metadata: authors: 0: author: Kassa,Woubet
1: author: Ndubuisi,Gideon Onyewuchi
2: author: Owusu,Solomon
count: Africa
docna: 0: docna: Economic Transformation in Africa : The Role
            of North-South and South-South Trade
docty: Policy Research Working Paper
abstracts: cdata!: This paper contributes to the discussion
            on Africa's pathways to economic transformation by
            examining the roles of trade
            patterns&#x2014;specifically, South-South and
            North-South trade&#x2014;focusing on intermediate and
            capital goods sourced from both the Global North and the
            Global South. The paper relies on a panel dataset comprising
            44 African countries from 2000 to 2022. To address
            endogeneity concerns, it uses a two-stage least squares
            method, employing instrumental variables that leverage
            exogenous changes in trading partner conditions. Findings
            from the analysis indicate that imported capital and
            intermediates significantly predict economic transformation
            in Africa. However, the impact of imports from the Global
            North and the Global South varies depending on the specific
            channel of economic transformation. Imports from the Global
            South are more influential in driving structural change,
            while those from the Global North are more effective in
            facilitating productivity convergence. This divergence
            highlights the distinct roles that North-South and
            South-South trade play in Africa&#x2019;s economic
            transformation agenda. The findings underscore the
            importance of a nuanced trade policy that leverages the
            strengths of both regional and global trade partners to
            advance Africa's economic transformation.
display_title: Economic Transformation in Africa :
            The Role of North-South and South-South Trade
listing_relative_url: /research/2024/09/34386192/economic-transformation-africa-role-north-south-south-south-trade
topicv3: Trade and Growth m1326018 466,Innovation, Technology Transfer and
            Diffusion m1326019 1084,Structural Transformation for Growth
            m1326245 1089,Trade m1326924 698
publishtoextweb_dt: 2024-09-04T00:00:00Z
dois: 10.1596/1813-9450-10896
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 38
author_keys: 0: author_key: 1226802
1: author_key: 1996262
2: author_key: 1670645
model_extractions: Policy Research Working Paper 10896 Economic Transformation in Africa The Role of North-South and South-South Trade _Woubet Kassa Gideon Ndubuisi Solomon Owusu_ Africa Region Office of the Chief Economist September 2024
Policy Research Working Paper 10896

# **Abstract**

This paper contributes to the discussion on Africa’s pathways to economic transformation by examining the roles of trade patterns—specifically, South-South and NorthSouth trade—focusing on intermediate and capital goods sourced from both the Global North and the Global South. The paper relies on a panel dataset comprising 44 African countries from 2000 to 2022. To address endogeneity concerns, it uses a two-stage least squares method, employing instrumental variables that leverage exogenous changes in trading partner conditions. Findings from the analysis indicate that imported capital and intermediates significantly predict economic transformation in Africa. However, the impact of imports from the Global North and the Global South varies depending on the specific channel of economic transformation. Imports from the Global South are more influential in driving structural change, while those from the Global North are more effective in facilitating productivity convergence. This divergence highlights the distinct roles that North-South and South-South trade play in Africa’s economic transformation agenda. The findings underscore the importance of a nuanced trade policy that leverages the strengths of both regional and global trade partners to advance Africa’s economic transformation.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wkassa1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Economic Transformation in Africa: The Role of North-South and South-South Trade**

Woubet Kassa _World Bank, Washington DC, United States_ Gideon Ndubuisi _Delft University of Technology, the Netherlands_ Solomon Owusu _Frederick S. Pardee School of Global Studies, Boston University, United States_

**Keywords** : North-South Trade, South-South Trade, Economic Transformation, Structural Change, Productive Efficiency **JEL** : F14 F21 F43 F63
# **1. Introduction**

<mark>Achieving economic transformation remains high on the national economic agenda of African countries. Structural change and productivity convergence are key to attaining this feat, as they reduce disparities across countries, induce global competitiveness, and foster inclusive economic growth. Ultimately, identifying the drivers of structural change and productivity convergence is crucial for shaping effective policies and advancing Africa’s economic transformation agenda. This paper contributes to this debate by examining the role of trade patterns</mark> vis-à-vis South-South and North-South trad <mark>e. We distinguish between the impact of intermediate and capital goods African countries source from the Global North and Global South (hereafter referred to as North and South) and then examine how each affects structural change and productivity convergence.</mark> <mark>Conceptually, imported capital goods such as machinery, equipment and tools</mark> avails technologically lagging countries like those in Africa the opportunity to access (world-class) technologies to produce or innovate in ways that could not be possible under autarky. Furthermore, the ability to import intermediates confers a country <mark>access to more affordable, superior quality, and greater variety of inputs that could drive down marginal cost (</mark> Ndubuisi _et al.,_ 2020; Alessandria _et al.,_ 2023 <mark>). Overall, imported capital and intermediate goods can boost productive efficiency. At the same time, they can cause a compositional shift in the sectoral structure for at least two reasons. First, by making available inputs and technologies that were either unaffordable or inaccessible, they can cause a change in the country’s specialization pattern. Second, the associated cost reduction can induce the entry of new firms and/or incentivize existing firms to expand investments.</mark> Imported capital and intermediate goods would then unambiguously drive structural change and productivity convergence. Nonetheless, such gains might depend on the source of imports and the importing country’s characteristics. Classical technology gap and product cycle models assume that firms in the Global North typically possess cutting-edge (frontier) or superior technologies, while firms in the Global South are at best imitators (see Krugman, 1979, RiveraBatiz & Romer, 1991a; Grossman & Helpman, 1991). In this case, the ability of firms or countries in 2
the Global South to source capital and intermediate goods from the Global North confers on them a higher potential for accessing quality inputs and technology transfer given the preexisting technological gap between both regions. Because North-South trade (imports from the Global North to the Global South) is capital intensive as well as embodies a higher-quality and technology content that are complementary to the capital and technology needs of higher-productive sectors, it can induce structural change. <mark>In the same vein, it can hasten the</mark> catching-up process to the global productivity frontier because it makes available the requisite quality input and cutting-edge technology.

However, this structural change and catch-up benefits accruing to countries in the Global South from trading with the Global North is not automatic. Technologically backward countries first need to acquire absorptive capacity as a prerequisite to gain from imported capital and intermediate goods. This perspective underscores the importance of the absorptive capacity in identifying and assimilating new external knowledge and advanced technologies, particularly in contexts where the technological gap is larger between exporting and importing countries (Gerschenkron, 1962; Abramovitz, 1989; Cohen & Levinthal, 1989). Indeed, capacity and capability for technology adoption or input (re-)combination may not necessarily align, highlighting the nuances involved. In this case, South-South trade may be a better alternative due to relational proximity in technologies, endowment, and shared preferences and tastes (Lall, 2000; Acemoglu, 2007; Basile _et al.,_ 2011).<sup>1</sup> South-South trade (imports from the Global South to the Global South) may also embody cheaper inputs and older or more appropriate technologies compared to cutting-edge technologies from the North (Fu _et al.,_ 2011; <mark>Hanlin & Kaplinsky, 2016).</mark> However, it remains unclear whether such cheaper and supposedly appropriate technologies are sufficient to structurally transform the local economy and converge to the global best practices. Leapfrogging to become globally competitive requires access to frontier technologies which may be the real costs of South-South trade.

> 1 Basile et al. (2011, p.21) introduced the concept of relational proximity in knowledge spillovers and assimilation, which enables countries acquiring technologies to leverage new knowledge irrespective of their absorptive capacity. Relational proximity refers to the likeness between two areas in shared behavioral codes, cultural norms, and technological proficiencies. This proximity fosters cooperative learning mechanisms that drive the accumulation of knowledge.

3
Despite this divergent view on the implication of South-South and North-South imported capital and intermediates for developing countries, we know little about how they affect economic transformation (here operationalized as structural change and productivity convergence) in Africa. The current paper fills this knowledge gap and by extension contributes to the burgeoning discussion on pathways to Africa’s economic transformation agenda. Our analysis relies on a panel sample comprising 44 African countries for the period 2000–2022. We assemble country-level indicators of economic transformation (i.e., structural change and productivity convergence) and imported capital and intermediate. For the latter, we use detailed bilateral trade data to identify imported capital and intermediate goods as well as distinguish these imports by their origin vis-àvis the Global North and Global South. A major empirical challenge of our analysis is endogeneity, resulting among others from reverse causality and omitted variable bias. To address this concern, we employ 2-stage least squares and rely on an instrumental variable technique that exploits exogenous changes in trading partner conditions to identify the effects of imported capital and intermediates. Our approach draws insights from <mark>Frankel and Romer (1999) and</mark> Blanchard and Olney (2017).

Overall, our results show that imported capital and intermediate goods are strong predictors of economic transformation, with imports of capital and intermediate goods from both the Global North and the Global South being equally important in this regard. However, the contribution of imported capital and intermediate goods from the Global South or the Global North to economic transformation depends on the considered channel. Imported capital and intermediate goods from the Global South play a dominant role in promoting structural change. For productivity convergence to the global best practices, however, there is a reversal of this trend with imported capital and intermediates from the Global North playing a more dominant role. Ultimately, capital and intermediate imports from the Global South or Global North to Africa appear to serve different purposes on the economic transformation agenda of the continent. As attaining any meaningful economic transformation in African countries entails that countries within the continent simultaneously achieve structural change and productivity convergence, our result calls for policies that enable African countries to tap into both the Northern and Southern markets. This also does not fully align with the nascent rhetoric of regional integration being a magic wand that unlocks Africa’s long-awaited economic transformation, even in some cases at the expense 4
of trading with non-regional partners. Our findings call for a more nuanced approach that takes advantage of trade from both regional and non-regional trade markets.

The remainder of this paper proceeds as follows: Section 2 reviews the related literature and our contributions. Section 3 discusses the data sources, computation of variables, and estimation strategy. Section 4 presents the results, and section 5 concludes.

# **2. Related Literature and Contribution**

<mark>Modern trade theories (including Helpman and Krugman (1985), Romer (1986), and R</mark> ivera-Batiz and Romer (1991b)) <mark>highlight the dynamic gains from trade, which continually push countries' production possibility frontiers outward. Motivated by this,</mark> several studies have considered the impact of trade on various indicators of country performance, especially on income and productivity. A substantial amount of this literature have focused on the developing world, including countries in Africa given the region’s much-needed economic transformation. Several studies in this research strand have examined how trade openness or liberalization affect economic growth <mark>(Onafowora & Owoye, 1998; B</mark> rückner & Lederman, 2012; Chang & Mendy, 2012; <mark>Zahonogo, 2016)</mark> . One of the often-documented evidence in this literature is that trade openness causes growth, albeit this effect may well depend on country characteristics. An aspect of the trade literature has also examined whether the growth effect of trade is conditional on the direction of trade, with emerging evidence indicating that the direction or pattern of trade play an important role in this regard (Baliamoune-Lutz, 2011; Busse _et al.,_ 2016; Mullings & Mahabir, 2018).

For instance, He (2013) compared the impact of imports from China to those from the United States and France, on Sub-Saharan African manufactured exports. The findings revealed that Chinese imports have positive effects on manufacturing exports, with a stronger overall effect compared to imports from the United States and France. Amighini and Sanfilippo (2014) examined the impact of South-South and North-South FDI and imports on export upgrading in Africa. Among others, they found that importing from the South positively drives export upgrading. Ndubuisi _et al._ (2020) examined the differential effect of imported intermediate from developed and developing countries on the variety of exported products across African countries. Although they 5
found that intermediates sourced from both regions positively affect the variety of exported products from the continent, the effect of imported intermediates from developed countries depend on the industry's absorptive capacity.

Although the preceding studies provide important insights on the relationship they examine, attaining the much-needed economic transformation in Africa goes beyond merely economic growth or export performance. As noted in the introduction, <mark>achieving structural change and productivity convergence hold a greater promise in this regard. However,</mark> available evidence highlights large and persistent sectoral productivity gaps amid stalled structural change across African countries (Mensah _et al.,_ 2023). At the same time, aggregate and sectoral labor productivity levels across African countries disproportionately lag those of the world frontier (Harchaoui & Üngör, 2018; Calderon, 2022). Achieving any meaningful economic transformation in Africa will therefore hinge on the dual ability to achieve growth enhancing structural change and to close productivity gaps. While trade could play a pivotal role in this regard, the extant literature has proceeded without due consideration to this. The only exception is Kaba _et al._ (2022), which examined the effect of trade openness on structural change. Whereas they found that trade openness negatively impacts the long-run and the short-run dynamics of structural change, this negative impact goes through aggregate exports not aggregate imports. Further, decomposition of aggregate exports shows that the negative impact is driven by commodity exports, not manufacturing exports.

Our study is related to Kaba _et al._ (2022) but differs in three important ways. First, we focus on explaining structural change and productivity convergence, while they focus only on explaining structural change. Second, we focus on the role of imported capital and intermediate goods, while their interest was on the composition of exports and imports vis-à-vis whether it was a commodity or manufactured good. Third and most importantly, whereas Kaba _et al._ (2022) do not distinguish the origin of imports or exports, our focus is on how the effects of imported capital and intermediate goods could vary on the source vis-à-vis the Global North and the Global South. It suffices to note that previous studies that examined how the growth effect of trade in Africa is conditional on the trade pattern are also yet to consider the role of capital and intermediate goods, let alone on how they impact structural change and productivity convergence. Beyond 6
Kaba et al (2022), therefore, our study extends the broader literature on the development implications of trade in Africa by highlighting the role of imported capital and intermediate goods.

Conceptually, imported capital and intermediate goods would drive structural change and productivity convergence in several ways. <mark>Imported capital and intermediates e</mark> nhance the productivity of labor and capital in production processe <mark>s, thereby amplifying output per unit input for firms. This phenomenon not only fosters productivity convergence through productive efficiency in domestic production processes but also triggers structural change via a cost-oriented approach: all else being equal, the resultant reduction in unit costs elevates the competitive edge of domestic firms, spurring them to ramp up investment within and across sectors. Furthermore, the influx of imported capital and intermediates endows countries with technologies and vital components or raw materials, potentially unavailable domestically or more economically sourced abroad.</mark><sup>2</sup> <mark>This can fuel productivity convergence by stimulating innovation and the production of higher value-added goods across diverse sectors. It can also facilitate entry into new sectors or expansion of existing ones, propelling structural shifts towards activities with higher value addition.</mark> <mark>While the preceding discussion hints at a potentially positive impact of imported capital and intermediates on structural change and productivity convergence, this influence may hinge on the origin. For example, classical technology gap and product cycle models argue that firms in the Global North typically possess superior inputs and cutting-edge technologies, making them innovators, while firms in the Global South are imitators (</mark> Krugman, 1979, Rivera-Batiz & Romer, 1991a; Grossman & Helpman, 199 <mark>1). Consequently, countries in the Global South that source capital and intermediates from the Global North could benefit from access to higher-quality inputs and technology transfer, given the existing technological gap between the regions. However, this view has faced significant criticism in the structuralist literature on uneven development, citing the asymmetrical economic structures and specialization patterns</mark> ( <mark>Dahi & Demir, 2018)</mark> . As an

> 2 As noted by <mark>Ndubuisi et al. (2020), even where these resources are domestically available, it could be of lower quality or is available at a noncompetitive price relative to the foreign counterpart. Hence, access to foreign and domestic alternatives allows a firm a wider variety to choose from based on observed and unobserved cost differences (Ndubuisi et al., 2020 p5).</mark>

7
alternative, South-South exchange has been proposed as a better option due to relational proximity in technologies, endowment, and shared preferences and tastes that <mark>allows for increased complementarity, absorptive capacity, and more suitable technology adoption</mark> (Demir & Duan, 2018) <mark>. While such patterns of trade (South-South trade) may trigger growth enhancing structural change even with little or no development of local absorptive capacity, the potentially lower quality nature of such trade (imported capital and intermediates goods) may not be sufficient to propel countries in the global South benefit from such trade to converge to the productivity of the global best practices. Imports from the Global North to the Global South (North-South trade) in this regard may be more beneficial in closing the global productivity gap given their superior quality (i.e., imported capital and intermediate goods). However, the latter</mark> is not automatic and requires technologically backward countries, like those in the Global South, to first build the absorptive capacity to leverage imported capital and intermediate goods from the Global North to boost structural change and productivity convergence to the global best practices <mark>. Overall, the disparities in the origin of capital and intermediates—whether from the Global North or Global South—can yield varying effects on structural change and productivity convergence, particularly in terms of the significance or magnitude of the impact.</mark> The objective of our study is to empirically test this heterogeneity focusing on structural change and productivity convergence. Along this line, our study also contributes to a broader but an incipient and a thin literature on the determinant of structural change <mark>(McMillan</mark> _et al.,_ 2014; Owusu, 2021, 2024; Konte _et al._ 2022; <mark>Rohit, 2023</mark> ). It also contributes to the literature on productive efficiency which to date pays only a limited attention to the role of human capital, ICT, institutions, and market distortions ( <mark>Ayuso & Rodríguez, 2004; J</mark> ayasuriya & Wodon, 2005; Danquah & Ouattara, 2015; Ndubuisi _et al.,_ 2022; Ndubuisi & Owusu, 2023). Although trade indicators are often featured in these studies, the focus tends to be on trade openness and not patterns of trade. More recently, Demir and Duan (2018) distinguished various FDI pattern vis-àvis South-South, South-North, North-South and North-North and examined how the impact productivity level and convergence. Their study, however, neither focused on Africa nor on trade. Accordingly, we advance the literature on the trade and structural change and productive efficiency by considering the role of imported capital and intermediate, and this effect may be conditional on the origin vis-à-vis the Global south and the Global North.

8
# **3. Research Design**

# 3.1. Data

The key variables for our analysis include indicators of structural change, productivity convergence, and imported capital and intermediates. The first two variables are the outcome variables—which we employ as key components of economic transformation. Indicators of imported capital and intermediates, on the other hand, are the main explanatory variables. To minimize potential omitted variable bias, it is important that we control for other variables in the empirical model specification. Guided by a closely related literature on the drivers of structural change and productivity efficiency (see Danquah & Ouattara, 2015; Owusu, 2024; Ndubuisi _et al.,_ 2022; Ndubuisi & Owusu, 2023), our specified empirical model will control for human capital, institutional quality, inflation rate, natural resource, and infrastructure (mobile broadband and transport). We also control for initial industrial structure since this has strong implication on structural change and productivity growth. To operationalize this variable, we use each country’s initial agricultural value-added share. Table A1 in the appendix describes and lists the data sources of these variables.

Structural change involves a compositional shift in the sectoral structure of an economy. From a production standpoint, structural change can be assessed by examining the evolution of sectoral value-added shares or employment shares (Herrendorf _et al.,_ 2014). Due to limited manufacturing employment data for Africa, our empirical analysis relies on value added data. Both the employment and value-added data, whenever available are used interchangeably to measure structural change. Following Kaba et al. (2022), we measure structural change as the ratio of manufacturing value-added to agriculture value added. Higher values of the ratio indicate a compositional shift of an economy’s sectoral structure from agriculture to manufacturing. We adopt a similar measure in our study. The sectoral value-added data used to compute the structural change index is taken from the UNCTAD Statistical Database.

<mark>For the productivity convergence</mark> , we use a measure of labor productivity efficiency computed as a country’s labor productivity relative to the global labor productivity frontier. To compute the global labor productivity frontier, we consider all the countries in the world as contained in the Penn World Table 10.1. The global labor productivity frontier in a year is then defined as those 9
observations that lie at the 95<sup>th</sup> percentile of the labor productivity distribution. Therefore, the higher the value of the labor productivity efficiency, the closer the country’s labor productivity to the global labor productivity frontier implying that the country’s labor productivity is globally competitive. Finally, data on trade are sourced from the CEPII-BACI dataset. The dataset contains bilateral import and export values and quantities across many countries at the 6-digit harmonized system classification (HS). Using appropriate concordance table, we map the trade data into Broad Economic Classification (BEC) Revision 4 to identify imported goods that are classified as intermediate and capital goods. For each African country, we then aggregate the imported good type (i.e., capital and intermediate good) across the partner countries into two categories based on their origin. Consistent with our research objective, we consider two origins: the Global North and the Global South. Our definitions of the Global South and Global North strictly follow Demir and Razmi (2022). Table A1 reports the correlation matrix among the variables of interest in our analysis.

# _3.2 Model Specification_

To examine how structural transformation is affected by the patterns of trade in Africa, we estimate the following empirical model: Where, the subscripts , , and denote country, region, and year, respectively. The level of economic transformation i is independently captured by structural change ( i ) and ii rr tt productivity convergence ( Fi ), then i i , Fi } . is initial industrial structure, measured as SS ZZ a country’s value-added share in 2000. is the corresponding coefficient to be estimated. ii is SS ∈{ZZ ψψ an indicator measuring the pattern of trade, while is the corresponding coefficient. The trade ii,ii φφ TT patterns we are interested in are capital and intermediate goods African countries source from θθ either the global North or South. Because of the high correlation between the trade variables (see Table A2), in the estimation we introduce the variables independently. is a vector of time- ιι varying country characteristics and the respective vector of coefficients. As discussed in ii,ii WW 1 × ιι the previous section, the vector ιι includes time-varying country characteristics such as human θθ 1 × ιι ιι capital, inflation rate, natural resource rent, and Institutional quality. ii,ii is regional dummies to WW rr δδ 10
capture differences across the regions, , while is time dummies capturing time-specific shocks (see Gui-Diby, 2015). υ i is the error term. ii δδ

# _3.3 Estimation Strategy_

We first estimate Equation 1 with OLS. However, the OLS estimation may be subject to endogeneity bias due to omitted variables and simultaneity bias. One of the conventional ways to address these endogeneity concerns is to adopt a two-stage least square (2SLS) method, wherein the endogenous explanatory variable is corrected using an external instrument. The external instrument must be valid, meaning it should strongly explain the endogenous explanatory variable and remain uncorrelated with the error component of the model. Inspired by the extant literature (see <mark>Frankel & Romer, 1999; B</mark> lanchard & Olney, 2017), we explore instruments that satisfy the validity condition. We achieve this by exploiting plausible exogenous determinants of trade flows to predict a country’s patterns of trade flow which we then use as an instrument for the respective components of . ii,ii TT The approach we employed to construct the external instruments proceeds in three mutually inclusive steps. The first step entails estimating a bilateral trade flow using the gravity model. Available evidence suggests that bilateral trade is generally determined by economic, political, cultural, and geographic factors. As some of these factors are also correlated with structural change and productivity, we cannot consider all of them as this could create an endogeneity problem. Instead, we focus mostly on geographical factors as they are exogenously determined. Once we estimate the gravity model, we predict the trade flows and then aggregate them across the trading partners to arrive at a country-specific time-varying predicted trade flow. Next, we use these values as instruments in the 2SLS. Our identification assumption is that predicted trade value is uncontaminated by endogeneity concerns such that when used in 2SLS it allows us to make causal inferences about the effect of trade patterns on the outcome variables.

Equation 2 specifies the baseline gravity model that guides our investigation on the determinants of trade. The typical gravity model predicts bilateral trade flows as a function of the economic sizes (often using GDP) and distance (as a proxy for trade costs) between two units. Nonetheless, the empirical specification of gravity model varies from study to study. In the most part, these 11
variations are informed by methodological developments in the literature as well as the research objective of the study in question. Our goal is neither to validate nor vilify these studies, but to identify the variation in capital and intermediate goods import that is unrelated to conditions in the importing African country. Accordingly, we estimate a gravity model containing a battery of variables that capture intra- and inter-country trade costs.

i 0 1 ln (G i 2ln (G i ) + Z<sup>′</sup> Θ + υ i (2) ii ii ln (TT ) = αα + αα GG ) + αα GG From equation 2, i ) is the logarithm of the import value of country from country in year<sup>3</sup> ln (G i ) and ln (TTln (G ii i ) are the respective annual GDP of importing and exporting countries ii jj (expressed in logs), while 1 and 2 are their corresponding coefficients to be estimated. Z<sup>′</sup> is a tt. GG GG vector of trade costs and it includes the following variables: i) The logarithm of bilateral distance αα αα between country-pair; ii) An indicator variable capturing the presence of contiguous borders between country-pair; iii) An indicator variable capturing the existence of a common official language between country-pair; iv) An indicator variable capturing the presence of colonial ties between country-pair; v) Indicator variables capturing exporters and importers that are landlocked; vi) difference in latitude between country-pairs; and vii) logarithms of the GDPs of exporters and importers. 0 is the intercept, while υ i is a normally distributed error term. ii αα

> 3 Note that we estimate separate equations for capital and intermediate goods and then aggregate the predicted values across the different origins of import (i.e., Global North or Global South). In this way, the instruments employed in the 2SLS vary across imported good types and origins.

12
|Table 1: Construction|of instrumen|t usingbilat<br>Ln(Capital|eral trade d<br>good import|ata<br>)|Ln|(Intermedia|te good imp|ort)|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Log Distance|-1.1867***<br>(0.017)|-1.2305***<br>(0.016)|-1.4858***<br>(0.024)|-1.5030***<br>(0.024)|-1.5920***<br>(0.016)|-1.6146***<br>(0.016)|-1.6462***<br>(0.024)|-1.6544***<br>(0.024)|
|Contiguity|1.9650***<br>(0.053)|2.0612***<br>(0.053)|1.5633***<br>(0.054)|1.5727***<br>(0.054)|1.9132***<br>(0.055)|1.9270***<br>(0.055)|2.0983***<br>(0.054)|2.0980***<br>(0.054)|
|Colonial relationship|1.3788***<br>(0.044)|1.2143***<br>(0.043)|1.1400***<br>(0.045)|1.1367***<br>(0.045)|0.8476***<br>(0.045)|0.7612***<br>(0.045)|1.0355***<br>(0.046)|1.0346***<br>(0.046)|
|Common language|<br>0.7199***<br>(0.024)|<br>0.7514***<br>(0.024)|<br>0.4907***<br>(0.027)|<br>0.4911***<br>(0.027)|<br>0.5749***<br>(0.021)|<br>0.5932***<br>(0.022)|<br>0.4452***<br>(0.025)|<br>0.4462***<br>(0.025)|
|Common colonizer<br>Importer Landlocked<br>Exporter Landlocked|<br>0.2525***<br>(0.032)<br>-0.6465***<br>(0.020)<br>-0.2222***<br>(0.025)|<br>0.3582***<br>(0.031)<br>-0.7389***<br>(0.020)<br>-0.1514***<br>(0.027)|<br>0.5325***<br>(0.032)<br>-0.5555***<br>(0.133)<br>-5.3362***<br>(0.226)|<br>0.5414***<br>(0.032)<br>-0.3725***<br>(0.132)<br>-5.2321***<br>(0.225)|<br>0.6536***<br>(0.028)<br>-0.9164***<br>(0.019)<br>-0.5424***<br>(0.023)|<br>0.6916***<br>(0.028)<br>-0.9698***<br>(0.019)<br>-0.4750***<br>(0.025)|<br>0.6872***<br>(0.029)<br>-0.6068***<br>(0.119)<br>-4.1408***<br>(0.203)|<br>0.6927***<br>(0.029)<br>-0.4994***<br>(0.119)<br>-4.1138***<br>(0.202)|
|Difference Latitude<br>Log Exporter GDP|0.0109***<br>(0.000)<br>1.2446***<br>(0.005)|0.0096***<br>(0.000)<br>1.1869***<br>(0.006)|0.0223***<br>(0.004)<br>-0.0290<br>(0.062)|0.0187***<br>(0.004)<br>0.2934***<br>(0.068)|0.0018***<br>(0.000)<br>1.3277***<br>(0.004)|0.0010***<br>(0.000)<br>1.3079***<br>(0.005)|-0.0273***<br>(0.003)<br>0.2347***<br>(0.055)|-0.0294***<br>(0.003)<br>0.4876***<br>(0.072)|
|Log Importer GDP|<br>0.6653***<br>(0.006)|<br>0.6779***<br>(0.007)|<br>1.6816***<br>(0.071)|<br>2.8287***<br>(0.097)|<br>0.8374***<br>(0.005)|<br>0.8378***<br>(0.006)|<br>1.0058***<br>(0.063)|<br>1.9330***<br>(0.090)|
|Institution dissimilarity||<br>-1.2738***|<br>-0.0988|<br>-0.0858||<br>-0.5702***|<br>-0.0232|<br>-0.0175|
|||(0.036)|(0.069)|(0.069)||(0.030)|(0.058)|(0.058)|
|Preference dissimilarity||-0.2607***<br>(0.081)|-9.5283***<br>(0.393)|-9.4751***<br>(0.394)||-0.0416<br>(0.072)|-3.4929***<br>(0.341)|-3.4272***<br>(0.341)|
|Exporter remoteness||||1.1801***<br>(0.071)||||0.9440***<br>(0.067)|
|Importer remoteness||||<br>0.4476***<br>(0.046)||||<br>0.2918***<br>(0.058)|
|Constant|-5.8813***<br>(0.157)|-3.7441***<br>(0.172)|13.2826***<br>(1.013)|<br>-34.9883***<br>(2.752)|-3.1708***<br>(0.148)|-2.2487***<br>(0.161)|12.9569***<br>(0.911)|<br>-24.7828***<br>(2.902)|
|Observations|84,730|83,460|83,460|83,460|105,287|103,808|103,808|103,808|
|R-squared|<br>0.500|<br>0.517|<br>0.654|<br>0.655|<br>0.563|<br>0.565|<br>0.679|<br>0.680|
|<br>Year FE|<br>YES|<br>YES|<br>YES|<br>YES|<br>YES|<br>YES|<br>YES|YES|
|Export FE|NO|NO|YES|YES|NO|NO|YES|YES|
|<br>Importer FE|NO|NO|YES|YES|NO|NO|YES|YES|
|Robust standard errors in<br>***p<0.01,**p<0.05,*p|parentheses<br><0.10||||||||

13
Table 1 reports the results of the gravity model estimates. Columns 1-4 show the results for capital goods, while Columns 5-8 show the results for intermediate goods. Columns 1 and 5 report the results of a crude gravity model estimate that control for only time effect in addition to the gravity model covariates. The estimates on all covariates are statistically significant and meet _a priori_ expected sign. Size, differences in latitude, speaking the same official language, and sharing a common border and colonial ties are pull factors of international trade. Bilateral distance and landlock, on the other hand, are push factors. In columns 2 and 6, we introduce two additional variables to capture country-pair dissimilarity as per preference and institutional quality.<sup>4</sup> Estimates of both variables are negative and statistically significant at all conventional significance level, implying that country-pair differences in preference and institutional quality are also push factors of trade.

In columns 3 and 7, we include importer and exporter fixed effects to account for _intra_ -country trade costs as required from gravity theory. The inclusion of these fixed effects also approximate the multilateral resistance term (MRT) (see Anderson & van Wincoop, 2003; Baldwin & Taglioni, 2006; Yotov _et al.,_ 2016). The results reported in columns 3 and 7 are qualitatively similar to those of the previous once with two exceptions: the estimate of exporter GDP turns out statistically insignificant in column 3, while institutional similarity turn out statistically insignificant in column 3 and 7. One of the views shared in the literature is that importer and exporter fixed effects does not account properly for the MRT. We address this concern by including exporter and importer “remoteness indexes”, which is one of the approaches that have been adopted in the literature to explicitly account for MRT (see <mark>Silva & Tenreyro, 2006; Y</mark> otov _et al.,_ 2016). We compute the indexes as functions of bilateral distance and GDPs following Head (2003). Columns 4 and 8 report the results when we include the indexes. As expected, their estimated coefficients are positive and statistically significant, implying that remote countries from the rest of the world tend to trade more. In both columns, we also observe that the estimated coefficient of exporter and importer GDP turn out positive and significant, albeit institutional similarity remains statistically insignificant.

> 4 We compute the preference similarity as the ratio of importer per capita income to exporter per capita income. Institutional similarity is the ratio of importer institutional quality to exporter institutional quality.

14
Because of the relative sophistication of the gravity model estimates reported in columns 3-4 and 7-8, our 2SLS estimate will rely on predicted trade values from these models. Figure 1 plots the total imported capitals against the corresponding derived instrument from the gravity model. In the same vein, Figure 2 plots total imported intermediates against the corresponding derived instrument from the gravity model. As expected, Figures 1 and 2 both show a strong positive relationship. In addition, the figures also show substantial variations which we can explore for identification. In the appendix, we report Figures for the different imported goods categories based on origin (see Figures A1 and A2). The emerging evidence are similar to those reported in Figures 1 and 2.

15
**Figure 1:** Total capital good vs. total capital good IV

**Figure 2:** Total intermediate good vs. total intermediate good IV

**_Note_** : IV 1 is based on model 3 and 7 as reported in Table 1, while IV 2 is based on models 4 and 8

# **4. Empirical Results**

# _4.1. Structural Change: The Role of Capital and Intermediate Imports_

Table 2 reports the results on the effect of imported capital on structural change. Panel A shows the result for the effect of total capital import, while Panels B and C reports the effect of capital import from the Global North and Global South, respectively. Beginning with Panel A, the OLS estimated coefficient of total capital import is positive and statistically significant at the 1 percent significance level. The result holds with or without controls. However, the coefficient without the controls appears to have overestimated the effect of total capital import. The IV estimated 16
coefficient of total capital import as shown in columns 3 and 4 is also positive, albeit it is only statistically significant in column 3 where we used IV 1 (as defined in section 3) as the external instrument. Compared to the OLS estimate, the size of the IV estimated coefficients is lower implying that the former overestimates the effect of foreign capital on structural change. To check the appropriateness of the IV estimation, Panel A of Table A3 in the appendix reports the IV-estimation first stage results. As expected, the estimated coefficients of the respective two external instruments are positive and statistically significant at conventional significance level. The respective first-stage F-statistic are also considerably higher than the 10 rule of thumb, suggesting that the external instrument are both relevant and strong in explaining the variable of interest.

Moving on to Panels A and B in Table 2, the OLS estimate of capital import from the Global North are consistently positive but only statistically in column 1 where we do not include controls. Once we introduce controls in column 2, it turns statistically insignificant indicating that the previous evidence is driven by omitted variable bias. This latter result remains unchanged after addressing endogeneity issues as reported in columns 3 and 4 of Panel B. Conversely, the OLS estimated coefficient of capital import from the Global South remains positive and statistically significant at 1 percent significance level with or without controls (columns 1 and 2 of Panel C). The result also remains unchanged after addressing endogeneity issues although the significance level drops to 5 percent (columns 3 and 4 of panel C). Panels A and B of Table A3 in the appendix reports the first stage results of the IV estimation. In both cases, the coefficients of the external instruments all meet the _a priori_ expectation. The respective first-stage F-statistic are also considerably higher than the 10 rule of thumb. Overall, the IV result support the OLS estimate, with the result indicating that capital import from the Global South is a more robust predictor and contributor to structural change in Africa. This additional insight indicates that the positive, albeit weak significant structural change effect of imported capital we document in Panel A is driven by capital import from the Global North.

Next, Table 3 shows the result of intermediate import. The structure of the table is similar to Table 2 as described in the previous section. We further report the first stage result of the IV estimation in Table A4. Beginning with Panel A of Table 3, the OLS estimated coefficient of total intermediate 17
import turns out positive and statistically significant at 1 percent significance level (columns 1 and 2 of Panel A). Again, this result holds irrespective of whether we control for other country characteristics although the coefficient without the controls appears to have overestimated the effect of total intermediate import. Columns 3 and 4 reports the second stage result of the IV estimation. Consistent with the OLS estimate, the IV estimated coefficient of total intermediate import also turns out positive. However, unlike in the case of total capital import, the coefficient is statistically significant at all conventional significance level irrespective of the employed IV. Again, the first stage IV estimation result for total intermediate import as reported in Panel A of Table A4 in the appendix show that the coefficients of the external instruments all meet the _a priori_ expectation. The respective first-stage F-statistic are also considerably higher than the 10 rule of thumb.

Panels B and C of Table 3 report the results on the structural change effect intermediate imports from the Global North and Global South. The OLS estimate of intermediate import from the Global North turns out positive, although it is only statistically significant without the controls (column 2 of Panel B). Nonetheless, the IV estimate show a consistently positive and statistically significant coefficient irrespective of the employed IV (columns 3 and 4 of Panel B). Regarding the intermediate import from the Global South, the OLS estimate is positive and remains statistically significant at conventional significance level with or without additional controls. The result is further corroborated by the IV estimate. Hence, while intermediate import from the Global North and Global South significantly drives structural change, intermediate import from the Global South has a greater contribution on structural change as indicated by the size of the estimated coefficient.

18
**Table 2.** Structural change: The role of imported capital

||**OL**<br>|**S**<br>|**2SLS(seco**<br>|**nd stage)**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||||**Panel A**<br>||
|Total imported capital (log)|0.115***<br>|0.071***<br>|0.058*<br>|0.053<br>|
||(0.034)|(0.025)|(0.035)|(0.034)|
|constant|-0.524|-1.029**|-0.866|-0.809|
||(0.482)|(0.436)|(0.549)|(0.548)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.30|0.69|0.69|0.69|
||||**Panel B**||
|Imported capital from North (log)|0.097**|0.034|0.0541|0.052|
||(0.039)|(0.030)|(0.036)|(0.036)|
|constant|-0.286|-0.587|-0.8155|-0.790|
||(0.544)|(0.492)|(0.578)|(0.585)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.30|0.69|0.69|0.69|
||||**Panel C**||
|Imported capital from South (log)|0.078***|0.073***|0.075**|0.071**|
||(0.028)|(0.020)|(0.032)|(0.031)|
|constant|0.122|-0.920***|<br>-0.934**|-0.895*|
||(0.349)|(0.349)|(0.471)|(0.472)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.29|0.69|0.69|0.69|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10 **_Note_** : The controls include initial industry structure, rule of law, human capital, inflation rate, mobile broadband, transport infrastructure, total natural resource rent (% GDP), and bank credit to private sector (% GDP).

19
**Table 3.** Structural change: The role of imported intermediates

||**O**|**LS**|**2SLS(sec**|**ond stage)**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||||**Panel A**<br>||
|Total imported intermediate (log)|0.193***<br>(0.038)|0.139***<br>(0.038)|0.219***<br>(0.047)|0.221***<br>(0.046)|
|constant|-1.852***|-2.268***|-3.462***|-3.493***|
||(0.578)|(0.684)|(0.815)|(0.807)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.32|0.69|0.69|0.69|
||||**Panel B**||
|Imported intermediate from North (log)|0.129***|0.009|0.120***|0.121***|
||(0.042)|(0.039)|(0.042)|(0.042)|
|constant|-0.860|-0.315|-1.850**|-1.865**|
||(0.619)|(0.676)|(0.728)|(0.730)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.31|0.68|0.68|0.68|
||||**Panel C**||
|Imported intermediate from South (log)|0.159***|0.168***|0.207***|0.210***|
||(0.033)|(0.032)|(0.044)|(0.043)|
|constant|-1.196**|-2.611***|3.145***|-3.180***|
||(0.478)|(0.577)|(0.753)|(0.748)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.31|0.70|0.70|0.70|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10 **_Note_** : The controls include initial industry structure, rule of law, human capital, inflation rate, mobile broadband, transport infrastructure, total natural resource rent (% GDP), and bank credit to private sector (% GDP).

20
# _4.2. Productivity Convergence: The Role of Capital and Intermediate Imports_

Table 4 reports the result of the productivity convergence effect of capital and intermediate import. Panel A reports the results for total capital import. the OLS estimates as reported in columns 1 and 2 turn out positive and statistically significant at the 1% significance level, suggesting a positive association between imported capital and productivity convergence across countries in Africa. We report the second-stage IV estimation results in columns 3 and 4, while the first stage IV results are reported in Panel A of Table A5 in the appendix. For the latter, the coefficients of the external instruments all meet the _a priori_ expectation. The respective first-stage F-statistics are also considerably higher than the 10 rule of thumb. Concerning the second-stage IV estimation results, they are consistent with those of OLS in suggesting that foreign capital import is a significant predictor of productivity convergence across African countries. This result holds at all conventional significance level and is robust to the choice of external IV.

Panel B and C of Table 4 reports the results when we distinguish the productivity convergence effect of capital imports from the Global North and Global South. The OLS estimated coefficient of capital import from the North and South are both significantly positive at the 1 percent significance level, as reported in the respective columns 1 and 2 of Panels B and C. This indicates a productivity convergence premium across African countries that source capital from abroad. In columns 3 and 4 of Panel B and C, we report the results of the second-stage IV estimation. The variables enter the regression with a positive coefficient and are highly statistically significant. Panels B and C of Table A5 in the appendix reports the first-stage IV estimation. The results show that the IVs are relevant instrument. Hence, the IV results corroborate those of the OLS results with the results being qualitatively similar. Quantitatively however, the IV estimates, which is our preferred estimate, clearly shows that capital import from the Global North exerts a bigger impact on productivity convergence as indicated by the size of the estimated coefficient.

Next, Table 5 reports the results on the effect of intermediate import on productivity convergence. Panel A shows the results for the total intermediate import. In the OLS estimation, the variable enters the regression with a positive coefficient and is highly statistically significant (see columns 1 and 2). For the IV estimation, however, although the coefficient remains positive, 21
it loses its statistical significance in column 3 but becomes significant again in column 4 albeit weakly. Panel B and C of Table 5 shows the result of the productivity convergence effect of intermediate imports from the Global North and Global South, respectively. The OLS results suggest that intermediate import from the Global North and Global South exert a significant positive effect on productivity convergence, although the size and sign of the estimated coefficient of intermediate import from the Global North suggest that it is a more robust channel of influence (statistical significance) as well as have a greater contribution (magnitude) on productivity convergence. This result is collaborated by the IV estimate. The IV regression particularly shows that intermediate import from the Global north is a strong predictors and contributor of productivity convergence in Africa. The estimated coefficient is positive and statistically significant at 1 percent significance level. Conversely, although the IV estimated coefficient of imported intermediate from the Global South remains positive, it loses its statistical significance in column 3 but becomes significant again in column 4 albeit weakly at 10 percent. The size of the estimated coefficient also remains small relative to that of intermediate import from the Global North.

Overall, the study underscores the distinct roles of imported capital and intermediate goods from different regions in driving economic transformation – structural change and productivity convergence in Africa. We show that imports from the Global South play a dominant role in promoting structural change in Africa. This trade fosters growth and transformation even with limited local absorptive capacity. For productivity convergence to global best practices, imports from the Global North are more influential. The superior quality of these imports helps close the productivity gap, but African countries need to build absorptive capacity to fully benefit from them. <mark>While South-South trade may trigger growth enhancing structural change even with little or no development of local absorptive capacity, the potentially low-quality nature of such trade may not be sufficient to propel countries in the Global South to benefit from such trade to converge to the productivity of the global best practices. North-South trade is more beneficial in closing the global productivity gap. T</mark> he superior quality of these imports helps close the productivity gap, but African countries need to build absorptive capacity to fully benefit from them. Our findings challenge the idea that regional integration alone can drive Africa’s economic transformation, even in some cases at the expense of trading with non-regional partners. Instead, 22
it calls for a balanced approach that leverages both regional (South-South) and global (NorthSouth) trade markets for optimal economic transformation in Africa.

|**Table4.**Produc|tivityfrontier:<br>OL<br>|The role of im<br>S<br>|ported capital<br>2SLS(sec<br>|ond stage)<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||||**Panel A**||
|Total imported capital (log)<br>constant|0.193***<br>(0.017)<br>-4.382***<br>(0.258)|0.091***<br>(0.011)<br>-3.670***<br>(0.173)|0.082***<br>(0.016)<br>-3.788***<br>(0.206)|0.084***<br>(0.015)<br>-3.809***<br>(0.197)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.31|0.84|0.84|0.84|
||||**Panel B**||
|Imported capital from North (log)|0.170***<br>(0.017)|0.061***<br>(0.014)|0.0871***<br>(0.014)|0.087***<br>(0.014)|
|constant|-4.075***<br>(0.255)|-3.310***<br>(0.201)|-3.8285***<br>(0.192)|-3.830***<br>(0.193)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.30|0.84|0.84|0.84|
||||**Panel C**||
|Imported Capital from South (log)|0.151***<br>(0.018)|0.092***<br>(0.010)|0.066***<br>(0.015)|0.069***<br>(0.014)|
|constant|-3.522***|-3.521***|-3.489***|-3.514***|
||(0.236)|(0.147)|(0.175)|(0.169)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.30|0.84|0.84|0.84|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10 **_Note_** : The controls include initial industry structure, rule of law, human capital, inflation rate, mobile broadband, transport infrastructure, total natural resource rent (% GDP), and bank credit to private sector (% GDP).

23
|**Table5.**Productivityfron|tier: The role o<br>**O**<br>|f imported inte<br>**LS**<br>|rmediates<br>**2SLS(sec**<br>|**ond stage)**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||***|**Pan**<br>***|**el A**<br>|*|
|Imported Intermediate (log)|0.215<br>|0.046<br>|0.034<br>|0.037<br>|
||(0.017)|(0.017)|(0.021)|(0.020)|
|constant|-4.986***<br>(0.283)|-3.295***<br>(0.283)|-3.351***<br>(0.325)|-3.398***<br>(0.313)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.31|0.83|0.83|0.83|
|||**Pan**|**el B**||
|Imported Intermediate from North (log)|0.195***<br>(0.015)|0.045***<br>(0.017)|0.045***<br>(0.016)|0.046***<br>(0.016)|
|constant|-4.613***|-3.220***|-3.462***|-3.477***|
||(0.244)|(0.260)|(0.240)|(0.238)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.31|0.83|0.83|0.83|
|||**Pan**|**el C**||
|Imported Intermediate from South (log)|0.155***|0.039**|0.032|0.035*|
||(0.020)|(0.015)|(0.020)|(0.019)|
|constant|-3.928***|-3.157***|-3.303***|-3.347***|
||(0.303)|(0.253)|(0.298)|(0.288)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.28|0.83|0.83|0.83|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10

**_Note_** : The controls include initial industry structure, rule of law, human capital, inflation rate, mobile broadband, transport infrastructure, total natural resource rent (% GDP), and bank credit to private sector (% GDP).

# **5. Conclusion**

24
Achieving economic transformation remains a high priority on the national economic agenda of African countries, with structural change and productivity convergence being pivotal components. This paper examined how imported capital and intermediate goods from the Global South and Global North drive economic transformation in Africa. The analysis relies on a panel dataset comprising 44 African countries from 2000 to 2022. Overall, we find that imported capital and intermediate goods are crucial to economic transformation of African countries. However, the contribution of imported capital and intermediates from the Global South or Global North varies with the specific channel of economic transformation. Imported capital and intermediates from the Global South are more influential in achieving structural change. Conversely, imported capital and intermediates from the Global North play a dominant role in achieving productivity convergence. These findings highlight the importance of leveraging both regional and global trade to achieve comprehensive economic transformation in Africa.

Our findings have significant policy implications. Firstly, African countries should focus on building absorptive capacities to maximize the benefits of advanced technologies and high-quality inputs from the Global North. This involves investing in education, infrastructure, and institutional frameworks that enhance the ability to assimilate and utilize new technologies. Secondly, policies should also promote South-South trade by improving regional integration and reducing trade barriers within the Global South. This can provide access to more appropriate technologies and inputs that are cost-effective and well-suited to local conditions. Finally, a nuanced trade policy that strategically leverages both North-South and South-South trade can help African countries achieve the dual goals of structural change and productivity convergence, leading to a more inclusive and sustainable economic transformation.

25
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