Can foreign aid influence the level of industrialization in African countries?

Asiama, Rex Kweku Awuku; Nell, Kevin Article Can foreign aid influence the level of industrialization in African countries? Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Asiama, Rex Kweku Awuku; Nell, Kevin (2024) : Can foreign aid influence the level of industrialization in African countries?, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-22, https://doi.org/10.1080/23322039.2024.2331369 This Version is available at: https://hdl.handle.net/10419/321466 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Rex Kweku Awuku Asiama & Kevin Nell To cite this article: Rex Kweku Awuku Asiama & Kevin Nell (2024) Can foreign aid influence the level of industrialization in African countries?, Cogent Economics & Finance, 12:1, 2331369, DOI: 10.1080/23322039.2024.2331369 To link to this article: https://doi.org/10.1080/23322039.2024.2331369 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 11 Apr 2024. Submit your article to this journal Article views: 1187 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Can foreign aid influence the level of industrialization in African countries? Rex Kweku Awuku Asiama a and Kevin Nell b a Sustainable Energy and Resources, University of Environment and Sustainable Development, Somanya, Ghana; b College of Business and Economics, University of Johannesburg, Johannesburg, South Africa ABSTRACT Capital inflows, such as foreign aid, can serve as a means to enhance infrastructure development in developing countries. This suggests that foreign aid might have an impact on the level of industrialization in African nations. While existing studies indicate that foreign aid can affect the competitiveness of the manufacturing sector by appreciating the real exchange rate, the veracity of this claim relies on empirical evidence. This paper explores the influence of aid on manufacturing value added using time-series data spanning from 1990 to 2018 for 27 African countries. Employing a panel vector autoregression technique and generating associated impulse response functions, the study scrutinizes the interactions between foreign aid and manufacturing. The analysis is conducted on both the full dataset and subsamples disaggregated based on the income levels of countries. The results indicate that foreign aid acts as a stimulus for manufacturing, primarily through a sustained depreciation of the real exchange rate. This finding holds true for both the overall dataset and the subset of low-income countries. The study attributes this phenomenon to the strategic utilization of aid to enhance infrastructure, leading to a reduction in the price of non-tradables relative to tradables. Consequently, this enhances the profitability and output capacity of the manufacturing sector in African countries. In essence, the results suggest that foreign aid plays a role in influencing or stimulating industrialization in African countries. The study concludes with a discussion on the implications of these findings for industrial policy in African nations. IMPACT STATEMENT This paper determines the extent to which foreign aid influences the level of industrialization in African countries. By using time-series data spanning from 1990 to 2018 for 27 African countries, a panel vector autoregression technique and generating associated impulse response functions, the study scrutinizes the interactions between foreign aid and the level of industrialization in African countries. The analysis is conducted on both the full dataset and subsamples disaggregated based on the income levels of countries. Our results negate the general perception that foreign aid inflows harm industrialization in developing countries, which can affect the competitiveness and profitability of manufacturing and lead to deindustrialization. However, this perception has been one-sided because foreign aid can also be used to boost infrastructure in developing countries. The paper helps to unearth the dominant effect of foreign aid in this context and samples African countries because they are generally less developed and industrialized and stand to benefit from foreign aid. ARTICLE HISTORY Received 9 August 2023 Revised 10 January 2024 Accepted 12 March 2024 KEYWORDS Foreign aid; manufacturing; industrialisation; Africa JEL CLASSIFICATION C23; E60; E61; E65 REVIEW EDITOR Tiwari Aviral, Rajagiri Business School, India SUBJECTS Economics and Development; Sustainable Development; Economics 1. Introduction Africa values foreign aid because it helps the continent achieve its global development objectives. Its impacts on development, however, have been uneven. Data from the Organisation for Economic Cooperation and Development (OECD) show that African countries have been the highest receivers of CONTACT Rex Kweku Awuku Asiama [email protected] Sustainable Energy and Resources, University of Environment and Sustainable Development, Somanya, Ghana ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2331369 https://doi.org/10.1080/23322039.2024.2331369 foreign aid since the Sustainable Development Goals (SDGs) were ratified. For example in 2018, South Africa topped the list of African countries that received the most aid (US$ 4.2 billion), followed by Egypt (US$ 3.5 billion), Mozambique (US$ 3.2 billion), Kenya (US$ 2.4 billion), Ethiopia (US$ 2.3 billion) and Nigeria (US$ 2 billion) (Organisation for Economic Cooperation and Development [OECD], 2021). In addition, some scholars also point out aid’s contribution to achieving SDGs on poverty, hunger and gender inequality and improving access to social services in African countries (Dhahri & Omri, 2020; Ndikumana & Pickbourn, 2017; Pickbourn & Ndikumana, 2016). Despite its seeming importance to developing countries, foreign aid can have some adverse effects— worsening resource governance, promoting corruption and weakening domestic export competitiveness (Cai et al., 2018; Mahembe & Mbaya Odhiambo, 2019). These negative effects of aid reduce its effectiveness and make it difficult to determine the effect of aid a priori. Some scholars have further argued that aid causes an appreciation of a economy’s real exchange rate, resulting in symptoms of the Dutch disease, which harms the manufacturing sector in developing countries and affects their export competitiveness (Magud & Sosa, 2013). This is an important issue for African countries because even though African countries receive several amounts in foreign aid, the growth and development of their manufacturing sector is crucial to their structural transformation and sustainable growth. In effect, there could be potential in supporting the sector using foreign aid. Hence, this paper’s re-examination of aid’s influence on manufacturing value added or the level of industrialization in African countries. Typically, a productive sector, like manufacturing, must trade with the rest of the world and especially with developed economies, which usually influence the price of traded final consumption and service goods (generally referred to as tradables). Most African economies are known to be price takers, assuming a fixed world price for final goods and utilizing a relative exchange rate, known as the real exchange rate. This real exchange rate is defined as the ratio of the fixed foreign price of tradables (usually proxied by the price in the United States) to the domestic price of non-tradables (mainly considered to include infrastructure like power supply and the cost of capital investment). In this sense, a depreciation of the real exchange rate results from a decrease in the domestic price of non-tradables, relative to the domestic price of tradables, which increases the profitability of manufacturing. Foreign aid, on the other hand, may cause the real exchange rate to appreciate by increasing the demand/imports for non-tradable goods and services, which raises the price of non-tradables, and reduces the relative profitibality of manufacturing, a situation likened to the Dutch disease syndrome that results from overreliance on revenues from particular natural resources. Indeed, several studies have examined the effect of aid in developing countries over time (Arndt et al., 2010; Boone, 1994; Easterly, 2003,2006; Herzer et al., 2015; Herzer & Morrissey, 2009; Lof et al., 2015). Nonetheless, a small section of the literature has argued that aid can potentially crowd out manufacturing exports because of the loss of competitiveness caused by the appreciation of the real exchange rate (Addison & Baliamoune-Lutz, 2017; Magud & Sosa, 2013). This is an empirical matter because foreign aid can also be used to increase the supply of non-tradable and productive infrastructure like power supply, construction and transport. Aid used in this way causes the real exchange rate to depreciate, causes manufacturing exports to become more profitable and accelerates industrial development. In any case, the non-tradable sector in African countries is underdeveloped and aid inflows can affect both the supply and demand for non-tradables, depending on its use. These different perspectives on aid effectiveness, therefore, give us a basis to empirically examine the effect of aid and the real exchange rate on manufacturing in African countries, which require structural transformation, despite recent growth and large receipts of aid (Akobeng, 2020; Rodrik, 2018). Using a panel autoregressive technique, with data on 27 African countries from 1990 to 2018, the paper empirically examines the relationships between foreign aid, the real exchange rate and manufacturing. We analyze these relationships first at an aggregate level and later disaggregate the analysis according to the income levels of countries in the sample. This is the paper’s first contribution to the literature. By focusing on the income levels in our analysis, we control for the difference in development levels of sampled countries, which reflects the reality that the effect of aid is unlikely to be the same across all countries, since some African countries depend on aid for development more than others. The paper’s second contribution to the literature is hinged on the application of impulse response functions 2 R. K. A. ASIAMA ET AL. in the aid effectiveness literature. These response functions are generated from panel vector autoregression models and show the persistence of the effect of aid and the real exchange rate on manufacturing. To the best of our knowledge, previous studies that examine the effect of foreign aid and the real exchange rate in developing countries do not analyse the persistence of the effects of these variables using impulse response functions, leaving a gap in the literature that this paper contributes to filling. The results first show that aid does not cause an appreciation of the real exchange rate in African countries. Instead, the results show that foreign aid causes a depreciation of the real exchange rate, which stimulates manufacturing in African countries. Also, the negative and independent effect of aid on manufacturing, is significant for the first few years and insignificant in later years, based on its impulse response function. Thus, this effect of aid does not persist as it is offset by aid’s stimulation of the real exchange rate (real exchange rate depreciation), which then stimulates manufacturing output relative to services. Therefore, the results suggest that foreign aid can influence manufacturing value added or the level of industrialization in African countries. In addition, the results show that the real exchange rate and manufacturing are inversely related. This means that a real exchange rate depreciation stimulates manufacturing in African countries. This finding suggests that competitive real exchange rates can boost manufacturing output in African countries. Also, we find that the effect of the real exchange rate persists over time, suggesting that a competitive real exchange rate is crucial for manufacturing development in African countries. We find similar results from some existing studies (Guzman et al., 2018; Rapetti et al., 2012; Rodrik, 2008) and discuss further the implications of the results for industrial policy in Africa. The rest of this paper is structured as follows: Section two reviews existing literature on aid, the real exchange rate and manufacturing. Section three discusses the research methodology and section four presents the empirical results. Section five concludes. 2. Literature review The dominant argument in the literature has been that undervaluation of the real exchange rate is good for growth and industrialization, especially in developing countries (Chukwu & Malikane, 2018; Rapetti et al., 2012; Rodrik, 2008). 1 In fact, not many studies recommend overvaluation of the real exchange rate in developing countries. 2 Several theoretical arguments supporting the undervaluation or depreciation of the real exchange rate have been put forth (Rodrik, 2008). The first theoretical argument in favour of undervaluation or depreciation of the real exchange rate is that it is indicative of international competitiveness and helps promote exports from the manufacturing sector. The rapid industrialization success of the East-Asian countries, in which export-led growth policies were pursued in conjunction with an exchange rate regime that supported undervaluation, bears testament to this argument. Indeed, as a result of undervalued exchange rates and positive externalities of the manufacturing sector, savings and investments were boosted by revenues from manufacturing exports in these economies (Dooley et al., 2004; Levy-Yeyati & Sturzenegger, 2007). In addition, real exchange rate undervaluation is arguably a compensatory mechanism for the manufacturing sector, despite existing distortions that make it difficult for the sector to receive equilibrium allocation of resources, especially in developing countries (Rapetti et al., 2012; Rodrik, 2008; Schr€ oder, 2013). These distortions are argued to result from bad institutions and market failures that affect the tradable sector, especially in developing countries (Ribeiro et al., 2020; Rodrik, 2008). Furthermore, scholars argue that real exchange rate undervaluation promotes economic diversification and technologyintensive structural change. This is plausible because a competitive exchange rate acts as a subsidy to the tradable sector that can generate learning spillovers across firms and other related sectors, thus making undervaluation a form of industrial policy (Guzman et al., 2018; Rodrik, 2008). The empirical literature mostly shows support for an undervaluation or depreciation, and not an overvaluation or appreciation, of the real exchange rate (Guzman et al., 2018; Rapetti et al., 2012; Rodrik, 2008). However, these studies do not show whether a real exchange rate undervaluation or depreciation driven by aid affects manufacturing in developing countries like this paper does. COGENT ECONOMICS & FINANCE 3 Another strand of the literature further argues that foreign aid, among many other capital inflows, leads to an appreciation of the real exchange rate, which then reflect some of the effects of the Dutch disease. This is because aid raises the demand for non-tradables and causes their price to rise. The increase in the price of non-tradables causes an appreciation of the real exchange rate, which reduces the profitability and, hence, amount of output manufacturing can produce at the given world price (Adam & Bevan, 2006; Addison & Baliamoune-Lutz, 2017; Issa & Ouattara, 2008; Li & Rowe, 2007; Magud & Sosa, 2013; Mongardini & Rayner, 2009). Thus, aid-induced Dutch disease harms a recipient country’s manufacturing sector by making its exports less profitable. 3 However, the response of the real exchange rate determines whether or not foreign aid affects the profitability of the manufacturing sector. The real exchange rate is a relative price and can change or be misaligned based on consumer activity in the tradable (consumer goods) and non-tradable (fixed infrastructure) sectors (Magud & Sosa, 2013). Based on the way the real exchange rate responds to foreign aid inflows, one of the following effects may result from foreign aid inflows. First, when aid is spent to increase the availability of non-tradables services like education and health, higher wages may draw more skilled labour in these sectors. The wage demands of highly-skilled labour may cause the price of non-tradable services to go up and cause the real exchange rate to appreciate. The appreciation of the real exchange rate reduces the profitability of manufacturing and, because a developing economy is small and open, exports of tradable products decline. This raises the domestic price of non-tradables relative to the domestic price of tradables and affects the productivity of the tradable sector. Secondly, aid used to invest in infrastructure (roads and ports) and power supply increases the supply of non-tradables and causes their price to fall. In this way, the real exchange rate depreciates and the tradable sector becomes profitable, which is good for growth. Thus when foreign aid is used to improve factor productivity by purchasing productive capital, intermediate goods and foreign consulting, the real exchange rate may depreciate and stimulate manufacturingbecause of the real exchange rate appreciation Again, higher wages paid to labour for non-tradable services leads to higher expenditure on nontradables. The real exchange rate is likely to be appreciated if foreign aid is spent on imports to satisfy local demand, which the domestic supply sector cannot counter. This means that Therefore, the conclusions of earlier studies that aid is bad for manufacturing in developing countries because of its appreciation of the exchange rate cannot be fully accepted without more empirical investigation. This is because capital inflows, such as aid, are important for some developing countries, especially in SubSaharan Africa (Ouedraogo et al., 2021). The dominance of any of the above-mentioned effects is therefore an empirical matter, since the effect of foreign aid cannot be determined beforehand and requires empirical testing. More so, in the case of African countries who receive so much in foreign aid and have not fully industrialized, the empirical test between foreign aid and manufacturing value added or the level of industrialization is crucial to guide industrial policy in the such countries. 3. Methodology This section begins with a discussion on the vector autoregressive model and presents the model specification. It then presents the estimation technique and descriptive statistics of the sample data. 3.1. VAR & endogeneity concerns We use a VAR model to determine whether aid causes the Dutch disease in African countries. Diagnostic tests for unit roots, optimal lag length and stability are conducted before estimating the VAR model, in order to ensure that meaningful inferences can be generated from the model. The panel VAR model allows one to examine the interactions between foreign aid, the real exchange rate and the manufacturing sector and determine whether there are reverse causalities between these variables. We also use impulse response functions, which are generated from the VAR model, to determine the dynamics in manufacturing and the persistence of innovations in aid and the real exchange rate. Other methods, such as fixed effects panel regression analysis, co-integration regression techniques (DFGLS) and difference-in-differences analysis have been used in the literature to analyse the effect of 4 R. K. A. ASIAMA ET AL. aid (Cai et al., 2018; Dupuy et al., 2016; Minasyan et al., 2017; Nowak-Lehmann et al., 2012). Nevertheless, the VAR model is preferred because it can better control for simultaneity by endogenizing all the variables. By using an unrestricted VAR model, interactions between the variables are captured to allow for reverse causality in the model. In this way, it is possible to determine the influence of aid on manufacturing and vice versa, while analysing the interactions between the real exchange rate, aid and manufacturing. More so, the measure of aid can affect the determination of aid’s effect. Lof et al. (2015) use aid per capita but this is criticized by Herzer et al. (2015), who argue that the use of aid per capita is affected by changes in aid in the numerator and the population in the denominator. As a result, aid, as a share of GDP is a better measure to use in empirical studies (Herzer et al., 2015). Therefore, aid’s share of Gross Domestic Product (GDP) is adopted to determine the effect of foreign aid. Finally, impulse response functions can be derived from the panel VARs. According to Lof et al. (2015), impulse response functions show the dynamic response of variables to innovations in other variables in the system over a given period. Also, we follow Abrigo and Love (2016) because they use confidence intervals that are estimated with Monte Carlo simulations obtained from the distribution of a fitted reduced-form panel VAR model, which improves the reliability of the model estimates. 3.2. Model specification Following the work of Abrigo and Love (2016) and adapting their estimation codes, a panel VAR model is specified. 4 The panel VAR codes of Abrigo and Love are relied on because they give one the option to control for exogenous variables, use GMM estimators, and conduct causality tests and optimal lag selection tests in line with Andrews and Lu (2001) and Granger (1969). The panel VAR model examines interactions between the following variables: aid, measured as the share of foreign aid in total output (AIDit), manufacturing, measured as manufacturing value added share of total output (MVAit) and the real exchange rate (RERit), which is measured as the domestic price level relative to the United States (US) price level, so that an increase indicates an appreciation. This RER is used as a proxy for the price of non-tradables relative to tradables and the domestic price level captures the price of nontradables. An increase in the domestic price represents a rise in the price of non-tradables, which causes the RER to appreciate. In addition, the model controls for fixed capital investment, which is measured as the share of gross fixed capital formation in total output (INVit), in line with the HD and two-gap models earlier discussed. The variables and their sources are defined fully in the next section. The causality prediction for the aid-induced Dutch disease is presented as: AIDit ! þRERit ! − MVAit (3.1) Secondly, the causality prediction for the positive effect of AID working though investment is presented as: AIDit ! þINVit ! þMVAit (3.2) Thirdly, the causality prediction for the positive/negative effect of aid operating though other forces, besides the Dutch Disease effect or investment effect is presented as: AIDit ! þ=− MVAit (3.3) To test the causality predictions of Equations (3.1) -(3.3), the following VAR models are specified: MVAit ¼a0þa1MVAit−1þ...þapMVAit−pþb1AIDit−1þ...þbpAIDit−p þc1RERit−1þ...þcpRERit−pþd1INVit−1þ...þdpINVit−pþliþeit (3.4) AIDit ¼c0þc1AIDit−1þ...þapAIDit−pþg1MVAit−1þ...þgpMVAit−p þd1RERit−1þ...þdpRERit−pþh1INVit−1þ...þhpINVit−pþmiþeit (3.5) RERit ¼k0þk1RERit−1þ...þkpRERit−pþj1MVAit−1þ...þjpMVAit−p þe1AIDit−1þ...þepAIDit−pþi1INVit−1þ...þipINVit−pþsiþfit (3.6) COGENT ECONOMICS & FINANCE 5 INVit ¼p0þp1INVit−1þ...þppINVit−pþq1MVAit−1þ...þqpMVAt−p þf1AIDit−1þ...þfpAIDit−pþ/1RERit−1þ...þ/pRERit−pþtiþwit (3.7) Where c,a,b,g,h,j,k,q,p,i,c,d,e,fare coefficients to be estimated in the VAR models. Also, fixed effects are captured by ti,si,miand li:Furthermore, the stochastic error terms are represented by wit,fit,eit and eit: From Equation (3.4), the null hypothesis that aid does not Granger cause MVAit,H0:b1¼b2¼... ¼ bp¼0, is tested against the alternative that aid Granger causes MVAit,HA:b16¼ 0Úb26¼ 0... Úbp6¼ 0:In addition, the null hypothesis that the real exchange rate does not Granger cause MVAit,H0:c1¼ c2¼... ¼cp¼0, is tested against the alternative that the real exchange rate Granger causes MVAit, HA:c16¼ 0Úc26¼ 0... Úcp6¼ 0:Furthermore, the null hypothesis that gross fixed capital investment does not Granger cause MVAit,H0:d1¼d2¼... ¼dp¼0, is tested against the alternative that gross fixed capital investment Granger causes MVAit,HA:d16¼ 0Úd26¼ 0... Údp6¼ 0: Hypotheses capturing reverse causality are also tested. Hence, from Equation (3.5), the null hypothesis that MVAit does not Granger cause aid, H0:g1¼g2¼... ¼gp¼0, is tested against the alternative that MVAit Granger causes aid, HA:g16¼ 0Úg26¼ 0... Úgp6¼ 0: Again from Equation (3.6), the null hypothesis that MVAit does not Granger cause the real exchange rate, H0:j1¼j2¼... ¼jp¼0, is tested against the alternative that MVAit Granger causes the real exchange rate, HA:j16¼ 0Új26¼ 0... Újp6¼ 0:Similarly, from Equation (3.6), the null hypothesis that AIDit does not Granger cause the real exchange rate, H0:e1¼e2¼... ¼ep¼0, is tested against the alternative that AIDit Granger causes the real exchange rate, HA:e16¼ 0Úe26¼ 0... Úep6¼ 0: Finally from Equation (3.7), the null hypothesis that MVAit does not Granger cause gross fixed capital investment, H0:q1¼q2¼... ¼qp¼0, is tested against the alternative that MVAit Granger causes gross fixed capital investment, HA:q16¼ 0Úq26¼ 0... Úqp6¼ 0: 3.2.1. Estimation technique & specification tests Consider now the estimation technique and specification tests used for the specified panel VAR model. The system GMM estimator is used to control for endogeneity, in line with Abrigo and Love (2016). This estimation technique—developed by Arellano and Bover (1995)—is used because it allows the variables to be estimated in levels, so that data loss is minimized. Using the Arellano and Bover GMM estimator, together with fixed effects and further specification tests, improves the reliability of the results from the panel VAR model. Anderson and Hsiao (1982) developed an alternative method—the difference GMM technique—which involves the use of first differences. However, this approach can be problematic when the panel used has missing observations or is unbalanced. Furthermore, the specification tests for the panel VAR model begin with panel unit root tests. The presence of unit roots reduces the efficiency of instruments used in the GMM estimator (Blundell & Bond, 1998). Also, moment conditions of the GMM estimator are nullified when unit roots are present (Abrigo & Love, 2016). We use both the Levin-Lin-Chu (LLC) and the Harris-Tzavalis (HT) tests, in line with the work of Levin et al. (2002) and Harris and Tzavalis (1999). Both tests use the null hypotheses that there are unit roots in all the panels. Also, the unit root tests, include and exclude the time trends as a robustness check for the results obtained. In addition, the zscores and adjusted t-statistics of the unit root tests are used to reject the null hypothesis of unit roots in the panels. After unit root testing, the optimal lag length of the VAR model is required. The optimal lag length of the VAR model is determined using Moment Model Selection Criteria (MMSC) based on Hansen’sJstatistic of overidentifying restrictions (Andrews & Lu, 2001; Hansen, 1982). These MMSC are similar to frequently used maximum likelihood-based criteria such as the Akaike, Bayesian and Hannan-Quinn information criteria (Abrigo & Love, 2016). The decision criteria, therefore, is to select the lag length that shows the lowest value of the Akaike, Bayesian or Hannan-Quinn MMSCs. Again, the panel VAR model must be tested for stability. The VAR model is stable when each moduli of the companion matrix is lower than one (Hamilton, 1994;L € utkepohl, 2005). Stability of the VAR model is important because it proves that the VAR is invertible and its impulse response functions are useful for interpretation. This is an important pre-test to ensure that the VAR model is not misspecified. 6 R. K. A. ASIAMA ET AL. In addition, the Granger causality tests are used to determine causality in the model. These tests are performed after the VAR estimates are obtained. Wald tests are used to test the null hypotheses in Equations (3.4)–(3.7). In addition, orthogonalized impulse response functions are derived from the underlying VAR models. These functions show the response of one variable to innovations in another variable in the system. In this way, the effect of the variables on each other can be discussed. Finally using this methodology, this paper is unique in the following way: a sub-sample analysis is conducted, where countries are grouped by their income levels to see the effect of aid, after the aggregate analysis. We use the World Bank’s income classification of countries over time, as this data is reliable. We anticipate that because low-income countries rely more on foreign aid, the effect of aid may be more noticeable in such countries, than in middle-income countries. In addition, by using impulse response functions generated from the panel VAR model, we are able to examine the dynamics of manufacturing over time and the persistence resulting from shocks in aid and the real exchange rate. Earlier studies examining the aid and the Dutch disease effect do not show the persistence of the effect of aid and the real exchange rate. 3.3. Data & descriptive statistics 3.3.1. Data The analysis uses a balanced panel dataset, containing data for 27 African countries from 1990 to 2018. The sample size is selected based on data availability. The definitions of all variables in the dataset are shown in Table 1. Most of the data are obtained from the World Development Indicators (WDI) database of the World Bank, 5 with the exception of data on the real exchange rate, which is obtained from the Penn World Tables (PWT) database version 9.1 (Feenstra et al., 2015). In addition, the list of countries sampled in the study are shown in Table 11 of the Appendix A. 3.3.2. Descriptive statistics The summary statistics for the main variables in our analysis are presented in Table 2. The results from the table show that the average output share of manufacturing value added in African countries sampled is 12% whereas the average share of foreign aid in total output is 6.5%. Also, the results from Table 2 show that the average real exchange rate (relative price ratio) in African countries sampled is 0.3993 while the average share of gross capital formation in total output (fixed investment) is approximately 21.9%. Looking further at the distribution of manufacturing value added share and the share of foreign aid in total output across countries (see Table 3), the results show that the average share of aid to GDP is Table 1. Definition of variables. Variable Definition Source MVAit Manufacturing value added share of GDP (constant 2010 US$) WDI RERit Relative price ratio, measured as the domestic price level relative to the price of the United States (US). An increase in the ratio denotes an appreciation. The relative price ratio is calculated by dividing the Purchasing Power Parity (PPP) exchange rate for each country by its nominal exchange rate. PWT INVit Gross capital formation share of GDP (constant 2010 US$) WDI AIDit Foreign aid share of GDP. Aid is measured as a combination of net official development assistance and official aid received (constant 2015 US$). WDI Source: Author’s compilations. Table 2. Summary statistics using full sample. Stats Mean Min Max MVAit 0.121 0.016 0.511 AIDit 0.065 −0.02 0.805 RERit 0.399 0.149 1.163 INVit 21.886 −2.424 58.188 Source: Author’s estimations. COGENT ECONOMICS & FINANCE 7 Previous studies such as Rajan and Subramanian and Magud and Sosa (2013) also find similar results. However, these studies do not examine the persistence of aid’s effect on manufacturing, as we do in this paper. Rajan and Subramanian give the impression that the negative effect is important, but without explicitly testing the persistence. But from this analysis, it is shown that it is important to qualify the negative effect; whether it is persistent or not. Thus, by using impulse response functions and determining the persistence of aid’s effect, a contribution is made to the literature on aid-induced Dutch disease in developing countries. 4.3. The influence of the real exchange rate The impulse response functions showing the response of manufacturing to innovations or shocks from the real exchange rate are now examined in this section. Using the full sample, the impulse response function presented in Figure 5 shows that, at 5% level of significance, the RER has a negative and significant influence on manufacturing in African countries sampled. This is because the zero line falls outside the confidence intervals shown in Figure 5. This finding suggests that innovations in the RER negatively and significantly influence manufacturing in the first few years and persists well into the tenth year. This means that an appreciation of the real exchange rate reduces the share of manufacturing value added in GDP. The independent effect of the RER on manufacturing in this paper is in line with the findings of earlier studies like Rajan and Subramanian, even though their paper argues that the RER is the transmission channel for aid’s effect on manufacturing. This major finding is also in line with the results obtained from the VAR model, presented in Table 7. Furthermore, it supports arguments in the literature on undervaluation and manufacturing promotion in developing countries (Rapetti et al., 2012; Rodrik 2008; Guzman et al. 2018; Missio et al. 2015; Schr€ oder, 2013). We further examine the response of manufacturing to innovations in the RER by analysing impulse response functions according to the income levels of countries in our dataset. These are presented in Figure 4. Response of MVA/GDP to innovations from AID/GDP for Upper-Middle Income countries. Figure 5. Response of MVA/GDP to innovations from RER using full sample. 14 R. K. A. ASIAMA ET AL. Figures 6–8. The results from this sub-sample analysis show that the RER negatively influences manufacturing and this effect persists into the sixth year in low-income countries. After the sixth year, innovations in the RER have no significant influence on manufacturing in low-income countries. However, for lower-middle income and upper-middle-income countries, impulse response functions showing manufacturing’s response to innovations in the RER are insignificant. This is because the zero line passes through the confidence bands shown in the impluse response functions in Figures 6–8. Figure 6. Response of MVA/GDP to innovations from RER - Low-Income countries. Figure 7. Response of MVA/GDP to innovations from RER - Lower-Middle Income countries. Figure 8. Response of MVA/GDP to innovations from RER - Upper-Middle Income countries. COGENT ECONOMICS & FINANCE 15 4.4. Impulse responses from aid to RER Finally, impulse response functions that show the response of the real exchange rate to impulses in aid are considered. From the VAR estimates presented earlier, we find that aid negatively influences the RER in African countries. This is also the case in low-income countries in our sample. We argue that this negative relationship is not consistent with the hypothesis of aid-induced Dutch disease, in which the RER would respond positively to aid inflows. We find that the impulse response function showing the response of the RER to aid confirms the negative influence of aid on the RER, as seen in the VAR model. This impulse response function, shown in Figure 9, indicates that aid’s influence on the RER does not persist over time. From Figure 9, the negative influence of aid on the RER is statistically significant for the first four years, after which it becomes insignificant, because the zero line falls within the confidence bands of the impulse response function. Our finding differ from results of earlier studies like Rajan and Subramanian and Magud and Sosa (2013), who argue that aid causes the Dutch disease because it has a positive influence on the RER. Additional impulse response functions obtained from the subsample analysis also show that the effect of foreign aid on the RER is negative in low-income countries, albeit insignificant. Also for lower-middleincome and upper-middle-income countries, the impulse response functions obtained were insignificant. 5. Conclusion The primary goal of this paper is to investigate the impact of foreign aid on the level of industrialization in African countries. Given the substantial significance of foreign aid to African economies, which are still in the process of catching up with the developed world, the study delves into the debate surrounding the growth effects of aid. While some scholars emphasize the positive role of aid in supporting infrastructure and manufacturing, others express skepticism due to the potential appreciation of the real exchange rate, reminiscent of the Dutch disease syndrome. The focus of this paper is to assess the influence of aid on manufacturing value added and the overall level of industrialization in African nations. The paper employs a panel VAR estimation technique to model the dynamic interactions among manufacturing, aid, and the real exchange rate, while accounting for fixed investment in alignment with established aid theories. Two key contributions are made to the existing literature. Firstly, the study analyzes the impact of aid and the real exchange rate based on the income levels of the sampled countries, recognizing the variability of aid effects across different economic contexts. Secondly, it utilizes impulse response functions to gauge the persistence of shocks from aid and the real exchange rate on manufacturing in African countries. Through this approach, the paper empirically contributes to the aid effectiveness literature, offering insights not commonly explored in earlier studies examining aid-induced Dutch disease, particularly in developing countries. In contrast to the conventional expectation that foreign aid inflows in developing economies lead to a real exchange rate appreciation, our findings do not align with this pattern. The outcomes derived from the VAR and impulse response functions indicate a contrary effect, wherein aid is associated with a Figure 9. Response of RER to innovations from AID using full sample. 16 R. K. A. ASIAMA ET AL. real exchange rate depreciation. Remarkably, this depreciation, in turn, acts as a stimulus for manufacturing in African countries. As a result, our conclusion is that foreign aid can indeed exert an influence on the level of industrialization in African nations. Our findings also show that the independent effect of foreign aid on manufacturing is negative. However, this effect is short-lived—it lingers for the first few years, after which it becomes insignificant. Previous studies such as Rajan and Subramanian and Magud and Sosa (2013) find a similar result, even though they do not show any evidence of persistence as we do. We argue that it such short-term effects that convince other scholars that foreign aid negatively affects the growth. We suggest that there is merit in looking at the persistence of the aid’s effect on manufacturing in the long-term because of the lead time involved in establishing industries and manufacturing. Also, we find that the real exchange rate negatively influences manufacturing in African countries. This means that a real exchange rate depreciation stimulates the output share of manufacturing in African countries. This result is in line with findings of earlier studies such as (Rapetti et al. 2012; Rodrik 2008; Guzman et al. 2018; and Missio et al. 2015). The effect of the real exchange rate on manufacturing also persists, all the way into the tenth year. This is an additional contribution to the literature as earlier studies do not look at persistence as we do in this paper. There are potential policy implications arising from the obtained results. Policymakers in Africa might contemplate utilizing foreign aid to enhance the provision of non-tradable goods, such as power supply and infrastructure. This approach could effectively lower the cost of non-tradables relative to tradables, stimulate the exchange rate (resulting in real exchange rate depreciation), and render manufacturing more economically viable. The resultant stimulation of the real exchange rate could potentially foster growth in the manufacturing sector across African countries. However, the utilization of foreign aid in this manner requires a cautious approach, aligning with each country’s existing industrial and trade policies. This ensures that each nation secures a competitive advantage in production, creating and sustaining employment in their productive sectors. By adopting such a strategy, donors can be assured of obtaining value for their funds, potentially motivating them to continue directing financial support to developing countries in Africa, independent of other contextual factors influencing foreign aid allocations. Although Africa is recognized for its leadership in resource-based exports, it has not yet established itself as a hub for product-based exports due to the relatively high production costs faced by local entrepreneurs and manufacturers. Real exchange rate depreciations present an opportunity for economies to gain a competitive advantage, a strategy successfully employed by current global economic leaders. Encouraging real exchange depreciation can incentivize producers to boost output and exports. This strategy could be considered for regional and continental trade regimes in Africa, aiming to enhance productivity and output over time. Nevertheless, future research on aid and industrialization might explore the effects of aid at the sectoral level using input-output analysis, providing additional insights into the impact of aid and the real exchange rate on various sectors in African economies. Acknowledgments The author expresses their gratitude to Professor Fiona Tregenna for her valuable reviews that have improved the quality of this paper. Disclosure statement No potential conflict of interest was reported by the author(s). Notes 1. The downside is that undervaluation of the exchange rate results in domestic inflation in small open economies who are mostly dependent on imports of final goods and services (Ribeiro et al., 2020). 2. See for instance Oreiro and D’Agostini (2017); Cottani et al. (1990); Ghura and Grennes (1993). 3. The spending and resource movement effects, which arise from aid inflows into an economy, are fundamental to the theory of aid-induced Dutch disease (Rajan & Subramanian) COGENT ECONOMICS & FINANCE 17 4. Abrigo and Love’s approach is an update of the methodology developed by Love and Zicchino (2006), which investigates the relationship between firm financial conditions and investment in 36 countries for the period 1988–1998. 5. Although openly available, we find that current checks to update the dataset proved futile due to missing data. We provide a data availability statement at the end of the paper regarding access to the dataset used in this paper, as we have no control over updates to the WDI database. 6. Other scholars have argued against the benefits of undervaluation or depreciation of the RER for manufacturing and growth (Ribeiro et al., 2020). 7. The high-income category is omitted in this analysis due to insufficient country observations. About the authors Dr Rex Kweku Awuku Asiama is an applied economist specializing in industrial policy in Africa, development economics and development studies. Professor Kevin Nell is a professor of Economics, specializing in macroeconomics, with specific reference to developing countries. ORCID Rex Kweku Awuku Asiama http://orcid.org/0000-0002-3530-2549 Data availability statement Data is available upon request to the corresponding author. References Abrigo, M. 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Chinese aid and nutrition improvement in Sub-Saharan Africa. Applied Economics, 1–19. https://doi.org/10.1080/00036846.2023.2204216 20 R. K. A. ASIAMA ET AL. Appendix A. Income classifications of sampled countries Panel VAR stability stats Table 11. Income classifications of sampled countries. Income category Country name(s) Low Burkina Faso, Congo (Democratic Republic), The Gambia, Malawi, Mozambique, Rwanda, Sierra Leone, Sudan, Togo, Uganda. Lower-middle Benin, Cameroon, Congo Republic, Eswatini (Swaziland), Kenya, Morocco, Nigeria, Tanzania, Tunisia, Zimbabwe. Upper-middle Botswana, Gabon, Mauritius, Namibia, Seychelles, South Africa. Note. World Bank Country Income classifications as at July 1, 2021. Source: World Bank Data. Table 12. Panel VAR stability stats. Eigenvalue Modulus Real Imaginary 0.9417 0 0.9417 0.811 0 0.811 0.711 −0.082 0.715 0.711 0.082 0.715 Source: Author’s computation. Figure 10. Panel VAR Stability Graph. Figure 11. Panel VAR Stability Graph—Low-income countries. COGENT ECONOMICS & FINANCE 21 Figure 12. Panel VAR Stability Graph—Lower-middle-income countries. Figure 13. Panel VAR Stability Graph—Upper-middle-income countries. 22 R. K. A. ASIAMA ET AL.