Introduction

Infrastructure development has been an essential part of China’s economic success. The government created one of its most successful policy slogans in the early 1980s, ‘to become rich, one must build roads first’; an idea that has become deeply rooted in the hearts of the people (Anderlini 2018). The slogan remains relevant when one tries to make sense of China’s development process. The total length of public roads in China increased from 1.2 million km in 1991 to 5.2 million km by 2020. Similarly, during this period, China’s registered carrier departures at airports increased by 26 times, from 0.19 million to nearly 5 million.Footnote 1 Given its success in infrastructure development and poverty alleviation, China has become increasingly interested in promoting its development model/strategies in other developing countries, which will conveniently help expand its global influence.

This article aims to quantitatively evaluate the impacts of China’s infrastructure projects, with a particular focus on the process of structural transformation in African nations. The importance of structural transformation as a fundamental driver of economic development has long been discussed by economists, such as Zhang (1949) and Lewis (1954). The development process is normally represented by the rising Gross Domestic Product (GDP) shares of the industrial sectors within the economy. At the same time, the GDP share of the agricultural sector and the corresponding employment experience a decline. This is particularly true for most Sub-Saharan African (SSA) countries, as the agricultural sector contributes a significant share of their GDP and most of their labour forces are also concentrated in the sector. This study utilitses the longitudinal data from various sources such as the Economic Transformation Database (ETD) jointly developed by the Groningen Growth and Development Centre (GGDC) and the World Institute for Development Economics Research (WIDER) within the United Nations University (UNU)Footnote 2 and SAIS-CARI database for the Chinese contract data.

Over the past decade, China has actively promoted its development model overseas, centred on extensive infrastructure development, through the Belt and Road Initiative (BRI). Introduced by President Xi Jinping during his 2013 state visit to Kazakhstan, the initiative seeks to capitalize on China's decades-long expertise in infrastructure development. It aims to enhance economic connectivity across Eurasian landmasses and to expand maritime networks in African countries via the Maritime Silk Road. Since then, it has become a hallmark of China’s foreign policy agenda, and it was even included in the Constitution of China in 2017.

Infrastructure plays an essential role in the economy, facilitating connectivity among economic activities, enables economies of scale and is the foundation for structural transformation and economic growth. According to Collier (2008), the economic development of a country has positive spillover effects on its neighbouring countries. However, due to the lack of critical infrastructures in low-income countries (LICs), especially in landlocked countries in Africa, such positive spillover effects are extremely limited. To eliminate such development bottlenecks, developing countries, especially the low-income ones, should prioritise investment in infrastructure within their development strategies.

However, the BRI has encountered significant controversy in the Western media and in political debates. Some argue that China is actively pursuing debt-trap diplomacy as a way to seize natural resources and political support from the African continent (Chellaney 2017). Some even view China’s presence in Africa as a new form of colonialism. Brautigam (2020) argues that such views, dominated by the negativity bias, can be quite misleading. Jones and Hameiri (2020) add that the so-called debt trap merely reflects the lack of due diligence on the part of the recipient countries, as these projects are typically initiated by the recipient governments in pursuit of their own domestic agendas.

So far, most studies on the impacts of China’s infrastructure projects in Africa and the BRI have relied on qualitative analyses,Footnote 3 such as through case studies. Rigorous quantitative evidence remains scarce in the literature. Therefore, one aim of this paper is to offer comprehensive quantitative evidence on the topic. Our findings suggest that, with a particular focus on infrastructure development, China’s BRI projects could positively impact industrialisation and other non-agricultural sectors, highlighting the significance of local governance quality in amplifying these effects. Given that infrastructure projects are long-term developments and it takes time for structural transformation to take place, this paper also highlights the importance of signalling effects of the BRI that may induce more investments from other investors, which indirectly contribute to industrialisation in African countries. The study challenges prevailing narratives about BRI's ineffectiveness in local job creation and suggests potential policy measures focusing on agricultural productivity, infrastructure quality, and human capital development. This analysis contributes to a deeper understanding of the complex economic dynamics between China and Africa under the BRI framework.

The next section conducts a literature review regarding structural transformation and infrastructure development to identify feasible hypotheses for this empirical analysis. The third section identifies reliable data sources in the light of the theoretical hypotheses formed in the previous section. The next section presents the main empirical results from the fixed-effects panel and staggered difference-in-difference analysis. Contrary to the common claims in the media, our results show that China’s infrastructure projects not only have positive impacts on the industrialisation process in Africa but also creates local employment.

Literature Review

Structural Transformation

Lewis's dual economy model (1954) serves as the theoretical foundation for this analysis. His seminal work highlights the importance of the interactions between the industrial and agricultural sectors during the economic development process, for which the neoclassical economics theory fails to provide a useful framework.

Lewis’s simple model of structural change has two sectors within a closed economy. One sector engages in industrial production while the other sector represents the subsistence agricultural sector. The model is particularly pertinent to most Sub-Saharan African (SSA) economies, where over 60% of the population are smallholder farmers, yet they collectively contribute only 23% of the continent's GDP (Goedde and OOko-Ombaka 2019).

The factors of production differ between the two sectors with the industrial sector using labour and capital and the agricultural sector relying on labour and land. The subsistence agricultural sector serves as a reservoir of cheap labour, which keeps the labour wage at a low level within the domestic economy. It only requires the industrial sector to offer a very modest wage premium to attract workers to migrate from the agricultural sector. In addition to labour, capitalists also have their place in the model. While the workers are not able to save any surplus, the capitalists invest all their industrial profits to support the growth of the more productive industrial sector as well as the overall economy.

Ranis and Fei (1961) elaborated upon Lewis’s model, conducting a formal examination of the assumptions pertaining to an unlimited supply of labour. Vines and Zeitlin (2008) offer a comprehensive review of their effort and theoretical accounts for the dual economy. They argue that economic growth and the relative price between agricultural goods and industrial goods should be jointly determined within Lewis’s model. During the transition process, the increased profits and savings resulting from the reallocation of resources between the subsistence and industrial sectors increases the demand for industrial goods and for labour in the industrial sector. However, the relative price between agricultural goods and industrial goods may change as more and more labour leaves the agricultural sector. An increase in the relative price of agricultural goods in terms of industrial goods raises the labour wage, measured using industrial goods, within the industrial sector. The manufacturer, therefore, prefers less labour-intensive technologies and industrial production becomes increasingly capital intensive. As a result, economic growth eventually slows since the industrial sector is no longer able to generate as much profit as when there is an unlimited supply of cheap labour.

Diao and McMillan (2018) contend that Lewis’s model continues to be useful in comprehending the recent trajectory of economic growth in Africa. However, instead of the dual economy setup, they propose to follow Lewis (1979) by introducing a third sector—an in-between sector. This captures the small and medium-sized enterprises that mainly operates within the informal sectors. These enterprises, although covering a wide range of sectors such as manufacturing, transportation, construction and services, are usually not substantially more productive than the traditional agriculture sector. The rise of the in-between sectors in Africa seems to differ significantly from the growth story of many Asian economies, which has a strong focus on the exports market and formal sectors.

Nedoncelle and Wolfersberger (2023) identify the interconnections between China's rise as a global supplier of manufacturing goods since joining the WTO in 2001 and the structural transformation dynamics in Sub-Saharan African (SSA) nations. Their study suggests that although the 'China shock' has led to a decline in the industrial sector and a redirection towards agriculture in many developing countries, this trend was not observed in SSA, where this impact seemingly catalyses growth within the service sector.

Infrastructure Development

According to Lin (2011), economic development is a dynamic process, which requires the improvement of corresponding infrastructures at different stages. Infrastructure development, together with advances in technology, facilitates the reallocation of resources from inefficient subsistence sectors to more efficient productive sectors. As a result, transaction costs are lowered, and returns on capital investment increase. Given the enormous positive externalities associated with infrastructure investment, in theory, it should be financed entirely and provided by the public sector. However, such financing resources are extremely limited in LICs and LMICs. These countries are constrained by challenges in both domestic revenue mobilisation due to the large informal sector and in raising funds from the international capital market due to the already alarming debt-to-GDP ratio.

In 1953, Lewis's report on industrialisation for the Government of Gold CoastFootnote 4 identified the lack of quality physical infrastructure in African nations, coupled with low agricultural productivity, as the foremost policy priorities (Weiss 2018). However, nearly seventy years after that report, there are still 600 million people living in SSA lacking access to electricity. The electricity demand is likely to quadruple between 2010 and 2040 (Lakmeeharan et al. 2020). Similar forecasts will apply to other infrastructures such as railways and roads since these countries are falling far behind the BRIC (Brazil, Russia, India and China) countries. Lakemeeharan et al. (2020) argue that the current infrastructure investment as a share of GDP, around 3.5% since 2000, is too low to close the infrastructure gap. An additional 1% GDP share might be needed. It implies that the infrastructure investments need to be doubled between 2015 and 2025 and reach US$150 billion by 2025. Given the tremendous infrastructure financing gap, the BRI projects in Africa could contribute to the much-needed financial resources and technical know-how.

In addition, using a Computable General Equilibrium (CGE) model, Diao and McMillan (2018) identified some adverse effects associated with infrastructure projects that are financed predominantly by foreign capital inflows. Large capital inflows would lead to a real exchange rate appreciation of the local currency, and ultimately stunt the growth of exporting sectors. Therefore, the net benefits of infrastructure projects are likely to be ambiguous and dependent upon financing models.

Brautigam (2019) argues that Chinese loans have played a pivotal role in Africa's structural transformation, predominantly by financing extensive infrastructure and industrial development projects since the 1960s. These loans, primarily from institutions like the China Export–Import Bank and China Development Bank, have facilitated the construction of roads, railways, power plants, and development in sectors such as agriculture and manufacturing. While these projects have significantly boosted Africa's infrastructure and productive capacity, leading to notable economic changes, they also raise concerns about debt sustainability and long-term economic independence. The impact of these loans on the African economy highlights the balance between transformative development and the potential risks associated with increased dependency on external financing.

However, financing is only half of the story, to ensure the success of the infrastructure projects, it also depends on the quality of governance at the local level and whether the recipient governments select the projects based on reliable feasibility studies. As pointed out by Jones and Hameiri (2020), most of these BRI projects are initiated by the local governments. It is, therefore, the responsibility of the recipient governments, rather than the financing body, to ensure the success of the proposed projects. That said, the success of these projects also depends on close collaboration with the Chinese government and oversight by credible third parties to ensure a mutually beneficial outcome. It is not in China’s interests to have its BRI projects further tied to ‘debt-trap diplomacy’ and ‘neo-colonialism’.

The Impact of the BRI

Gu and Carey (2019) highlight China's significant role in African infrastructure development over the past two decades. Through its policy banks like the China Development Bank and China Exim Bank, China has become a major player in global development finance, challenging established multilateral institutions with its substantial loan contributions. Central to this has been the Belt and Road Initiative (BRI) and the Forum for China–Africa Cooperation (FOCAC), which have fostered new cooperation platforms and substantial investments in African infrastructure.

Since the BRI was first announced in 2013, 42 countries in Sub-Saharan Africa have signed a Memorandum of Understanding with China to join the initiative. Most of the SSA economies are currently categorised as either LICs or lower-middle-income countries (LMICs).

Without a comprehensive plan/strategy developed between the Chinees government and the recipient countries, it is unclear how the BRI-related projects are different from the previous Chinese infrastructure projects in the continent. Lu (2019) argues that the BRI projects are a continuation of China’s previous financing model. Moreover, the impacts of infrastructure projects on structural change are long-term in nature, so it is too early to identify the impacts of BRI-related projects at this stage. Therefore, examining the impacts of China’s previous infrastructure projects in the region will provide some useful insights for the BRI projects.

Given the scale of the BRI, bilateral arrangements alone are not sufficient to meet the financing requirements of all the infrastructure projects envisaged (Dossani et al. 2020). At the same time, China's approach, characterised by public entrepreneurship and practical implementation, diverges from Western standards in areas like environmental, social and governance. Therefore, ensuring the sustainability and long-term suncess of these projects also requires the development of shared platforms and cooperative efforts between China and other global actors, such as the African Development Bank and the World Bank. The launch of the BRI and the active participation of member countries in the region send a positive signal to the private sector, which has thus far been reluctant to invest in the region (Shao 2020). Therefore, in addition to the direct benefits of infrastructure projects, the BRI may positively impact the structural transformation process in Africa through the signalling effect.

Cheru and Oqubay (2019) present a compelling case study illustrating how Ethiopia strategically utilised its partnership with China for industrailisation. It acknowledges the benefits and challenges in these relations, including economic policy differences and trade imbalances. The success of this partnership requires the Ethiopian government to have a long-term development vision and foster an environment conducive to Chinese investments, especially in infrastructure. Ethiopia's strategy encompasses diversified trade, significant Chinese investment in manufacturing, and substantial infrastructure projects. Despite the imbalances in trade, Ethiopia was able to learn from China's development model and tailor it to local circumstances to enhance its infrastructure and industrial capacity. Oqubay and Lin (2019) also underscore the importance of Africa's proactive policy and ownership in future strategic engagement with China to maximise economic benefits by focusing on productive investments, particularly in infrastructure and human capital, and prioritizing manufacturing for economic development. These lessons from Ethiopia thus can be extrapoliated to the broader context of Africa's economic transformation and the role of BRI projects.

Based on the literature review, the following hypotheses are proposed for thorough examinations in the empirical section.

Hypothesis 1 (H1): Belt and Road Initiative (BRI) infrastructure projects would have a positive impact on the industrialisation process within African countries.

Hypothesis 2 (H2): A higher level of regulatory quality within African countries would enhance the positive impact of Chinese infrastructure projects on industrialisation.

Data and Descriptive Statistics

Data and Sample

The data regarding Chinese contracts in Africa used in this study were obtained from the Chinese Revenue Contract Data within the SAIS-CARI databaseFootnote 5 and country-level information from the Penn World Table (PWT) version 10.0 (Feenstra et al. 2015) and the World Development Indicator (WDI) database. PWT provides cross-country data on economic development, employment, human capital, etc. The governance indicator for each country is obtained from the Worldwide Governance Indicators (WGI) database.

The research sample comprises data from African countries from 1998, coinciding with the commencement of Chinese Revenue Contract Data, to 2019. Observations with missing Contract information were excluded, resulting in a sample of 1097 observations, covering 53 countries over 22 years. Additionally, we followed previous studies and winsorised all continuous variables at a 1% level to mitigate the impact of outliersFootnote 6 (Jiang et al. 2020; Li et al. 2021).

Furthermore, Chinese loan data were collected by SAIS-CARI researchers through various publicly available sources, such as government documents and contractor websites, which were carefully verified and cleaned. According to Brautigam and Hwang (2016), while the alternative data source, China.AidData.org, is useful for identifying new projects, the lack of project verification means that it should be used with caution. Therefore, the SAIS-CARI database was selected as the preferred data source for Chinese loan data. Additionally, employment data from GGDC/UNU-WIDER ETD were included in our analysis. It provides comprehensive, long-term, and internationally comparable sectoral data on employment and productivity in Africa, Asia, and Latin America.

Similarly, we also use the FDI data from SAIS-CARI, which has combined the overseas FDI figures from the China Statistical Yearbooks, the Statistical Bulletins of China's Outward Foreign Direct Investment published by China's MOFCOM.

Measurement for Economic Structural Transformation

The industrialisation process can significantly impact economic growth in developing countries (Szirmai 2012; Haraguchi et al. 2019). This paper follows Szirmai (2012) and Wonyra (2018) in using manufacturing value added (as a percentage of GDP) as the proxy for structural transformation (Indus). Another proxy for structural transformation, following Wonyra (2018), is the ratio of non-agricultural industry in GDP (Non-agricultural).

Measurement for Governance Quality

Governance quality is measured using the regulatory quality index (Regulatory), which reflects perceptions of the capacity of local governments to enact and conduct reasonable policies and regulations that allow and facilitate the development of the private sector. This index is collected from the WGI database and widely utilised in previous literature (Liu et al. 2021; Martínez-Zarzoso and Márquez-Ramos 2019; Fredström et al. 2021). Additionally, we also employ the percentile rank of the regulatory quality index (Regulatory2) as a robust check.

Measurement for Unemployment

Most western media depicted a narrative that most Chinese projects failed to create local employment in African countries. Dossani et al. (2020), however, reject such a view, as many field studies in various African countries have shown local workers account for 85–90% of the labour force when it comes to projects financed by China. To examine the impact on employment, this paper uses the unemployment rate (the ratio of unemployment to the total labor force) as a measure of unemployment, addressing the debate around the impact of BRI on local employment. Additionally, the paper investigates the impact of China’s construction projects on total employment, using two other proxies for employment: Emp and Emp2. Both are the natural logarithm of total employment, acquired from WDI and GGDC/UNU-WIDER databases, respectively.

Chinese Infrastructure Projects in Africa

Following previous studies (Ehizuelen 2017; Eom et al. 2017), this paper uses the gross annual revenues of Chinese companies’ construction projects in Africa (Contract) from the SAIS China-Africa data to measure China’s infrastructure projects in Africa. Data for both FDI from China and Chinese loans in African countries are included as alternative measurements for China's investment.

In addition, endogeneity may arise when considering investments from China. It is likely that China invests in countries with a higher level of industrialization and employment. BRI provides an opportunity to address this endogenous concern. This paper follows previous studies (Luo et al. 2021; Shao 2020; Mao et al. 2019; Yu et al. 2019) in conducting a quasi-natural experiment, setting countries with Memoranda of Understanding (MoU) with China as the treated group. In other words, BRI countries have official agreements with China.Footnote 7

Control Variables

This paper follows most previous studies on industrialisation (Haraguchi et al. 2019) in its choice of control variables: (1) Gross domestic product per capita (PerGDP), measuring cross-country differences in economic development. The existing literature shows that wealthier countries predominantly rely more on manufacturing industries (Rodrik 2013; Haraguchi et al. 2019). (2) Real effective exchange rate (REER), having a crucial effect on economic development (Martorano and Sanfilippo 2015). (3) Human capital (HC), which, in endogenous growth models, enables a country to adapt to new technologies, be more innovative, and facilitate economic growth and transformation (Romer 1986). This paper uses the human capital index constructed by Barro and Lee (2013) as a proxy for human capital, measuring the average number of years of education of employees. (4) Capital openness (KAOpen), referring to the capital account openness index constructed by Chinn and Ito (2006). According to the Solow growth model, capital openness reduces a firm’s financing costs and substantially affects a country’s industrialisation (Chari et al. 2012). (5) Rents, referring to the ratio of mineral rents to GDP, measures the impact of natural resources. Natural resources reshape a country’s income but negatively affect a country’s long-term development (Rodriguez and Sachs 1999) and manufacturing sector (Sachs and Warner 2001). Thus, this paper follows Haraguchi et al. (2019) in using the mineral rents ratio to GDP to control the impact of natural resources. (6) Credit, which is the domestic credit allocated to the private sector (as a percentage of GDP) and measures the impact of financial development. Based on the Schumpeter theory, financial institutions and systems promote firms’ investments, especially in the manufacturing sector (Rajan and Zingales 1998), thereby facilitating economic development.

Most variables were nominal and have been adjusted with 2015 as the base year, with the unit of measure in million USD. They are normalised using natural logarithm in the model. Other variables are either in percentage of GDP (e.g. Indus) or constructed index. In addition, this paper includes the country fixed effect and year fixed effect to control for the impact of unobservable time-invariant country characteristics and time-specific factors that are invariant with the country.

Empirical Model

We use Eq. 1 to investigate the relationship between BRI projects and the economic structural transformation in Africa:

$${Indus}_{i,t+1}={\upbeta }_{0}+{\upbeta }_{1}{Contract}_{i,t}+\sum Controls+{\delta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(1)

We take a lead for the dependent variable since it usually takes time for the BRI projects to exert any impacts on the Industrialisation process. \(\sum Controls\) represents the control variables, including PerGDP, REER, HC and so on. \({\delta }_{i}\) represents the country fixed effect and \({\theta }_{t}\) denotes the time-fixed effects. A positive and significant \({\upbeta }_{1}\) indicates that the BRI projects could promote industrialisation within African countries.

Our second model examines the impact of regulatory quality on the relationship between Chinese projects and industrialisation. A positive and statistically significant estimate of \({\beta }_{3}\) means that higher regulatory quality enables Chinese projects to exert a stronger positive effect on the industrialisation process.

$${Indus}_{i,t+1}={\upbeta }_{0}+{\upbeta }_{1}{Contract}_{i,t}+{\upbeta }_{2}{Regulatory}_{i,t}+{\upbeta }_{3}{Contract\times Regulatory}_{i,t}+\sum Controls+{\delta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(2)

To examine the impact of Chinese projects on both unemployment (\({Unemp}_{i,t+1}\)) and employment (\({Emp}_{i,t+1}\)) dynamics, we developed another model, represented by Eq. 3 (Table 1):

Table 1 Variable definition
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$${Unemp}_{i,t+1}={\upbeta }_{0}+{\upbeta }_{1}{Contract}_{i,t}+\sum Controls+{\delta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(3)

Summary Statistics

Table 2 presents the summary statistics for all the variables used in this paper. The mean of Indus is 12.19, indicating that, on average, the manufacturing industries of the sample countries represent 12.19% of the local GDP.

Table 2 Description statistics
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Table 3 presents the Pearson correlation matrix among the primary variables in Eq. 1. The highest correlation coefficient between Contract and the control variables is 0.747, observed between HC and PerGDP, raising concerns about multicollinearity. To address this issue, we conducted a Variance Inflation Factor (VIF) analysis. The largest VIF value is 2.94, with a mean value of 1.70. These values are all below 5, indicating that our results are not affected by multicollinearity problems (Jiang et al. 2020).

Table 3 Pearson correlation matrix
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Empirical Results

Baseline Result

Table 4 presents the baseline results. In Col. (1), the control variables are not included, while they are added in Col. (2). To address the impact of heteroskedasticity, heteroskedasticity-robust standard errors were employed, and the T statistics are reported in parentheses.

Table 4 Baseline result
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The coefficient of Contract is positive and significant in all columns, indicating that China's investments significantly promote the industrialisation of African countries. Specifically, a one-standard-deviation (SD) increase in China's projects leads to approximately an 8.79% improvement in industrialisation relative to the average level (calculated as 0.299 * 2.163 / 7.359). This result confirms the positive and economically significant effect of China’s projects on the industrialisation of African countries, supporting the central hypothesis of this paper (H1).

Interestingly, the coefficient of PerGDP is negative and significant, showing that high-PerGDP countries tend to have a lower GDP share of manufacturing. This result is in line with Timmer et al. (2015) and Haraguchi et al. (2019). A possible explanation is that African countries with relatively high GDP per capita are more inclined toward the development of the service industry, which does not align with the definition of industrialisation here.

Robustness Check

In our baseline regression, we employ the natural logarithm of Chinese contract revenue as the proxy for China’s infrastructure projects. However, this proxy did not consider the size of the recipient economies. Hence, we use the ratio of Chinese contract revenue to the local country GDP as the alternative measure and show the regression results in Table 5. The coefficient of Contract2 is positive and statistically significant, indicating a positive effect of Chinese projects on African industrialization. Specifically, a one-standard-deviation (SD) increase in Contract2 leads to approximately a 2.3% increase relative to the average level in the GDP share of the manufacturing sector (calculated as 11.858*0.0238/12.19). This result further confirms the robustness of our findings.

Table 5 Alternative measure for the Chinese infrastructure projects
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Furthermore, similar to most empirical studies on this topic, our study is also constrained by data availability and data quality. Therefore, we consider loans and foreign direct investment from China as alternative measures for Chinese projects. We collect data on China's Foreign Direct Investment (CFDI) and Loans (CLoan) to examine their impact on African industrialisation. These measures share some overlapping aspects with the contract data, and our goal is to test whether the overall relationship remains positive under a more nuanced measure.

The coefficients of CFDI and CLoan are both statistically significant at the 10% level, indicating their positive impacts on industrialization in Africa. It's worth noting that the broader coverage of FDI and loans may explain why their statistical effects appear weaker. However, due to missing observations for loans and FDI, we cannot directly compare their impacts with those of the contract data.

To mitigate the potential impact of measurement errors, we also use Non-Agricultural as an alternative measure for economic structural transformation. The empirical results are presented in Table 6. In Table 6, the coefficient of Contract remains positive and statistically significant in all columns. These findings are consistent with previous results and further confirm the robustness of our findings.

Table 6 Alternative measure for the industrial
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Difference-in-Difference (DiD) Analysis

In this section, we further examine the potential impacts of BRI on African industrialisation through a different channel. Following previous studies (Luo et al. 2021; Shao 2020; Mao et al. 2019; Yu et al. 2019), this paper conducts a quasi-natural experiment. We adopt the staggered difference-in-differences analysis and employ the BRI to denote whether a country has signed Memorandum of Understanding (MoU) with China, as specified by Nedopil’s (2021).Footnote 8 The staggered difference-in-differences model is specified by Eq. 4.

$${Indus}_{i,t+1}={\upbeta }_{0}+{\upbeta }_{1}{BRI}_{i,t}+\sum Controls+{\delta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(4)

BRI = 1 if a country has signed the MOU with China. For example, if country \(i\) signed the MoU with China on November 13th, 2017, then we set BRI = 1 for years after 2016 for country \(i\). The regression results are presented in Table 7. In Col. (2) of Table 7, BRI has a significant coefficient of 1.349, illustrating that the BRI countries have shown significant improvement in the industrialisation process after the announcement of the BRI. Industrialisation proxy increased by about 11.07% (1.349/12.19). This result further confirms the positive effect of the BRI through the signalling effects.

Table 7 Results of the difference-in-differences analysis
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Impact of Regulatory Quality

The previous analysis confirms the positive role of China’s infrastructure projects in facilitating the industrialisation process in Africa. This section focuses on the interaction effects with the regulatory quality of recipient countries. In Table 8, the coefficients of ContractxRegulatory and ContractxRegulatory2 are positive and significant at 5% level, suggesting that Chinese projects exert more substantial positive impacts in the countries with better regulatory quality.

Table 8 Impact of regulatory quality
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The Impact of Chinese Infrastructure Projects on Local Employment

Finally, the impact on the employment dynamics of African countries was examined, and the regression results are shown in Table 9. Employment is proxied by the ratio of unemployment (Unemp), the natural logarithm of total employment from the WDI database (Emp), and the natural logarithm of total employment from the GGDC/UNU-WIDER database (Emp2).

Table 9 Impact on the employment of local countries
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Structural transformation not only reshapes a country’s GDP but also has a substantial effect on local employment (Can and Doğan 2020). In most countries, the manufacturing sector is the most essential sector to create job opportunities for the local workers (Tregenna 2008). Thus, Chinese infrastructure projects may indirectly promote local employment through the rising GDP share of manufacturing and other non-agricultural sectors.

The coefficient of Contract is negative and significant in Col. (1), indicating a significantly negative relationship with unemployment. One SD increase of Contract eliminates about 11.35% (− 0.412*2.163/7.853) of Unemployment relative to the mean level, which is economically significant.

In Col. (2) and Col. (3), the coefficients of Contract are positive and statistically significant at 1% level, confirming the positive role of Chinese projects in jobs creation. These results further confirm our second hypothesis (H2) on local employment.

Policy Implications

Lewis's dual economy model provides the necessary theoretical framework for this empirical study. Our quantitative evidence suggests that, contrary to the narrative of debt-trap diplomacy, China's infrastructure projects have made a significant contribution to the structural transformation process in Africa after controlling for a wide range of variables. It has not only contributed to a higher GDP share in the manufacturing and other non-agricultrual sectors but has also created more jobs and employment opportunities within the local economies.

This subsection aims to discuss the potential policy implications regarding the future of the BRI projects based on the theoretical framework and empirical findings. In line with Lewis's 1953 report on industrialisation for the Government of the Gold Coast, improving productivity in agriculture and the quality of physical infrastructures should remain top priorities on the policy agenda over the next few decades in Africa. China could play a vital role in both areas. Gu and Carey (2019) argue that China's approach, characterised by the presence of state-owned enterprises and its focus on practical implementation over Western-style conditionality, has significantly contributed to electrification and digitalisation in Africa. However, this approach diverges from Western standards in areas like environmental, social, and governance (ESG).

Therefore, to reduce negativity bias in the media, Chinese contractors and financing bodies need to introduce more ESG indicators/measurements into their decision-making matrix, instead of solely focusing on economic and political dimensions. ESG and sustainability are more than just buzzwords that everyone talks about these days. Companies that fail to comply with ESG goals may lose their long-term competitiveness in the market. Therefore, it is also incentive-compatible with China's long-term interests. Compared with private firms, if managed and monitored properly, state-owned companies can be better suited to pursue long-term value creation instead of short-term financial gains.

Additionally, local governance is critical in ensuring that foreign companies comply with ESG goals. As the results reveal, the success of infrastructure projects in transforming the economy is heavily dependent on the quality of local governance. For example, good local governance ensures that only projects with sufficient socioeconomic returns are pursued and helps improve the transparency of bidding and procurement processes. Therefore, engaging in practices that improve local governance could be a win–win solution. However, it may contradict China's long-standing principle of non-interference in foreign policy, which might seem too drastic as a policy change.

One potential solution for China is to further enhance the domestic judiciary system and law enforcement regarding anti-corruption governance. In recent years, China has indeed made some positive changes in this direction. For example, in 2011, a foreign anti-bribery provision was enacted, which forbids 'giving foreign public officials or international organisation's officials assets and properties in order to make improper business benefits.' However, more efforts regarding the enforcement of this provision have yet to be seen.

Another possibility is to go beyond China's existing bilateral collaboration model in Africa. China can benefit from further collaboration with international development institutions (e.g., the World Bank) and other donor countries in the future. The establishment of multilateral institutions, such as the Asian Infrastructure Investment Bank (AIIB) and the New Development Bank (NDB), seems to indicate that China is gradually exploring new collaboration models.

Last but not least, without improving the economic fundamentals, the newly built roads can soon become dilapidated, factories could be left unused, and hospitals could even end up empty without acquiring qualified medical staff. Therefore, in addition to physical infrastructure, the BRI projects should also aim to enhance the development of human capital in Africa. A more educated and healthy labor force will make Africa a more attractive destination for future investments. At the same time, the rising income and booming consumer market also provide more economic opportunities for China. The post-COVID recovery may offer the perfect opportunity to invest in human capital and global public health.

Conclusion

It has been ten years since the birth of the BRI. Instead of a comprehensive and integrated development strategy, most of the projects developed under the BRI have been served in a piecemeal manner. The BRI has generated much controversy within international policy debates and Western media. However, common claims, such as debt-trap diplomacy and the failure to create local employment, often rely on anecdotal evidence and lack rigorous quantitative support.

The results of this study challenge the stereotypical view of China’s role in Africa. Chinese infrastructure projects have made a positive contribution to the industrialisation process and employment creation in African countries, both of which have driven economic development in the region. These results remain robust even after controlling for a wide range of variables. While the long-term impacts of BRI projects cannot be measured at this stage, our study, using a difference-in-differences analysis, finds that the launch of the BRI has had a positive signalling effect on structural transformation in Africa.

This paper is subject to some limitations. First, like many other empirical studies focusing on developing countries, there are several observations with missing variables. If more comprehensive datasets become available in the future, subsequent studies could utilise them for a more thorough investigation of the impacts of China's infrastructure projects in Africa. Second, our research relies on country-level information. Future research could acquire more firm-level or individual-level information to provide micro-level evidence. Third, this study uses proxies of industrialisation as measures of structural transformation, which only partially capture the overall effects of structural change. Future work could explore other dimensions, such as productivity growth and knowledge transfers.