; Abdmoulah, 2023). These studies emphasise that upgrading exports not only improves the value and diversity of a country’s export basket but also positively impacts economic development. Consequently, numerous studies have sought to identify the determinants of export sophistication (Weldemicael, 2012; Zhu and Fu, 2013; Kočenda and Poghosyan, 2018; Zapata et al., 2024). These factors include trade facilitation (Hu et al., 2022), aid for trade (Gnangnon and Roberts, 2015), patent protection (Zhang and Xing, 2019), environmental regulation (Wang et al., 2022), financial constraints (Wang et al., 2021) and foreign direct investment (FDI) (Wacker et al., 2016; Zhang and Xing, 2019; Zhang and Chen, 2020; Liu and Wang, 2022; Yan et al., 2023).
Of particular interest is the role of FDI in influencing export sophistication. The relationship between FDI and export sophistication remains complex and multifaceted. Zhang and Xing (2019) argue that FDI plays a critical role in fostering the production of high-quality products, which is facilitated by the transfer of advanced technology and knowledge spillovers. The ability of recipient countries to absorb and apply this technology is crucial for enhancing their export sophistication. However, the relationship between FDI and export sophistication is not unequivocally positive. While some studies highlight the beneficial spillover effects of FDI, others challenge the idea that FDI directly contributes to export upgrading.
In the context of sub-Saharan Africa (SSA), FDI inflows have historically been low and volatile. Figure 1, depicting FDI inflows into SSA from 1990 to 2022, demonstrates notable fluctuations over time. For instance, FDI reached a peak in 2014, likely driven by factors such as improved political stability and heightened global demand for commodities. However, there was a significant plunge in 2002, possibly linked to economic instability and shifts in the global investment landscape. In 2020, FDI inflows fell sharply to US $785 million, primarily due to the economic disruptions caused by the COVID-19 pandemic. This decline underscores the region’s vulnerability to external shocks and highlights the broader global economic uncertainties that can affect foreign investment. Despite these fluctuations, SSA has consistently attracted substantial FDI, indicating sustained foreign interest in the region.
The vertical axis on the left measures “F D I (in U S dollar million)” from negative 4000 to 12000 in increments of 2000 U S million dollars. The vertical axis on the right measures “Percentage,” from negative 1 to 1.5 in increments of 0.5 units. The chart displays three data series: F D I: It is represented by black vertical bars. The bars show significant fluctuations over the years, with peaks around 1997, 2006, and a major peak in 2013. The values are low in the early 1990s and rise significantly after 2005. The highest value is slightly over 10,000 in 2013, and the lowest is a negative value in 2001. F D I (Percent of G D P): It is represented by a dotted line. This line also shows volatility. It peaks around 1992 and 2006, reaching above 1.0 percent. The line drops to near negative 0.5 around 2001, and shows a general decline after 2013, ending below 0.5 percent in 2022. F D I (Percent of World): It is represented by a solid gray line. This line shows a less volatile pattern than the others, generally staying below 1.0 percent. It has a high point around 1992 and 2006, and a more recent peak around 2013. The line trends downwards after 2013, finishing below 0.5 percent in 2022. Note: All numerical values are approximated.Foreign direct investment in sub-Saharan Africa. Note: Secondary axis; FDI as a percentage of GDP and the World total. Source: UNCTADStat (2024)
The vertical axis on the left measures “F D I (in U S dollar million)” from negative 4000 to 12000 in increments of 2000 U S million dollars. The vertical axis on the right measures “Percentage,” from negative 1 to 1.5 in increments of 0.5 units. The chart displays three data series: F D I: It is represented by black vertical bars. The bars show significant fluctuations over the years, with peaks around 1997, 2006, and a major peak in 2013. The values are low in the early 1990s and rise significantly after 2005. The highest value is slightly over 10,000 in 2013, and the lowest is a negative value in 2001. F D I (Percent of G D P): It is represented by a dotted line. This line also shows volatility. It peaks around 1992 and 2006, reaching above 1.0 percent. The line drops to near negative 0.5 around 2001, and shows a general decline after 2013, ending below 0.5 percent in 2022. F D I (Percent of World): It is represented by a solid gray line. This line shows a less volatile pattern than the others, generally staying below 1.0 percent. It has a high point around 1992 and 2006, and a more recent peak around 2013. The line trends downwards after 2013, finishing below 0.5 percent in 2022. Note: All numerical values are approximated.Foreign direct investment in sub-Saharan Africa. Note: Secondary axis; FDI as a percentage of GDP and the World total. Source: UNCTADStat (2024)
Nevertheless, the graph reveals a critical concern in that SSA’s export structure remains predominantly reliant on raw commodities, with limited diversification into higher-value, technology-driven products. This suggests that while FDI inflows have been significant, they have not been effectively channelled into upgrading exports or diversifying the region’s economic base. The concentration of FDI in sectors such as extractive industries, which mainly export raw commodities, has not resulted in the technological advancements or value-added production necessary for shifting SSA’s export basket toward more sophisticated goods. This highlights the need for targeted policies that enhance the absorptive capacity of SSA’s workforce and strengthen institutional frameworks to ensure that FDI can be more effectively leveraged to drive export sophistication and foster sustainable economic development.
This research aims to contribute to the literature by examining the role of FDI in the sophistication of exports, with a particular focus on the unique context of SSA, where limited research exists on this topic. First, the study seeks to enrich the discourse on FDI and export sophistication by exploring the composite and multifaceted relationship between FDI and export upgrading in SSA. This is particularly important given that much of the existing literature, such as Ngundu and Ngepah (2019), has concentrated on the broader relationship between FDI and economic growth without delving deeply into export sophistication. Additionally, studies like Lin et al. (2017) have focused primarily on FDI’s impact on income, without considering its role in export quality and diversification. Second, this study makes a novel contribution by highlighting the moderating roles of human capital (HC) and institutional quality in this relationship. It emphasises that FDI alone is insufficient to spur export upgrading in SSA; rather, the success of FDI in driving export sophistication is contingent upon the absorptive capacity of the workforce and the strength of institutional frameworks. This builds on Wang et al. (2022), who argue that the relationship between FDI and export sophistication operates through both direct and indirect channels.
The contribution of this research has important implications for both the academic literature and policy. For the literature, it provides an improved understanding of how FDI influences export sophistication, specifically in SSA, where the existing evidence is sparse. For policymakers, the study underscores the importance of not only attracting FDI but also investing in HC development and improving institutional quality to ensure that FDI translates into meaningful export diversification and upgrading. The study also suggests that policy interventions aimed at strengthening these areas could significantly enhance the region’s export performance and overall economic growth.
The remainder of this paper is structured as follows: Section 2 provides a review of the related literature on the nexus between FDI and export sophistication and hypothesis development. Section 3 outlines the data and empirical strategy employed in the study. Section 4 presents and discusses the empirical results, while Section 5 concludes the paper and offers policy recommendations.
2. Literature review and hypothesis development
FDI can have both direct and indirect effects on export sophistication, although the empirical evidence on this relationship remains mixed (Harding and Javorcik, 2012). Direct effects arise when foreign and domestic firms collaborate in joint ventures to produce and export more sophisticated products to international markets (Weldemicael, 2012). Indirectly, FDI influences export sophistication through its spillover effects, particularly on the productivity and innovativeness of domestic firms. However, the literature on this nexus is varied and complex, with differing views on the extent and nature of FDI’s impact on export sophistication.
In the context of the above, three primary strands of empirical literature have emerged on the relationship between FDI and export sophistication. The first strand suggests that FDI has a significant positive spillover effect, enhancing export sophistication through various mechanisms, including increased productivity levels in firms receiving FDI or those owned by multinational enterprises (Anwar and Sun, 2018; Ozsoy et al., 2021; Yan et al., 2023). Additionally, FDI can promote export sophistication by fostering forward and backward linkages in the value chain. The second strand challenges this view, arguing that FDI does not generate spillover effects and has no substantial impact on export quality (Lenaerts and Merlevede, 2015). For example, Zhang and Chen (2020) examined FDI and export sophistication in China and found that outward FDI had no significant effect on export sophistication, although positive relationships emerged when regional data were considered.
The third strand posits that FDI could hinder technological progress, thereby negatively affecting export sophistication (Godart and Görg, 2013; Suyanto and Salim, 2013). Anwar and Sun (2018) also argue that while FDI’s spillover effects may allow firms with lower capabilities to enter international markets, these firms may still produce low-quality products. This variety in findings makes it challenging to definitively assess the role of FDI in enhancing export sophistication. Despite these mixed views, much of the literature supports the idea that FDI generates positive spillover effects that contribute to export upgrading. Therefore, we hypothesise that
FDI positively influences export sophistication.
The quality of HC plays a crucial role in determining the ability of countries to absorb and implement the technological and knowledge spillovers generated by FDI. Several studies have emphasised that skilled labour is essential for leveraging FDI to enhance export sophistication (Weldemicael, 2012; Zhang and Chen, 2020). A well-educated workforce can facilitate the adoption of advanced production techniques, improve innovation and drive the development of high-quality goods, thereby increasing export sophistication.
According to Hausmann et al. (2007), countries with a highly skilled workforce are better positioned to diversify their exports and move up the value chain. Zhu and Fu (2013) further support this argument by suggesting that HC development is a key factor in improving export quality in developing countries. Moreover, Ozsoy et al. (2021) assert that the effectiveness of FDI in driving export sophistication is contingent on the level of HC, with more developed HC enabling greater absorption of FDI-induced technological advancements. As such, countries with higher HC quality are likely to experience stronger positive impacts from FDI on export sophistication as the workforce is better equipped to take full advantage of foreign investments. We, therefore, hypothesise that
The quality of HC positively and significantly influences the relationship between FDI and export sophistication.
The role of institutional quality in shaping the relationship between FDI and export sophistication has been well documented in the literature. Strong institutions, characterised by good governance, regulatory frameworks and the protection of property rights, provide a conducive environment for the effective utilisation of FDI (Wang et al., 2022). According to Zhang and Chen (2020), the presence of sound institutions helps to maximise the benefits of FDI by ensuring that foreign investments are used efficiently and contribute to long-term economic development. In countries with weak institutional frameworks, the spillover effects of FDI may be limited as poor governance structures can hinder technology transfer, reduce investor confidence and undermine the overall impact of FDI on export quality (Weldemicael, 2012).
Institutional quality is particularly important for supporting innovation, protecting intellectual property and ensuring the rule of law, all of which are crucial for fostering export sophistication. As noted by Hausmann et al. (2007), strong institutions not only create a favourable environment for foreign investors but also enable the efficient allocation of resources, facilitating the development of higher-value, more sophisticated exports. Therefore, countries with better institutional quality are more likely to experience a stronger positive relationship between FDI and export sophistication as they provide the necessary conditions for foreign investments to translate into export upgrading. Hence, the following hypothesis is posed:
Institutional quality positively influences the relationship between FDI and export sophistication.
3. Data, descriptive statistics and empirical strategy
3.1 Data
This study utilises a balanced panel dataset of 35 SSA countries from 2010 to 2023. The selection of these countries is based on the availability of data. The primary dependent variable is the export sophistication index (EXPY), as developed by Hausmann et al. (2007). This index is computed using data obtained from the World Bank via the World Integrated Trade Solution platform. The EXPY measure assigns each good k that a country produces an intrinsic level of sophistication, PRODYk, which is calculated as the weighted average of the income levels of the exporters of that good. The weights are based on the revealed comparative advantage of each country i in good k. Mathematically, this is expressed as
where denotes the per capita gross domestic product (GDP) of country , is the exports of country in product and is the total exports of country i. Therefore, according to Hausmann et al. (2007), the export sophistication index is calculated as
Figure 2 illustrates the evolution of the log-transformed export sophistication index, obtained using the equation above. The figure reveals substantial heterogeneity in export sophistication across the selected SSA countries. Notably, countries such as Nigeria, South Africa, Botswana and Mauritius have maintained relatively stable export sophistication levels over the study period. However, these countries tend to have relatively higher levels of export sophistication than other SSA nations. In contrast, countries like Malawi, Zimbabwe and Niger have experienced a decline in their export sophistication index, reflecting a deterioration in the quality of their exports. On the other hand, Rwanda, Tanzania, Togo, Ethiopia and Uganda have shown significant improvements in export sophistication, indicating enhanced competitiveness in their export sectors.
The vertical axis in all graphs is labeled “log of (E X P Y)” and ranges from 8.5 to 10 in increments of 0.5 units. The horizontal axis in all graphs represents the year and ranges from 2010 to 2020 in increments of 5 years. The graph trends are as follows: Angola: The line remains stable at the value of 9.5. Benin: The line shows a decreasing trend with minor fluctuations. Botswana: The line is stable, showing very little change over the decade. Burkina Faso: The line remains consistent, follows a slightly decreasing trend in the beginning. Burundi: The line fluctuates significantly, with a peak in the early 2010s. Cameroon: This line remains stable, with a slightly declining trend near 2016. Central Africa Republic: The line is highly volatile, with a significant drop around 2015, followed by a rise. Congo Republic: The line shows a fluctuating trend with an overall slight decrease. Congo, Democratic Republic: The line is relatively stable with a slight upward trend. Côte d‘Ivoire: This line shows a stable trend with minor, gradual increases. Eswatini: The line shows an increasing trend. Ethiopia: The line shows a steady upward trend. Gabon: The line remains stable at the value 9.5. Gambia: The line is relatively stable with a major dip near 2017, followed by a rise. Ghana: The line shows a consistent, slightly upward trend. Kenya: The line is relatively stable with minor fluctuations. Lesotho: The line is stable with a slight downward trend. Madagascar: The line shows a fluctuating trend with a slight overall increase. Malawi: The line shows a relatively stable trend with a slight decline towards 2020. Mali: The line remains stable at the value of 9.25. Mauritania: The line is stable at 9.75, showing very little change. Mauritius: The line shows a relatively stable trend with a slight decline. Mozambique: The line shows a stable, slightly downward trend. Namibia: The line is stable, with a slight upward trend after 2016, followed by a decline. Niger: The line shows an overall decreasing trend. Nigeria: The line is relatively stable at value 9.5. Rwanda: The line shows a consistent upward trend. Senegal: The line shows a stable trend with minor fluctuations. Sierra Leone: The line remains relatively stable, with a peak in 2013, followed by a decline. South Africa: The line remains stable at the value 9.75 across all the years. Tanzania: The line shows an increasing trend with a slight dip in 2011. Togo: The line shows a consistent, slightly upward trend. Uganda: The line shows a stable trend with a slight, gradual increase after 2015. Zambia: The line is relatively stable with minor fluctuations. The line shows a small peak in 2015. Zimbabwe: The line shows a stable, slightly downward trend. Note: All numerical values are approximated.Export sophistication index for SSA countries. Source: Authors’ illustration
The vertical axis in all graphs is labeled “log of (E X P Y)” and ranges from 8.5 to 10 in increments of 0.5 units. The horizontal axis in all graphs represents the year and ranges from 2010 to 2020 in increments of 5 years. The graph trends are as follows: Angola: The line remains stable at the value of 9.5. Benin: The line shows a decreasing trend with minor fluctuations. Botswana: The line is stable, showing very little change over the decade. Burkina Faso: The line remains consistent, follows a slightly decreasing trend in the beginning. Burundi: The line fluctuates significantly, with a peak in the early 2010s. Cameroon: This line remains stable, with a slightly declining trend near 2016. Central Africa Republic: The line is highly volatile, with a significant drop around 2015, followed by a rise. Congo Republic: The line shows a fluctuating trend with an overall slight decrease. Congo, Democratic Republic: The line is relatively stable with a slight upward trend. Côte d‘Ivoire: This line shows a stable trend with minor, gradual increases. Eswatini: The line shows an increasing trend. Ethiopia: The line shows a steady upward trend. Gabon: The line remains stable at the value 9.5. Gambia: The line is relatively stable with a major dip near 2017, followed by a rise. Ghana: The line shows a consistent, slightly upward trend. Kenya: The line is relatively stable with minor fluctuations. Lesotho: The line is stable with a slight downward trend. Madagascar: The line shows a fluctuating trend with a slight overall increase. Malawi: The line shows a relatively stable trend with a slight decline towards 2020. Mali: The line remains stable at the value of 9.25. Mauritania: The line is stable at 9.75, showing very little change. Mauritius: The line shows a relatively stable trend with a slight decline. Mozambique: The line shows a stable, slightly downward trend. Namibia: The line is stable, with a slight upward trend after 2016, followed by a decline. Niger: The line shows an overall decreasing trend. Nigeria: The line is relatively stable at value 9.5. Rwanda: The line shows a consistent upward trend. Senegal: The line shows a stable trend with minor fluctuations. Sierra Leone: The line remains relatively stable, with a peak in 2013, followed by a decline. South Africa: The line remains stable at the value 9.75 across all the years. Tanzania: The line shows an increasing trend with a slight dip in 2011. Togo: The line shows a consistent, slightly upward trend. Uganda: The line shows a stable trend with a slight, gradual increase after 2015. Zambia: The line is relatively stable with minor fluctuations. The line shows a small peak in 2015. Zimbabwe: The line shows a stable, slightly downward trend. Note: All numerical values are approximated.Export sophistication index for SSA countries. Source: Authors’ illustration
The key covariates used in the econometric estimation include population (Pop), which serves as a proxy for country size and is measured in millions, and HC, represented by a human capital index that incorporates years of schooling and returns to education. It is hypothesised that improvements in HC lead to higher export sophistication (Weldemicael, 2012). Institutional quality is proxied by the rule of law, as suggested by Kočenda and Poghosyan (2018), with this study hypothesising a positive relationship between institutional quality and export sophistication, despite the ongoing debate in the literature. Lastly, FDI is considered a key driver of technology diffusion, enhancing a country’s production capacity and export sophistication. FDI inflows, measured in constant US dollars, are used as a proxy for FDI. Control variables include control of corruption, government effectiveness, political stability and absence of violence, regulatory quality and trade openness. For a complete list of the variables and their descriptions, see Table A1 in Appendix.
Tables A2–A4 present the descriptive statistics, correlation matrix and variance inflation factors (VIFs), respectively, offering important insights into the characteristics and interrelationships of the variables used in the analysis. Table A2 shows that export sophistication has a relatively narrow distribution, while variables such as FDI (mean = 4.29%, SD = 5.85), institutional quality (mean = −0.597) and population exhibit notable variation, reflecting heterogeneity among SSA countries. Table A3 reveals moderate positive correlations between export sophistication and key predictors such as GDP per capita (0.523) and trade openness (0.442), with no correlation exceeding the 0.8 threshold, suggesting no serious multicollinearity. Table A4 confirms this, with all VIF values well below the critical value of 5 and a mean VIF of 1.79, indicating that multicollinearity is not a concern.
3.2 Empirical strategy
This study investigates the factors influencing export sophistication, with the export sophistication index (EXPY) as the dependent variable. The index is constructed following the methodology developed by Hausmann et al. (2007), capturing the weighted average income per capita of countries that export a specific product. In this context, countries are indexed by i and products by k.
The econometric model used in this study is based on the specification proposed by Weldemicael (2012), which builds on the framework of Hausmann et al. (2007). The model is modified to account for the possibility of a positive feedback effect on export sophistication. Specifically, the model includes the lagged value of the dependent variable (EXPY) to capture the persistence of export sophistication over time. Thus, the model is specified as follows:
Including the lagged value of the dependent variable implies that endogeneity exists between the lagged value of EXPY and fixed effects in the disturbance term (Weldemicael, 2012). This calls for the use of the Arellano and Bond (1991) generalised method of moments (GMM) estimators over panel ordinary least squares as the former deals with autocorrelation of the error term in panel estimation.
4. Results and discussions
This section presents and interprets the results of the analysis on the relationship between FDI and export sophistication in SSA. The findings are derived from the system generalised method of moments (SYS-GMM) estimation and are reported in Table 1. The analysis further explores the moderating effects of HC and institutional quality in this relationship.
Determinants of export sophistication
| Variables | (1) | (2) | (3) |
|---|---|---|---|
| lnExport sophistication | 0.662*** | 0.660*** | 0.667*** |
| (0.106) | (0.0978) | (0.107) | |
| lnGDP per capita | 0.0650** | 0.0723** | 0.063** |
| (0.0306) | (0.0347) | (0.032) | |
| lnopenness | 0.0939* | 0.0866* | 0.096** |
| (0.0524) | (0.0498) | (0.049) | |
| lnpopulation | 0.0170 | 0.0126 | 0.017 |
| (0.0165) | (0.0151) | (0.016) | |
| FDI | 0.00129 | 0.0131* | 0.00682* |
| (0.00130) | (0.008) | (0.004) | |
| FDI*human capital | 0.00689* | ||
| (0.00382) | |||
| FDI*institutional quality | 0.00930* | ||
| (0.005) | |||
| Constant | 1.943*** | 2.014*** | 1.916*** |
| (0.500) | (0.438) | (0.500) | |
| Observations | 315 | 315 | 315 |
| Number of countries | 35 | 35 | 35 |
| Country effect | No | No | No |
| Year effect | No | No | No |
| Hansen_test | 28.50 | 28.48 | 28.04 |
| Hansen Prob | 0.438 | 0.439 | 0.462 |
| AR(1)_test | −2.383 | −2.395 | −2.376 |
| AR(1)_p-value | 0.0172 | 0.0166 | 0.0175 |
| AR(2)_test | 0.570 | 0.534 | 0.580 |
| AR(2)_p-value | 0.569 | 0.593 | 0.562 |
| No. of instruments | 32 | 32 | 33 |
| Variables | (1) | (2) | (3) |
|---|---|---|---|
| lnExport sophistication | 0.662*** | 0.660*** | 0.667*** |
| (0.106) | (0.0978) | (0.107) | |
| lnGDP per capita | 0.0650** | 0.0723** | 0.063** |
| (0.0306) | (0.0347) | (0.032) | |
| lnopenness | 0.0939* | 0.0866* | 0.096** |
| (0.0524) | (0.0498) | (0.049) | |
| lnpopulation | 0.0170 | 0.0126 | 0.017 |
| (0.0165) | (0.0151) | (0.016) | |
| FDI | 0.00129 | 0.0131* | 0.00682* |
| (0.00130) | (0.008) | (0.004) | |
| FDI*human capital | 0.00689* | ||
| (0.00382) | |||
| FDI*institutional quality | 0.00930* | ||
| (0.005) | |||
| Constant | 1.943*** | 2.014*** | 1.916*** |
| (0.500) | (0.438) | (0.500) | |
| Observations | 315 | 315 | 315 |
| Number of countries | 35 | 35 | 35 |
| Country effect | No | No | No |
| Year effect | No | No | No |
| Hansen_test | 28.50 | 28.48 | 28.04 |
| Hansen Prob | 0.438 | 0.439 | 0.462 |
| AR(1)_test | −2.383 | −2.395 | −2.376 |
| AR(1)_p-value | 0.0172 | 0.0166 | 0.0175 |
| AR(2)_test | 0.570 | 0.534 | 0.580 |
| AR(2)_p-value | 0.569 | 0.593 | 0.562 |
| No. of instruments | 32 | 32 | 33 |
Note(s): AR: autoregressive; Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1
Table 1 displays the determinants of export sophistication across three model specifications. Column 1 presents the baseline model, which investigates the direct effects of FDI and other control variables. Columns 2 and 3 introduce interaction terms to assess whether HC and institutional quality, respectively, condition the impact of FDI on export sophistication.
In Column 1, the coefficient of FDI is positive but statistically insignificant, indicating that FDI, in isolation, does not have a significant effect on export sophistication in SSA. This finding aligns with the results of Zhang and Chen (2020), who suggest that FDI alone may not be sufficient to foster sophistication unless certain complementary conditions are met. In contrast, GDP per capita and trade openness have significant positive effects. A higher GDP per capita suggests that countries with greater levels of economic development tend to produce and export more sophisticated goods. Likewise, trade openness positively affects export sophistication, supporting the argument that integration into global markets facilitates access to advanced technologies and encourages domestic firms to upgrade their products in response to global competition.
In Column 2, the interaction between FDI and HC is introduced. The coefficient on the interaction term is positive and statistically significant, suggesting that HC plays a crucial role in enabling FDI to enhance export sophistication. This finding supports the argument by Ozsoy et al. (2021) that the absorptive capacity of the domestic workforce is essential for the successful transmission of technology and know-how from foreign investors. It implies that FDI is more likely to contribute to export sophistication when SSA countries invest in improving education and skill development.
Column 3 examines the interaction between FDI and institutional quality. The interaction term is also positive and statistically significant, indicating that countries with better institutional frameworks are more capable of leveraging FDI to improve export sophistication. This result reinforces the conclusions of Kočenda and Poghosyan (2018), who argue that effective institutions reduce transaction costs, enforce contracts and create an enabling environment for productive investment. Notably, the interaction term in Column 3 is larger than that in Column 2, suggesting that institutional quality may be a more critical channel than HC in mediating the FDI–export sophistication relationship in SSA.
The positive and highly significant coefficient of the lagged dependent variable across all specifications confirms the persistence of export sophistication over time. This suggests that improvements in export sophistication are path-dependent and reflect long-term investment and structural transformation processes. As Weldemicael (2012) notes, building export capacity is often a gradual process involving sustained policy support, international partnerships and trade facilitation mechanisms.
Surprisingly, population size, used as a proxy for market size, does not show a statistically significant effect across the models, despite earlier studies such as Weldemicael (2012) highlighting its importance. This could imply that market size alone, without corresponding improvements in infrastructure, productivity and education, may not directly translate into export sophistication.
Contradictions appear when reconciling the regression coefficients with the later descriptive narrative that suggests FDI increases export sophistication by 0.01% and that population has a strong positive impact. These interpretations are inconsistent with the regression results in Table 1, where the FDI coefficient is not significant and population is also insignificant across all specifications. Hence, these claims should be either revised or omitted to maintain empirical consistency.
The results indicate that FDI can positively impact export sophistication in SSA, but only when coupled with adequate levels of HC and strong institutional quality. This underscores the importance of context-specific absorptive capacities in realising the developmental benefits of foreign investment.
5. Conclusion and policy implications
Motivated by the urgent need for economic modernisation and transformation in SSA, this study set out to examine the determinants of export sophistication in the region. Using panel data from 35 SSA countries and applying SYS-GMM estimation technique, the study sought to identify the key drivers influencing the complexity and value of exports. The empirical results highlight that the lagged value of export sophistication, FDI, HC and GDP per capita are statistically significant factors in shaping export sophistication. These findings confirm the relevance of both domestic capabilities and international linkages in fostering structural change in export patterns.
One of the critical insights from the analysis is that FDI, on its own, does not significantly contribute to export sophistication. However, when FDI is coupled with high levels of HC and robust institutional frameworks, its effect becomes both positive and statistically significant. This suggests that the absorptive capacity of domestic economies, measured through education levels and institutional effectiveness, is a vital condition for translating foreign investment into meaningful improvements in the structure of exports. Additionally, trade openness and rising GDP per capita were found to positively affect export sophistication, emphasising the role of global market integration and economic development in advancing export quality. The persistence of export sophistication, as indicated by the significance of its lagged value, points to the path-dependent nature of structural transformation. This implies that consistent and long-term policy efforts are required to sustain and enhance export sophistication over time.
Considering these findings, several policy recommendations emerge. Firstly, strengthening HC development should be a top priority for SSA countries. Investment in education and skills training, particularly in areas that support industrial upgrading and technological absorption, is crucial. Reforms in vocational and tertiary education can enhance the ability of the workforce to capitalise on the knowledge spillovers that accompany FDI. Secondly, institutional quality must be improved to create an enabling environment for economic transformation. Policies aimed at increasing transparency, reducing bureaucratic inefficiencies and upholding the rule of law are essential, not only for attracting higher-quality FDI but also for empowering domestic firms to move into more sophisticated export activities. Thirdly, trade openness and infrastructure development should be pursued to support export upgrading. Improving transport and logistics networks, as well as customs systems, can lower trade costs and facilitate access to regional and global markets. Regional integration should also be deepened to encourage competitive pressures that drive innovation and efficiency.
Furthermore, aligning FDI policy with industrial development strategies is essential. Rather than adopting a generic approach to FDI promotion, governments should target investments in sectors with high potential for value addition. Encouraging technology transfer and fostering linkages between foreign investors and local supply chains can strengthen domestic industrial capacity and enhance backward linkages. Lastly, while export sophistication is an important element of economic development, it should be situated within a broader framework of long-term structural transformation. This requires a coordinated policy approach that integrates industrial, educational and innovation strategies to ensure the sustainability and inclusiveness of economic gains.
In terms of areas for future research, further investigations could examine the differentiated effects of various forms of FDI, such as greenfield investments versus mergers and acquisitions, on export sophistication outcomes. Additionally, country-specific case studies could offer rich insights into how improvements in HC and institutional quality interact with trade and investment policies to influence export upgrading. Such research would contribute to a deeper understanding of the mechanisms through which SSA countries can achieve sustainable export transformation and economic development.
The supplementary material for this article can be found online.

