Analysis of total factor production and official development assistance relationship in developing countries

Tony, Ezako Jean Article Analysis of total factor production and official development assistance relationship in developing countries Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Tony, Ezako Jean (2024) : Analysis of total factor production and official development assistance relationship in developing countries, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-17, https://doi.org/10.1080/23322039.2023.2294631 This Version is available at: https://hdl.handle.net/10419/321390 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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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Analysis of total factor production and official development assistance relationship in developing countries Ezako Jean Tony To cite this article: Ezako Jean Tony (2024) Analysis of total factor production and official development assistance relationship in developing countries, Cogent Economics & Finance, 12:1, 2294631, DOI: 10.1080/23322039.2023.2294631 To link to this article: https://doi.org/10.1080/23322039.2023.2294631 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 12 Jan 2024. Submit your article to this journal Article views: 928 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 Analysis of total factor production and official development assistance relationship in developing countries Ezako Jean Tony Department of Economics and Management, University of Burundi, Bujumbura, Burundi ABSTRACT The objective of this paper was to analyze the relationship between official development assistance (ODA) and total factor production (TFP) in developing countries to allow a better allocation of aid. We used a panel of 69 developing countries, 29 lowincome and 40 middle-income countries, from 2005 to 2019. System GMM and fixed effects were used both with time, and country fixed effects to get rid of unobserved heterogeneity and possible aid endogeneity. For more precision, we disaggregated aid by sector to assess their effect on the main parts of TFP. The results found showed no significant impact of ODA on TFP. However, for low-income countries, aid in the sector of agriculture had a positive impact on human capital, employment, and real GDP. Aid in the sector of industry had a positive impact on human capital, the share of labor compensation, and the real GDP. For middle-income countries, Aid in the sector of education had a positive impact on employment and capital stock. Aid in the sector of economic infrastructure had a positive impact on human capital, employment, and capital stock. We recommend that donors direct most of the aid to low-income countries in the agropastoral sector and in the sector of industry and mining. In addition, we recommend that donors direct a significant part of their support to middle-income countries in the sectors of economic infrastructure and education. Furthermore, very good coordination between donors and institutions in receiving countries is a priority to adapt assistance to the policies of receiving countries. ARTICLE HISTORY Received 24 April 2023 Revised 17 November 2023 Accepted 5 December 2023 KEYWORDS ODA TFP relationship; developing countries; ODA by sector; aid effectiveness; total factor production REVIEWING EDITOR Aye Goodness, University of Agriculture, Nigeria SUBJECTS Econometrics; Development Economics; Political Economy JEL CLASSIFICATION CODES O4; C33; F35 1. Introduction Despite the numerous economic crises that have shaken the world, ODA (official development assistance) has proven to be the most stable source of external financing for developing countries, especially in comparison with private flows, which are more sensitive to economic shocks. Net ODA, which is the total ODA spent minus the repayment of loan principal by recipient countries, has generally increased steadily in volume terms since 1960, when ODA was first measured and stood at just under USD 40 billion (in 2020 prices). It has more than doubled in real terms (þ118%) since 2000, when the Millennium Development Goals were adopted, despite the impact of the 2008 crisis on suppliers’economies. In total, Development Assistance Committee (DAC) donors have allocated USD 18.7 billion to COVID-19related activities, which represents 10.5% of their combined net ODA for 2021. For many years, official development assistance (ODA), which according to the Development Assistance Committee (DAC) of the OECD is defined as aid provided by States for the express purpose of promoting economic development and improving living conditions in developing countries, has been one of the preferred means used by developed countries to help the least developed countries to initiate the development process. The latter requires sustained economic growth which is only possible through an increase in factor productivity. However, even if it was assumed to be exogenous, total CONTACT Ezako Jean Tony [email protected] Department of Economics and Management, University of Burundi, Gihosha, Bujumbura, Burundi 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, 2294631 https://doi.org/10.1080/23322039.2023.2294631 factor production has been considered as one of the main factors that determine the growth rate of countries since Solow (1956) and Swan (1956) growth models. Moreover, endogenous growth models, emphasizing factors such as education, innovations, and quality of institutions to explain productivity, show the central role played by TFP in explaining economic growth in the long run and thus, development (Aghion & Howitt, 1996; Grossman & Helpman, 1991). However, despite the continuous increase of ODA, their effects on productivity and thus, on economic growth remain mitigated. In the literature, some authors defend the positive impact of aid on growth (Arndt et al., 2015; Groß & Nowak-Lehmann Danzinger, 2022). In the same vein,  Swierczy nska and Kliber (2019) show that aid in the form of technical cooperation positively affects productivity. The positive effect of aid on growth can be explained by the fact that it provides supplementary resources of finance and enhances investment and capital stock in recipients’countries. In addition, as shown in Mahembe and Odhiambo (2017), foreign aid affect the development process and thus, reduces poverty through the channels of economic growth, the funding of infrastructure, education, health, and other development initiatives. For other authors, aid has a negative impact on growth (Doucouliagos & Paldam, 2013; Nowak-Lehmann et al., 2012). According to Groß and Nowak-Lehmann Danzinger (2022), this negative effect of aid can be explained by its opposite impact on different channels of growth, namely investment, savings, human capital, and total factor productivity. In addition, as suggested by Park and Park (2019), the dependence of African countries on aid prevents them from taking advantage of other opportunities. This reflects the problems related to foreign aid in developing countries because the tying of aid to conditions set by donor countries may not serve the interests of developing countries (Edo et al., 2023). However, Phiri (2017) suggests that development aid is not a problem itself but it’s the misappropriation that limits its ability to foster growth. The development process requires significant financing needs for the establishment of infrastructure and public services, thus, Bird and Choi (2020) suggest that it is imperative to contemplate the effectiveness of external sources of finance in engendering economic growth. Since the adoption of the Sustainable Development Goals (SDGs) by United Nations Member States, it has become fundamental for equitable progress for all that foreign aid intended for developing countries must increase. In addition, achieving the SDGs requires sustained economic growth which in endogenous models involves, among other things, increasing TFP (total factor production). The latter describes the part of a country’s increase in production that cannot be explained by increases in capital or labor. Jia and Williamson (2019) point out that the effectiveness of foreign aid in developing countries is constantly questioned. Additionally, Bird and Choi (2020) mention that it is imperative to consider the effectiveness of external financing sources to generate economic growth. Given the importance of achieving the SDGs, the study of the relationship between TFPs and official development assistance becomes a priority, given the essential role they play in the development process. Furthermore, most research on the impact of official development assistance on the economies of developing countries has often focused on the impact of aid on GDP growth Arndt et al. (2015). For others, they assess the impact of different types of ODA (grants, loans, bilateral, multilateral) on economic growth. To our knowledge, there is no paper that specifically evaluates the effect of ODA from official donors on the total factor production of developing countries. Thus, the objective of this paper is to assess if ODA promotes TFP in developing countries to allow a better allocation of aid while reducing its possible negative effects. For more precision, this paper analyzes the direct impact of ODA by sector on the main determinants of TFP. This will allow developing countries to optimize the contribution of ODA in the development process and in the achievement of the SDGs. In addition, donors will therefore be able to better direct their support by taking into account the factors that drive development in developing countries. The originality of this work lies in: first, the exclusive use of official development assistance provided by official donors as a proxy for aid while other studies focus mainly on bilateral or multilateral aid, grants, and loans. Thus, this paper is a contribution to the existing literature on the link between ODA and productivity: second, the fact that we divided developing countries in the panel based on income level for a better assessment of the effect aid on productivity based on the level of development: third, the use of aid by sector and the main parts of TFP to show the impact of aid in such sectors on those parts of productivity and thus allow a better allocation of aid in sectors that induce more productivity: 2 E. JEAN TONY fourth, the use of different econometric models System GMM and Fixed effect with time and country fixed effect to minimize the problem of endogeneity and estimation bias to produce more consistent and reliable estimation results: fifth, the assessment of long-run coefficients to better guide policies. The rest of the paper is organized as follows: section 2 presents a brief review of the theoretical and empirical literature on the relationship between aid and productivity; section 3 discusses the methodological framework of this research; section 4 presents the estimation results, while section 5 concludes and summarizes the policy implications of the study. 2. Review of the literature The quality and impacts of aid can be studied through a wide variety of approaches. Among others, cross-country regressions can provide insight into the relationship between aid flows and country-level progress in areas such as economic development, human development, and governance. The development literature has devoted much attention to the link between aid and growth, as aid was supposed to solve the problems of the savings gap and poverty in low-income countries. Nevertheless, the relationship between development aid and productivity remains mixed. This productivity is represented by total factor productivity which is the residual of the growth regression equation (Kim & Loayza, 2019). Theoretically, several studies have suggested that generally, an increase in ODA has a positive effect on investment and growth (Arndt et al., 2015; Clemens et al., 2012; Groß & Nowak-Lehmann Danzinger, 2022). However, the magnitude of the impact depended on the country considered and its institutions quality, the level of aid disbursed, among other factors. This is supported by Alvi et al. (2008) who showed that aid is positively related to economic growth, conditional on sound economic policies, good governance, strong institution, and favorable natural endowment. In addition, foreign aid has a positive effect on growth as it helps to fill the foreign exchange gap and the saving-investment gap of developing countries. For other authors, aid has a negative effect on growth (Askarov & Doucouliagos, 2015; Doucouliagos & Paldam, 2013; Nowak-Lehmann et al., 2012). One of the reasons leading to aid ineffectiveness is the use of the latter for consumption purposes. As stated by Askarov and Doucouliagos (2015) aid can lead recipient countries to be less accountable on domestic accounting by not financing expenditure through taxation because the country is content with the aid received. Moreover, Feeny and De Silva (2012) show that aid can also be ineffective due to constraints related to the absorption capacity of the receiving country. These constraints are, among others, human and physical capital constraints, policy and institutional constraints, macroeconomic constraints, and social and cultural constraints, to add to this the constraints are linked to the conditions set by the donor countries to benefit from this assistance. Empirically, some studies have found a positive link between ODA and productivity. Groß and Nowak-Lehmann Danzinger (2022) analyzed the impact of different forms of aid (grants, loans, bilateral and multilateral) on productivity using panel data from 27 recipient countries over a 25-year period (1985–2009). The authors used quantile regressions to determine whether aid is less effective in countries with the lowest TFP quantiles. They found differences between the impact of grant and loan aid and the impact of bilateral and multilateral aid, with evidence that aid reduced TFP growth inthe0.1and0.25quantiles.Veiderpass(2015) using a balanced panel of 89 lowand middleincome countries from five different geographical categories over an 11-year period, analyzed the effect of foreign aid on productivity. He found a weak significant correlation between foreign aid and productivity. Kallon (2018) examined the long-run relationship between labor productivity and foreign aid in Sierra Leone. He found a positive and significant long-run aid-growth relationship. He also finds that total factor productivity (TFP) is the most important factor in explaining labor productivity in the country. Other studies found a negative link between ODA and economic growth. Liew et al. (2012) examined the impact of foreign aid on the economic growth of East African countries over the period of 1985 to 2010. The results suggested that foreign aid has a significant negative influence on economic growth for these countries. Mallik (2008) analyzed why African countries, namely the Central African Republic, Malawi, Mali, Niger, Sierra Leone and Togo, didn’t break the poverty trap despite receiving large inflows of foreign aid. He found that the long-run effect of aid on growth was negative for most of these COGENT ECONOMICS & FINANCE 3 countries. However, other studies found a mixed or not significant relationship between ODA and growth. Rahnama et al. (2017) analyzed the effect of ODA on economic growth using GMM methodology. They found a mixed effect of aid on growth with positive effects on growth in high-income developing countries and negative effects on growth in low-income developing countries. Yiew and Lau (2018) investigated the role and the impact of foreign aid (ODA) on economic growth (GDP) for 95 developing countries. The results indicated that initially, foreign aid negatively impacts the countries’ growth and over a period, it positively contributes to economic growth. Moreover, ODA effect on productivity can be assessed through its effect on the main parts of TFP; El Namrouty et al. (2022) shed light on the role of foreign aid to secondary education on the development of human capital. The results showed that foreign aid contributes significantly to human capital. Mueller (2022) analyzed if Chinese foreign aid foster economic development and found a large positive effect of aid on employment. Mishra and Aithal (2021) assessed the effect of foreign aid on the development of Nepal and found that the real GDP and Aid are highly associated. The summary of the literature shows that ODA has a positive impact on productivity and thus on economic growth. However, these positive effects of aid can be annihilated by the structural shortcomings of the countries receiving aid, but also by the conditions under which aid is granted by donors. This mixed effect of aid on growth can be due to the type of aid considered. While most studies used aggregate ODA the few studies that have used disaggregated ODA have focused on ODA in terms of bilateral or multilateral aid, grants, and loans. This paper fills the gap in the literature by using exclusively ODA from official donors but also by disaggregating ODA based on sectors in which this aid is allocated. Moreover, the panel has been divided into two parts based on income level, lowand middleincome countries. Nevertheless, Rajan and Subramanian (2008) suggested that there is no evidence that foreign aid works better in the presence of better policy or geographical environments, or that certain forms of aid work better than others. This reflects the fact that the literature on aid effectiveness has been plagued by a variety of empirical impediments such as endogeneity, omitted variable bias or not considering the dynamic aspect of aid. To address these issues, we used system GMM and fixed effect models with country and time fixed effect. In addition, the ODA variable has been lagged in all models. 3. Methodology One of the most common problems of cross-country studies is heterogeneity. That means that each individual in the panel is different, and structural relationship varies across individuals. To get over this heterogeneity that might bias our estimations, we opted for a fixed effects model. This method allows to eliminate bias from omitted variables that are constant over time (country fixed effect) and vary over individuals and also for variables that are constant over individuals and constant over time (time fixed effect). Usually, the economic impact of ODA inflows takes time to become observable. Thus, the aid variable will be included in the model as lag in other to take into count this dynamic. TFP growthit ¼b0þb1ODAi,t−1þqXit þciþdtþeit (1) In Equation (1),TFP growthit is the growth of total factor production, ODAi,t−1is the lag of net official development assistance, Xit is a vector of explanatory variables (government final consumption, health expenditure, remittances, infrastructure, and governance). b1,b0, and qare parameters. Individuals fixed effects are denoted by ciand time fixed effect are denoted by dt: Thus, the endogeneity of aid is a key factor to address. This endogeneity is due to the fact that they might be a bidirectional causality between aid and total factor productivity. Not only aid endogeneity, but other explanatory variables such as infrastructure based on the literature, seem to be affected by immediate feedback from the dependent variables (total factor productivity). To get rid of that, and of a possible serial correlation, we also used one step System GMM to assess the robustness of the results. Developed by Blundell and Bond (1998) System GMM allows to get rid of time constant unobserved individual effects by first differencing with heteroskedasticity robust standard errors and yields residuals without second order serial correlation. For this matter of preventing serial correlation and endogeneity, we added in the System GMM model time fixed effect to remove cross individual correlation and we lagged the aid variable in the model. 4 E. JEAN TONY TFPgrowthit ¼b0þb1TFP growthi,t−1þb2ODAi,t−1þqXit þdtþeit (2) In Equation (2),TFP growthit is the growth of total factor production, TFP growthi,t−1is the lag of total factor production, ODAi,t−1is the lag of net official development assistance, Xit is a vector of explanatory variables (government final consumption, health expenditure, remittances, infrastructure, and governance). b1,b0,b2, and qare parameters. Time fixed effects are denoted by dt: Before using the model stated above, we estimated the model with two stage least square by considering aid as an endogenous variable instrumented by the governance index. The results of the WUHausman test of endogeneity showed that variables are exogenous. It’s not surprising, in several papers governance is used as an instrument of to get rid of aid endogeneity Dreher et al. (2019). Thus, adding the governance index and lagging the aid variable in the model helped in addressing this issue. This result reinforces our choice to use a fixed effect model which assumes that explanatory variables are exogenous. System GMM is considered as the main estimator because it allows the use of instruments even if variables are exogenous and, in that way, helps to control for unobservable endogeneity. For more clarity on the relationship between total factor production and official development assistance, we divided the dataset into two parts based on income level with 29 low-income countries and 40 middle income countries. To go even deeper into this relationship, we divided official aid in sector (Agriculture, forestry, and fishing; Economic infrastructure and Services; Education; Energy; Industry, mining, and construction) and total factor production based on its main parts such as employment; human capital; share of labour compensation; real GDP; Capital stock to assess the effect of aid in those sectors on different parts of TFP. The variables used for the paper are described in Table 1. We used data from the Penn World Tables (PWT), OECD International Development Statistics, World Development Indicators (WDI), and World Governance Indicators (WGI) from 2005 to 2019 for 69 developing countries. The countries used in the panel are shown in the table in Appendix A. The total factor productivity measure was taken from the PWT and was constructed using the following procedure: RTFPNA jt RTFPNA jt−1 ¼RGDPNA jt RGDPNA jt−1 =Qjt,t−1 Where, Qjt,t−1¼1 2LABSHjt þLABSHjt−1EMPjt EMPjt−1 HCjt HCjt−1  þ1þ1 2LABSHjt þLABSHjt−1 ðÞ  RKNA jt RKNA jt−1 ! RTFP NA : TFP level, computed with RGDP NA ,RK NA ,EMP,HC, and LABSH. RGDP NA : Real GDP at constant national prices. RK NA : Capital stock at constant national prices. EMP: The number of people employed. HC: Human capital based on the average years of schooling from Barro and Lee (2013) and an assumed rate for primary, secondary, and tertiary education from Caselli (2005). LABSH: The share of labor income of employees and self-employed workers in GDP. j: country, and t: year. For our analysis, we calculate annual TFP growth rates by differencing the log-transformed TFP levels of year tand t−1, ln(rtfpna t )−ln(rtfpna t−1 ). In Equation (1) infra_index and gov_index are indices of infrastructure quality and institutional quality, respectively. These two indices are constructed with the PCA (Principal Component Analysis) approach. Furthermore, the variables used for the construction of these two indices are recorded in Tables 2 and 3 for the infrastructure index and governance index, respectively. 4. Presentation of the results Table 4 shows the differences between countries in terms of growth of the TFP, despite the fact that all these countries are considered as developing countries. There is also a very low average growth of the TFP which is 0.7%. This is in line with the endogenous growth theory which explains the differences COGENT ECONOMICS & FINANCE 5 between countries by their differences in productivity. Thus, one of the characteristics of developing countries is a low TFP. In terms of total aid received, the minimum value is positive. This shows the regularity and importance of the support given to developing countries by official donors. Regarding aid by sector, it is important to note that only aid received for the education sector has a positive minimum value, which testifies to the regularity of support in this sector by donors. In addition, before estimating the model, we analyzed the correlation between the variables used and tested their stationarity. Table 5 reports the correlation table. The results suggest a negative link between official development assistance and total factor production. A positive link is found between aid in the sector of agriculture and total factor production. However, if official development assistance is Table 1. Description of variables. Name Variable description and source Health_exp Current health expenditure per capita (current US$) [SH.XPD.CHEX.PC.CD] Source: WDI lnODAsect_agri_f Log of Total ODA received from official donors in Agriculture, forestry and fishing Source: OECD lnODAsect_ecoinfra Log of Total ODA received from official donors in Economic infrastructure and Services Source: OECD lnODAsect_edu Log of Total ODA received from official donors in Education Source: OECD lnODAsect_ene Log of Total ODA received from official donors in Energy Source: OECD lnODAsect_indu Log of Total ODA received from official donors in Industry, mining and construction Source: OECD lnODA_net Log of Total ODA received from official donors Source: OECD TFP_growth Total factor productivity known as the proxy of productivity Source: PENN WORLD TABLES Gov_fincons General government final consumption expenditure (annual % growth) [NE.CON.GOVT.] Source: WDI Remittances Personal remittances, received (% of GDP) [BX.TRF.PWKR.DT.GD.ZS] Source: WDI Infra_index Proxy of infrastructure quality Source: author using WDI Gov_index Proxy of governance and institution quality Source: author using WGI Emp Number of person engaged (in millions) Source: PENN WORLD TABLES Hc Human capital index Source: PENN WORLD TABLES Labsh Share of labour compensation in GDP at current national prices Source: PENN WORLD TABLES Realgdp Log of Real GDP at constant 2017 national prices (in mil. 2017US$) Source: PENN WORLD TABLES Capstock Log of Capital stock at constant 2017 national prices (in mil. 2017US$) Source: PENN WORLD TABLES Table 2. Variable used for the index of infrastructure. Name Description of variables and sources Access_electricity_population Access to electricity (% of population) [EG.ELC.ACCS.ZS] source: WDI Air_transport_passengers Air transport, passengers carried [IS.AIR.PSGR] Source: WDI Individuals_using_internet Individuals using the Internet (% of population) [IT.NET.USER.ZS] source: WDI Table 3. Variable used for the index of governance. Name Description of variables and sources Control_corruption Control of Corruption: Estimate [CC.EST] Source: WGI Government_effectiveness Government Effectiveness: Estimate [GE.EST] Source: WGI Regulatory_quality Regulatory Quality: Estimate [RQ.EST] Source: WGI Voice_and_accountability Voice and Accountability: Estimate [VA.EST] Source: WGI 6 E. JEAN TONY divided based on sector of allocation its impact on productivity depends on the part of total factor production considered. Table 6 displays the results of unit root testing. The results showed that the null hypothesis of non-stationarity is rejected for all variables in the first difference. This finding suggests that all variables are integrated at first order I(1) or at level I(0) and thus, we can proceed with the estimations. Table 4. Descriptive statistics. Variable Obs Mean Std. Dev Min Max TFP growth 658 .007 .035 −.119 .455 ODA_net 1008 6.404 1.105 .963 10.001 Gov_fincons 908 5.047 9.326 −32.84 90.75 Health_exp 1029 195.893 219.167 6.654 1531.484 Infra_index 919 0 .98 −1.466 4.44 Gov_index 1035 0 1.438 −4.42 3.541 Remittances 1035 6.108 7.321 0 44.126 ODAsect_agri_f 1034 2.771 1.575 −2.526 6.591 ODAsect_ecoinfra 1034 3.8 1.934 −2.408 8.738 ODAsect_edu 1035 3.841 .976 .058 6.477 ODAsect_ene 1023 2.028 2.631 −4.605 7.381 ODAsect_indu 1033 1.092 1.68 −4.605 6.92 Emp 1035 22.191 58.699 .249 497.616 Realgdp 1035 11.691 1.552 8.202 16.031 Labsh 780 .481 .109 .212 .903 Capstock 1035 12.868 1.686 9.38 17.383 Hc 945 2.218 .571 1.117 3.581 Table 5. Correlation matrix. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (1) lnODA_net 1.000 (2) lnTFP_growth −0.044 1.000 (3) ln_gov_fincons 0.117 0.179 1.000 (4) ln_health_expend −0.403 −0.178 −0.142 1.000 (5) ln_infra_index −0.117 −0.062 −0.146 0.530 1.000 (6) ln_gov_index −0.112 −0.096 −0.052 0.505 0.388 1.000 (7) ln_remitances −0.015 0.193 −0.075 −0.239 −0.057 −0.193 1.000 (8) lnODAsect_agri_f 0.465 0.030 0.098 −0.393 −0.128 −0.028 −0.039 1.000 (9) lnODAsect_edu 0.548 −0.135 0.084 −0.181 0.222 0.056 −0.218 0.426 1.000 (10) lnODAsect_ene 0.477 −0.011 0.025 −0.108 0.197 0.153 −0.075 0.329 0.490 1.000 (11) lnODAsect_indu 0.411 −0.021 0.051 −0.058 0.169 0.209 −0.120 0.433 0.485 0.422 1.000 (12) lnODAsect_ecoinfra 0.543 −0.005 0.011 −0.192 0.205 0.219 0.003 0.416 0.500 0.763 0.510 1.000 (13) ln_emp 0.224 0.049 0.035 −0.076 0.414 0.134 −0.111 0.293 0.306 0.333 0.334 0.387 1.000 (14) Ln_hc −0.343 0.078 −0.163 0.445 0.505 0.201 0.378 −0.433 −0.282 −0.032 −0.045 −0.026 −0.070 1.000 (15) ln_labsh 0.086 0.027 0.033 0.117 0.011 0.308 0.154 0.152 −0.069 0.102 0.099 0.133 0.025 0.176 1.000 (16) ln_rgdp 0.154 −0.004 −0.018 0.158 0.670 0.206 −0.191 0.188 0.351 0.339 0.306 0.357 0.903 0.053 −0.051 1.000 (17) ln_capstock 0.122 −0.006 −0.043 0.165 0.684 0.192 −0.193 0.148 0.361 0.338 0.278 0.329 0.856 0.094 −0.029 0.971 1.000 Table 6. Unit root test (Augmented Dicky fuller). Variables At level First difference Order of integration lnODA_net (0.011) (0.000) I(0) lnTFP_growth (0.000) (0.000) I(0) ln_gov_fincons (0.005) (0.000) I(0) ln_health_expend (0.006) (0.000) I(0) ln_infra_index (0.687) (0.000) I(1) ln_gov_index (0.092)(0.000) I(0) ln_remitances (0.211) (0.000) I(1) lnODAsect_agri_f (0.000) (0.000) I(0) lnODAsect_edu (0.000) (0.000) I(0) lnODAsect_ene (0.027) (0.000) I(0) lnODAsect_indu (0.000) (0.000) I(0) lnODAsect_ecoinfra (0.000) (0.000) I(0) ln_emp (0.453) (0.000) I(1) Ln_hc (0.632) (0.026) I(1) Ln_labsh (0.000) (0.000) I(0) ln_rgdp (0.345) (0.000) I(1) ln_capstock (0.765) (0.000) I(1) p-Value between(): (),() ,()  significant at 10, 5, and 1%. COGENT ECONOMICS & FINANCE 7 real GDP. In addition, aid in economic infrastructure and services has a positive impact on human capital, employment, and capital stock. These results reflect how aid can improve total factor production if it’s well allocated. Moreover, this paper provides new insight into development assistance allocation to improve productivity and therefore for the realization of SDGs. Nevertheless, Wright and Winters (2010) conclude that international politics affects foreign aid distribution as well as the reliability of aid conditions. Moreover, Aboubacar et al. (2015) suggested that the effect of aid on growth depends on the sector in which it is distributed. However, Rajan and Subramanian (2011) concluded that foreign aid had an unfavorable impact on a country’s competitiveness. In addition, the authors found no evidence of aid effectiveness considering better policy or geographical environments, or that certain forms of aid work better than others. In the same vein, Quibria (2010) concluded that the mixed results on aid effectiveness can be traced to shared failures on the part of both the government and donors. 5. Conclusion The objective of this paper was to highlight the relationship between total factor production (TFP) and official development assistance (ODA) to allow a better allocation of aid while reducing its possible negative effects. To do so, we used a panel of 69 developing countries that we divided into 29 lowincome countries and 40 middle-income countries. We used two models, fixed effect and system GMM with robust standard errors to control for unobserved heterogeneity, omitted variable bias, and possible aid endogeneity. Those two models were used with time fixed effects and country fixed effects but also the aid variable was lagged in all models. For more precision, we divided ODA based on sector and TFP based on its main parts such as human capital; employment; share of labour income; real GDP; capital stock to assess the effect of aid on them. The findings showed that official development assistance had a negative and non-significant impact on total factor production. However, considering the set of all countries, aid in the sector of agriculture and aid in the sector of education had a positive impact on employment and capital stock while aid in education had a negative impact on the share of labour compensation. Aid in the sector of industry (industry, mining, and construction) and aid in the sector of economic infrastructure (economic infrastructure and services) have a positive impact on human capital and capital stock. Moreover, aid in the sector of economic infrastructure have a positive impact on employment. Given the positive impact of aid in different sectors, we recommend that donors reduce the conditions related to the granting of assistance while directing aid according to the factor related to productivity that we want to improve. Proper management of this aid must be the priority of the institutions of the receiving countries. Furthermore, for more precision in terms of aid orientation, we must consider the level of development of the country in question. Considering low-income countries, aid in the sector of agriculture had a positive impact on human capital, employment, and real GDP. Aid in the sector of industry had a positive impact on human capital, the share of labor compensation, and on the real GDP. Aid in those sectors (agriculture and industry) positively affects three of the determinants of total factor production. This emphasizes the importance of assistance in these sectors to optimize productivity. We recommend that donors direct most of the aid to low-income countries in these sectors which are the basis of these economies. Furthermore, policy makers in those countries must focus their development plans around these sectors (agropastoral, mining) because they are often better equipped with factors. Considering middle-income countries, aid in the sector of agriculture had a positive impact on employment. Aid in the sector of education had a positive impact on employment and capital stock while having a negative impact on the share of labor compensation. Aid in the sector of industry had a positive impact on capital stock while aid in the sector of energy had a negative impact on the latter. Aid in the sector of economic infrastructure had a positive impact on human capital, employment, and capital stock. As the results show, aid in the sector of economic infrastructure and services affects three determinants of total factor production. Moreover, the influence of aid in the industrial sector and that of aid in the agricultural sector on productivity is reduced. Additionally, the impact of aid in the education sector becomes effective. Thus, we realize that when countries are at a higher stage of development, improving the business climate (through economic infrastructure and services) and improving the quality of education play a 14 E. JEAN TONY central role in increasing productivity. We recommend that donors direct a significant part of their support to middle-income countries in the sectors of economic infrastructure and education. Furthermore, very good coordination between donors and institutions in receiving countries is a priority to adapt assistance to the policies of receiving countries. Nevertheless, this research has some limitations related to data availability in developing countries among other thing, leading to the use of secondary data and short study period. In regard of the paramount role played by ODA for developing countries, we recommend that future studies on the impact of aid focus on the effectiveness of aid through the assessment of the results of financed projects in the sectors stated above to optimize the positive effect of aid on productivity. Acknowledgments B. Evole Pierre and B. Regine for their tremendous moral support and encouragement throughout the research process. Disclosure statement No potential conflict of interest was reported by the author(s). About the author Ezako Jean Tony, Burundian citizen, holder of a bachelor’s degree from the Faculty of Economics and Management of the University of BURUNDI. Moreover, I am passionate about economic research with the aim of contributing to the development of my country, Burundi, and developing countries in general. 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Fiji, 22. Georgia, 23. Ghana, 24. Guatemala, 25. Haiti, 26. Honduras, 27. India, 28. Indonesia, 29. Iran, Islamic Rep., 30. Iraq, 31. Jordan, 32. Kazakhstan, 33. Kenya, 34. Kyrgyz Republic, 35. Lebanon, 36. Madagascar, 37. Malawi, 38. Malaysia, 39. Mali, 40. Mauritania, 41. Mexico, 42. Moldova, 43. Mongolia, 44. Morocco, 45. Mozambique, 46. Myanmar, 47. Namibia, 48. Nepal, 49. Nicaragua, 50. Nigeria, 51. Pakistan, 52. Philippines, 53. Rwanda, 54. Senegal, 55. Serbia, 56. South Africa, 57. Sri Lanka, 58. Tajikistan, 59. Tanzania, 60. Thailand, 61. Tunisia, 62. Turkiye, 63. Uganda, 64. Ukraine, 65. Uzbekistan, 66. Vietnam, 67. Zambia, 68. Zimbabwe, 69. Albania Low-income countries 1. Angola, 2. Benin, 3. Bhutan, 4. Burkina Faso, 5. Cameroon, 6. Congo, Dem. Rep., 7. Cote d‘Ivoire, 8. Dominican Republic, 9. El Salvador, 10. Ethiopia, 11. Ghana, 12. Guatemala, 13. Haiti, 14. Jordan, 15. Kenya, 16. Kyrgyz Republic, 17. Madagascar, 18. Malawi, 19. Mali, 20. Mauritania, 21. Mongolia, 22. Mozambique, 23. Namibia, 24. Rwanda, 25. Senegal, 26. Tanzania, 27. Uganda, 28. Zambia, 29. Zimbabwe Middle-income countries 1. Albania, 2. Argentina, 3. Azerbaijan, 4. Bangladesh, 5. Bosnia and Herzegovina, 6. Brazil, 7. Cambodia, 8. Colombia, 9. Costa Rica, 10. Ecuador, 11. Egypt, Arab Rep., 12. Fiji, 13. Georgia, 14. Honduras, 15. India, 16. Indonesia, 17. Iran, Islamic Rep., 18. Iraq, 19. Kazakhstan, 20. Lebanon, 21. Malaysia, 22. Mexico, 23. Moldova, 24. Morocco, 25. Myanmar, 26. Nepal, 27. Nicaragua, 28. Nigeria, 29. Pakistan, 30. Philippines, 31. Serbia, 32. South Africa, 33. Sri Lanka, 34. Tajikistan, 35. Thailand, 36. Tunisia, 37. Turkiye, 38. Ukraine, 39. Uzbekistan, 40. Vietnam COGENT ECONOMICS & FINANCE 17