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

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 low-income 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.

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 (+118%) 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-19-related 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 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, Świerczyńska 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: 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 in the 0.1 and 0.25 quantiles. Veiderpass (2015) using a balanced panel of 89 low- and middle-income 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 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, low- and middle-income 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. (1) $${\mathit{\text{TFPgrowth}}}_{\mathit{\text{it}}}={\beta }_0+{\beta }_1{\mathit{\text{ODA}}}_{i,t-1}+\rho X_{\mathit{\text{it}}}+{\gamma }_i+{\delta }_t+{\epsilon }_{\mathit{\text{it}}}$$(1)

In Equation (1)(1) $${\mathit{\text{TFPgrowth}}}_{\mathit{\text{it}}}={\beta }_0+{\beta }_1{\mathit{\text{ODA}}}_{i,t-1}+\rho X_{\mathit{\text{it}}}+{\gamma }_i+{\delta }_t+{\epsilon }_{\mathit{\text{it}}}$$(1) , ${\mathit{\text{TFPgrowth}}}_{\mathit{\text{it}}}$ is the growth of total factor production, ${\mathit{\text{ODA}}}_{i,t-1}$ is the lag of net official development assistance, $X_{\mathit{\text{it}}}$ is a vector of explanatory variables (government final consumption, health expenditure, remittances, infrastructure, and governance). ${\beta }_1,$ ${\beta }_0,$ and $\rho$ are parameters. Individuals fixed effects are denoted by ${ \gamma }_i$ and time fixed effect are denoted by ${\delta }_t.$

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. (2) $${\mathit{\text{TF}}\mathrm{P}\mathit{\text{growth}}}_{\mathit{\text{it}}}={\beta }_0+{\beta }_1{\mathit{\text{TFPgrowth}}}_{i,t-1}+{\beta }_2{\mathit{\text{ODA}}}_{i,t-1}+\rho X_{\mathit{\text{it}}}+{\delta }_t+{\epsilon }_{\mathit{\text{it}}}$$(2)

In Equation (2)(2) $${\mathit{\text{TF}}\mathrm{P}\mathit{\text{growth}}}_{\mathit{\text{it}}}={\beta }_0+{\beta }_1{\mathit{\text{TFPgrowth}}}_{i,t-1}+{\beta }_2{\mathit{\text{ODA}}}_{i,t-1}+\rho X_{\mathit{\text{it}}}+{\delta }_t+{\epsilon }_{\mathit{\text{it}}}$$(2) , ${\mathit{\text{TFPgrowth}}}_{\mathit{\text{it}}}$ is the growth of total factor production, ${\mathit{\text{TFPgrowth}}}_{i,t-1}$ is the lag of total factor production, ${\mathit{\text{ODA}}}_{i,t-1}$ is the lag of net official development assistance, $X_{\mathit{\text{it}}}$ is a vector of explanatory variables (government final consumption, health expenditure, remittances, infrastructure, and governance). ${\beta }_1,$ ${\beta }_0,$ ${\beta }_2,$ and $\rho$ are parameters. Time fixed effects are denoted by ${\delta }_t.$

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 WU-Hausman 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.

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

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: $$\frac{{\mathit{\text{RTFP}}}_{\mathit{\text{jt}}}^{\mathit{\text{NA}}}}{{\mathit{\text{RTFP}}}_{\mathit{\text{jt}}-1}^{\mathit{\text{NA}}}}=\frac{{\mathit{\text{RGDP}}}_{\mathit{\text{jt}}}^{\mathit{\text{NA}}}}{{\mathit{\text{RGDP}}}_{\mathit{\text{jt}}-1}^{\mathit{\text{NA}}}}/Q_{\mathit{\text{jt}},t-1}$$

Where, $$Q_{\mathit{\text{jt}},t-1}=\frac{1}{2}({\mathit{\text{LABSH}}}_{\mathit{\text{jt}}}+{\mathit{\text{LABSH}}}_{\mathit{\text{jt}}-1})\left(\frac{{\mathit{\text{EMP}}}_{\mathit{\text{jt}}}}{{\mathit{\text{EMP}}}_{\mathit{\text{jt}}-1}}\frac{{\mathit{\text{HC}}}_{\mathit{\text{jt}}}}{{\mathit{\text{HC}}}_{\mathit{\text{jt}}-1}}\right)+[1+\frac{1}{2}({\mathit{\text{LABSH}}}_{\mathit{\text{jt}}}+{\mathit{\text{LABSH}}}_{\mathit{\text{jt}}-1}\left)\right]\left(\frac{{\mathit{\text{RK}}}_{\mathit{\text{jt}}}^{\mathit{\text{NA}}}}{{\mathit{\text{RK}}}_{\mathit{\text{jt}}-1}^{\mathit{\text{NA}}}}\right)$$

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 t and t − 1, ln(rtfpna_t) − ln(rtfpna_t−1).

In Equation (1)(1) $${\mathit{\text{TFPgrowth}}}_{\mathit{\text{it}}}={\beta }_0+{\beta }_1{\mathit{\text{ODA}}}_{i,t-1}+\rho X_{\mathit{\text{it}}}+{\gamma }_i+{\delta }_t+{\epsilon }_{\mathit{\text{it}}}$$(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.

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

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 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.

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

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 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 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%.

Table 7 gives the results of the estimations on the impact of the model variables on the TFP and in particular on the impact of ODA on productivity. The first column displays the results of the preliminary estimation with 2SLS in other to assess the aid endogeneity. The Wu-Hausman test shows that variables are exogenous. In addition, the table shows the results for the model based on income level using the system GMM and Fixed Effect. The main constatation is that the results found using the different models are almost identical whether in terms of sign of the coefficients or in terms of significance. Furthermore, the AR(2) has a value >0.05 which shows the absence of autocorrelation but the Hansen test also has values >0.05 which shows that the instruments used are valid. Thus, we can consider the results found as robust.

Table 7. Short run impact of total ODA on TFP.

Variables	All panel low income middle income
	(2SLS)	(Syst GMM)	(Fixed Effect)	(Syst GMM)	(Fixed Effect)	(Syst GMM)	(Fixed Effect)
	TFP_growth	TFP_growth	TFP_growth	TFP_growth	TFP_growth	TFP_growth	TFP_growth
L.lnTFP_growth		0.185***		0.237***		0.083
		(0.000)		(0.000)		(0.323)
Lag_lnODA_net	−.0150	−0.001	−0.000	−0.002	0.002	−0.001	0.000
	(0.351)	(0.116)	(0.869)	(0.535)	(0.701)	(0.183)	(0.787)
Ln_Gov_fincons	.000***	0.000**	0.000***	0.001***	0.001***	0.000	0.000
	(0.001)	(0.023)	(0.000)	(0.000)	(0.000)	(0.572)	(0.682)
Ln_Health_exp	−.000	−0.000***	−0.000	−0.000**	0.000	−0.000**	−0.000
	(0.166)	(0.001)	(0.795)	(0.037)	(0.459)	(0.011)	(0.688)
Ln_infra_index	0.005*	0.002**	0.010**	0.016**	0.023	0.003	0.010**
	(0.057)	(0.032)	(0.019)	(0.036)	(0.449)	(0.138)	(0.034)
Ln_Gov_index		0.000	0.000	0.000	−0.010	0.001	0.005
		(0.725)	(0.931)	(0.681)	(0.175)	(0.433)	(0.289)
Ln_Remittances	0.000	0.000*	0.000	−0.000	0.001	0.001***	0.000
	(0.176)	(0.060)	(0.254)	(0.164)	(0.220)	(0.007)	(0.406)
Constant	0.098	0.020**	0.027*	0.042	0.014	−0.006	0.016
	(0.383)	(0.044)	(0.097)	(0.223)	(0.795)	(0.606)	(0.372)
Country fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R-squared	0.062		0.162		0.235		0.183
Number of country	69	69	69	29	29	40	40
AR(1)		0.000		0.015		0.000
AR(2)		0.646		0.388		0.205
Sargan test		0.084		0.374		0.080
Hansen test		0.742		0.597		0.651
WU-Hausman test	(0.452)

L. lag: ln logarithm.

Robust p-value in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

ODA has a negative and not significant impact on TFP in all models except for the fixed effect model in low- and middle-income countries. This reflects the fact that ODA reduces TFP. This negative effect of aid is consistent with what was found by Groß and Nowak‐Lehmann Danzinger (2022) who analyzed the impact of foreign aid on productivity in 27 recipient countries and found that in countries with low productivity, which is the case for developing countries, ODA has a negative impact on TFP. Moreover, this result is in line with Doucouliagos and Paldam (2009) who used 97 empirical studies with meta-analysis method to assess aid effectiveness. After 40 years, most of the results suggested that aid has not been effective.

In addition, Wright and Winters (2010) highlighted that international politics affects foreign aid distribution. The authors suggested that we need to focus on whether international politics restricts how aid money can be consumed and assess whether a recipient government thinks future development aid will be forthcoming to optimize aid effectiveness. Government consumption expenditure has a positive and significant impact on TFP in all the models except for middle-income countries in which this positive effect becomes not significant. This reflects the important role that developing country governments must play in increasing TFP by taking on tasks in areas where there is no private investment. This result is consistent with the findings of Su and Nguyen (2021).

Current health expenditure has a significant negative impact on TFP in all income levels except for the fixed effect model in which this negative effect becomes insignificant. This may reflect the fact that these expenses constitute an additional burden on economic agents in developing countries, who in addition to their relatively low incomes have to allocate a large share of the latter to health care. This result is in line with the findings of Rizvi (2019). The infrastructure index has a positive and significant impact on TFP. This reflects the important role played by infrastructure in endogenous growth models in explaining growth but also in explaining differences between countries.

Infrastructure allows to get the most out of physical or labor capital, among others by improving the mobility of the latter. This result is in line with the findings of Mitra and Sharma (2022). Personal remittances have a positive and significant impact on TFP considering all the panels which becomes not significant if we consider the level of income. This positive effect reflects the share of international transfers in the improvement of the TFP, by the fact that an increase in these transfers allows economic agents to have more funds for investment. This result is in line with the findings of Makhlouf (2019). In addition, governance has a non-significant positive impact on the TFP.

This relationship between the TFP and the different variables of the model was evaluated in the long run to see their interactions. This long-run relationship is evaluated only for significant coefficients of the System GMM model.

Table 8 shows that all the coefficients that were significant in the short run remained significant in the long run. Thus, current expenditure on health has a significant and negative impact on TFP in the long run no matter the income level, even if this effect is relatively small. Public expenditure, infrastructure, and international individual transfers have a positive and significant impact on TFP in the long term. These findings show that whatever the level of income a country has, the government must put in place politics allowing to lower the burden on economic agents to increase the TFP in long run.

Table 8. Long run coefficients for significant variables.

	All countries	Low income	Middle income
Lag_lnODA_net
Ln_Gov_fincons	.000**	.001***
Ln_Health_exp	−.000***	−.000**	−.000***
Ln_Infra_ind	.003**	.022**
Ln_Gov_index
Ln_remittances	.000*		.001***

Coefficient significant at 0.01, 0.05, 0.1 with *** when pvalue<0.01, ** when pvalue<0.05, * when pvalue<0.1.

However, it is possible that this negative and not significant impact of ODA on TFP is due to the fact that ODA and TFP were taken as aggregate. Thus, the ODA was divided into sectors to assess the effects of aid in these different sectors on the main parts of TFP such as employment; human capital; share of labour compensation; real GDP; Capital stock. We also divided the sample based on income level for more clarity.

Table 9 shows the results for the estimation of aid based on sectors on the main parts of TFP except for Real GDP. The estimate of aid on the latter yield second order correlation and was excluded. The estimate on human capital index showed that aid in industry, mining, and construction; aid in economic infrastructure and services have a positive and significant effect. This result is in line with Tasinda et al. (2021) who found that aid had a positive effect on human capital. This result shows the essential role played by aid in those sectors that facilitate the transfer of technology and thus increase human capital and hence factor productivity.

Table 9. ODA by sector on main parts of TFP (all countries).

Variables	(syst GMM)	(Fixed effect)	(syst GMM)	(Fixed Effect)	(Syst GMM)	(Fixed effect)	(Syst GMM)	(Fixed Effect)
	lnHc	lnHc	lnEmp	lnEmp	lnLabsh	lnLabsh	lnCapstock	lnCapstock
Lag_lnhc	0.988***
	(0.000)
Lag_lnODAsect_agri_f	0.000	−0.002	0.085***	0.165	0.002	0.000	0.002	0.013***
	(0.516)	(0.424)	(0.006)	(0.112)	(0.222)	(0.746)	(0.136)	(0.004)
Lag_lnODAsect_edu	0.000	0.006	0.095***	0.375*	−0.001	−0.007**	0.0111***	−0.030***
	(0.967)	(0.307)	(0.007)	(0.071)	(0.672)	(0.018)	(0.004)	(0.000)
Lag_lnODAsect_ene	0.000	−0.002	0.009	−0.011	−0.000	0.000	−0.000	−0.000
	(0.119)	(0.211)	(0.502)	(0.852)	(0.708)	(0.388)	(0.911)	(0.753)
Lag_lnODAsect_indu	0.001*	0.005**	−0.021	−0.034	0.000	−0.000	0.004***	0.005
	(0.053)	(0.028)	(0.647)	(0.668)	(0.305)	(0.800)	(0.006)	(0.112)
Lag_lnODAsect_ecoinfra	−0.000*	0.007***	0.027	0.248***	0.000	−0.000	0.003**	0.001
	(0.097)	(0.007)	(0.372)	(0.009)	(0.774)	(0.922)	(0.010)	(0.678)
Ln_Gov_fincons	0.000*	0.000	0.000	−0.014	−0.000*	0.000***	0.000	0.000
	(0.093)	(0.526)	(0.833)	(0.147)	(0.071)	(0.000)	(0.736)	(0.212)
Ln_Health_exp	0.000**	0.000	0.000	−0.005***	0.000*	0.000***	−0.000	−0.000**
	(0.044)	(0.170)	(0.938)	(0.000)	(0.076)	(0.000)	(0.911)	(0.037)
Ln_infra_index	0.003	0.019**	0.087	4.693***	−0.002	0.010**	0.056***	0.002
	(0.306)	(0.038)	(0.444)	(0.000)	(0.158)	(0.039)	(0.000)	(0.867)
Ln_gov_index	0.000	−0.034***	−0.062**	−0.022	0.002	−0.009**	−0.014***	−0.021*
	(0.924)	(0.000)	(0.036)	(0.939)	(0.161)	(0.033)	(0.003)	(0.099)
Ln_Remittances	0.000*	−0.000	−0.009**	−0.0554	0.000	−0.000	−0.003***	0.000
	(0.054)	(0.846)	(0.018)	(0.295)	(0.384)	(0.952)	(0.000)	(0.756)
Lag_ln_emp			1.006***
			(0.000)
Lag_ln_labsh					0.891***
					(0.000)
Lag_ln_realgdp
Lag_ln_capstock							0.954***
							(0.000)
Constant	0.035	2.086***	−0.461**	24.08***	0.0455	0.486***	0.594***	12.96***
	(0.218)	(0.000)	(0.0295)	(0.000)	(0.139)	(0.000)	(0.000)	(0.000)
Country fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R-squared		0.669		0.482		0.155		0.749
Number of country	69	69	69	69	69	69	69	69
AR(1)	0.070		0.236		0.000		0.047
AR(2)	0.614		0.369		0.389		0.577
Sargan test	0.128		0.247		0.000		0.025
Hansen test	0.862		0.621		0.422		0.345

L. lag: ln logarithm.

Robust p-value in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

However, this result is the opposite of what was found by Nwani (2021). The author examined the interactive role of human capital development (HCD) in foreign aid-growth relations in South Asia and sub-Saharan Africa countries from 1985 to 2019. The results showed that foreign aid and HCD had negative impacts on economic growth. Nevertheless, he found that the interaction of human capital with foreign aid reduced the extent to which foreign aid impeded economic growth. The estimate on employment shows that aid in agriculture, fishing, and forestry; aid in education have a positive and significant effect. The same result was found by Mueller (2022) for aid on employment. This is consistent with the reality of developing countries where most of the population lives from activities in the primary sector and aid in this sector logically contributes to the growth of factor productivity through the reduction of unemployment.

The estimate on capital stock shows that aid in industry, mining and construction; aid in economic infrastructure and services; aid in education have a positive and significant effect. This result is consistent with Orji et al. (2019) who found a positive impact of aid on capital formation in Nigeria. Obviously aid in the field of industry and that of economic infrastructure, given the dependence of developing countries on natural resources, allows the increase in the production of factors through the capital stock growth. However, Wafula et al. (2019) showed that the effect of foreign aid on capital formation is mitigated depending on the type of aid considered. The results they found suggested that multilateral aid has a positive and not significant effect on capital formation while bilateral aid retards capital formation in the long run but enhances it in the short run during the first year. The estimate on share of labor compensation shows that there is no significant effect of aid.

Table 10 shows the results for the estimation of aid based on sectors on the main parts of TFP except capital stock. The estimate of aid on the latter yield second order correlation and was excluded. The result shows that aid in agriculture, fishing, and forestry has a positive and significant effect on the human capital index. The same result was found by El Namrouty et al. (2022) for aid on human capital. In most of low-income countries agriculture, fishing, and forestry are the main resources of income for most households. Thus, aid in these paramount sectors helps increase household income and thereby allows households to finance education, which is one of the engines of human capital growth and thus of total factor productivity.

Table 10. Aid by sector on main parts of TFP in low-income countries.

Variables	(Syst GMM)	(Fixed effect)	(Syst GMM)	(Fixed effect)	(Syst GMM)	(Fixed effect)	(Syst GMM)	(Fixed effect)
	lnHc	lnHc	lnEmp	lnEmp	lnLabsh	lnLabsh	lnRealgdp	lnRealgdp
Lag_lnhc	1.040***
	(0.000)
Lag_lnODAsect_agri_f	0.002*	−0.000	0.007	0.195*	0.000	0.004	0.003	0.012**
	(0.083)	(0.831)	(0.349)	(0.053)	(0.672)	(0.109)	(0.481)	(0.044)
Lag_lnODAsect_edu	−0.000	0.007	0.018	−0.133	0.000	−0.003	0.001	−0.014
	(0.727)	(0.268)	(0.365)	(0.383)	(0.625)	(0.439)	(0.840)	(0.100)
Lag_lnODAsect_ene	−0.000	−0.000	0.001	0.055	−0.000	0.000	−0.000	0.002
	(0.439)	(0.726)	(0.850)	(0.243)	(0.745)	(0.932)	(0.743)	(0.437)
Lag_lnODAsect_indu	−0.000	0.0058*	−0.013	0.080	0.002*	0.001	0.004**	0.014***
	(0.308)	(0.055)	(0.264)	(0.270)	(0.083)	(0.353)	(0.012)	(0.000)
Lag_lnODAsect_ecoinfra	−0.000	0.000	−0.013	−0.035	0.000	−0.000	0.000	0.003
	(0.147)	(0.769)	(0.285)	(0.654)	(0.489)	(0.989)	(0.959)	(0.510)
Ln_Gov_ln_fincons	0.000	0.000	−0.000	0.002	−0.000	−0.000	0.000***	0.000
	(0.752)	(0.598)	(0.829)	(0.741)	(0.490)	(0.450)	(0.000)	(0.794)
Ln_Health_exp	−0.000	0.000*	0.000	−0.008**	0.000	−0.000	−0.000	−0.000
	(0.267)	(0.081)	(0.488)	(0.034)	(0.630)	(0.143)	(0.580)	(0.727)
Ln_infra_index	−0.017*	0.032	0.018	0.067	0.001	0.070**	0.022	0.016
	(0.077)	(0.440)	(0.709)	(0.938)	(0.768)	(0.025)	(0.205)	(0.754)
Ln_gov_index	0.003	0.019*	0.029	−0.666***	−0.001	−0.026***	−0.005	0.048***
	(0.107)	(0.065)	(0.164)	(0.006)	(0.220)	(0.000)	(0.454)	(0.000)
Ln_Remittances	−0.000	0.006***	0.002	−0.057	−0.000	−0.004***	−0.001*	0.004
	(0.846)	(0.001)	(0.407)	(0.242)	(0.243)	(0.000)	(0.071)	(0.129)
Lag_ln_Emp			1.045***
			(0.000)
Lag_ln_labsh					0.971***
					(0.000)
Lag_ln_realgdp							0.983***
							(0.000)
Lag_ln_capstock
Constant	−0.063***	1.762***	−0.149	8.250***	0.012	0.580***	0.249	10.49***
	(0.008)	(0.000)	(0.155)	(0.000)	(0.408)	(0.000)	(0.201)	(0.000)
Country fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R-squared		0.747		0.505		0.275		0.888
Number of country	29	29	29	29	29	29	29	29
AR(1)	0.062		0.029		0.032		0.009
AR(2)	0.673		0.337		0.158		0.748
Sargan test	0.250		0.014		0.360		0.217
Hansen test	0.452		0.286		0.614		0.378

L. lag: ln logarithm.

Robust p-value in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

However, Ahlerup (2019) who studied whether foreign aid is a factor that helps or hinders structural transformation found mitigated results. The findings suggested that; first where aid projects were being implemented appeared to become, on several metrics, less developed; second aid had a negative effect on wages and household expenditures; third aid had a negative short-term effect on the local economic structure by depressing modern sectors and encouraging the traditional agricultural sector.

The estimate on the share of labor compensation and real GDP shows that aid in industry, mining, and construction has a positive and significant effect. This result on GDP is in line with Mishra and Aithal (2021) who found that aid contributes to increasing GDP. This reflects the fact that developing countries are often dependent on the exploitation of natural resources and basic industry. The exploitation of these natural resources constitutes an important part of national income and real GDP. Thus, aid in the industry and mining sectors boosts the profitability of this sector and thereby leads to an increase in the share of labor compensation through an increase in national income and real GDP.

Table 11 shows the results for the estimation in middle income of aid based on sectors on the main parts of TFP except on real GDP. The estimate of aid on the latter yield second order correlation and was excluded. The estimate on human capital shows that aid in agriculture, fishing, and forestry has a negative and marginally significant effect. This result is in contradiction with Tasinda et al. (2021) who found that aid had a positive effect on human capital. The estimate on employment shows that aid in agriculture, fishing, and forestry; aid in education have a positive and significant effect. The result is in line with Mueller (2022) who found that aid had a positive effect on employment. The estimate on capital stock shows that aid in industry, mining, and construction; aid in economic infrastructure and services; aid in education have a positive and significant effect while aid in energy has a negative and significant impact on capital stock. This result is consistent with Orji et al. (2019) who found a positive impact of aid on capital formation.

Table 11. Aid by sector on main parts of TFP in middle-income countries.

Variables	(Syst GMM)	(Fixed effect)	(Syst GMM)	(Fixed effect)	(Syst GMM)	(Fixed effect)	(Syst GMM)	(Fixed effect)
	Ln_Hc	Ln_Hc	Ln_Emp	Ln_Emp	Ln_Labsh	Ln_Labsh	Ln_Capstock	Ln_Capstock
Lag_hc	0.929***
	(0.000)
Lag_lnODAsector_agri_f	−0.003*	−0.000	0.092**	0.176	0.002	−0.000	0.002	0.003
	(0.083)	(0.961)	(0.015)	(0.236)	(0.216)	(0.696)	(0.109)	(0.425)
Lag_lnODAsect_edu	−0.005	−0.000	0.118**	0.828**	−0.002	−0.011***	0.009**	−0.015
	(0.278)	(0.989)	(0.020)	(0.020)	(0.460)	(0.009)	(0.040)	(0.177)
Lag_lnODAsect_ene	−0.000	−0.002	0.028	−0.000	−0.000	0.000	−0.002**	−0.004
	(0.990)	(0.436)	(0.278)	(0.996)	(0.747)	(0.641)	(0.017)	(0.207)
Lag_lnODAsect_indu	0.000	0.002	−0.052	−0.036	0.000	−0.001	0.002*	0.001
	(0.396)	(0.419)	(0.446)	(0.755)	(0.691)	(0.428)	(0.051)	(0.634)
Lag_lnODAsect_ecoinfra	−0.000	0.009**	0.020	0.388***	−0.000	0.000	0.004***	0.001
	(0.345)	(0.016)	(0.648)	(0.007)	(0.942)	(0.841)	(0.003)	(0.781)
Gov_fincons	−0.000**	0.000	0.002	−0.029*	−0.000**	0.001***	0.000	0.000
	(0.045)	(0.683)	(0.408)	(0.091)	(0.033)	(0.000)	(0.322)	(0.833)
Health_exp	0.000**	0.000	−0.000	−0.005***	0.000*	0.000***	−0.000	−0.000
	(0.011)	(0.478)	(0.921)	(0.005)	(0.096)	(0.000)	(0.246)	(0.657)
infra_index	0.010*	−0.006	0.070	5.043***	−0.000	0.018***	0.032***	0.082***
	(0.074)	(0.601)	(0.658)	(0.000)	(0.895)	(0.001)	(0.000)	(0.000)
gov_index	0.002	−0.091***	−0.036	0.926*	0.002*	−0.000	−0.009***	0.005
	(0.495)	(0.000)	(0.313)	(0.059)	(0.092)	(0.967)	(0.009)	(0.713)
Remittances	0.001*	−0.004**	−0.011*	−0.055	0.000	0.002**	−0.003***	0.000
	(0.088)	(0.020)	(0.072)	(0.464)	(0.402)	(0.018)	(0.000)	(0.764)
Lag_emp			1.005***
			(0.000)
Lag_labsh					0.900***
					(0.000)
Lag_realgdp
Lag_capstock							0.967***
							(0.000)
Constant	0.200***	2.351***	−0.356	31.21***	0.0405	0.467***	0.458***	13.87***
	(0.000)	(0.000)	(0.182)	(0.000)	(0.181)	(0.000)	(0.000)	(0.000)
Country fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R-squared		0.687		0.512		0.267		0.735
Number of country	40	40	40	40	40	40	40	40
AR(1)	0.0470		0.234		0.002		0.043
AR(2)	0.332		0.356		0.113		0.517
e(sargan)	0.158		0.211		0.001		0.254
e(hansen)	0.796		0.457		0.657		0.712

L. lag: ln logarithm.

Robust p-value in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

Moreover, these relationships between the main parts of TFP and ODA based on the sector were assessed in the long run to see their interactions. This long-run relationship is evaluated only for significant coefficients of the System GMM model.

Table 12 shows the results for the long run coefficients in the panel. The results show that the effect of aid in education on employment becomes negative and significant in long run. This may be due to the fact that if aid in the education sector is not oriented so that it is consistent with the needs of the labor market it may lead to an increase in unemployment. Moreover, aid in industry, mining and construction; aid in economic infrastructure and services; aid in education kept their positive and significant effect on capital stock in long run.

Table 12. Long run coefficients for significant variables (all countries).

	HC	Emp	Capstock
Lag_lnODAsect_agri_f		−15.169
Lag_lnODAsect_edu		−16.913*	.241***
Lag_lnODAsect_ene
Lag_lnODAsect_indu	.087		.097***
Lag_lnODAsect_ecoinfra	−.066		.084***

Coefficient significant at 0.01, 0.05, 0.1 with *** when p value<0.01, ** when p value<0.05, * when p value<0.1.

Table 13 shows the results for the long run coefficients in low-income countries. In one hand, Aid in agriculture, forestry, and fishing kept a negative and significant effect on human capital in long run. On the other hand, aid in industry, mining, and construction became insignificant on real GDP and share of labor compensation.

Table 13. Long run coefficients for significant variables (low income).

	HC	Labsh	Realgdp
Lag_lnODAsect_agri_f	−.057*
Lag_lnODAsect_edu
Lag_lnODAsect_ene
Lag_lnODAsect_indu		.084	.270
Lag_lnODAsect_ecoinfra

Coefficient significant at 0.01, 0.05, 0.1 with *** when p value<0.01, ** when p value<0.05, * when p value<0.1.

Table 14 shows the results for the long run coefficients in middle-income countries. The results show that the effect of aid in education on employment becomes negative and significant in long run. Moreover, aid in industry, mining and construction; aid in economic infrastructure and services; aid in education kept their positive and significant effect on capital stock in long run. In addition, aid in energy kept a negative and significant effect on capital stock in long run.

Table 14. Long run coefficients for significant variables (middle income).

	HC	Emp	Capstock
Lag_lnODAsect_agri_f	.012	9.896
Lag_lnODAsect_edu		−22.462*	.271**
Lag_lnODAsect_ene			−.069*
Lag_lnODAsect_indu			.087**
Lag_lnODAsect_ecoinfra			.126***

Coefficient significant at 0.01, 0.05, 0.1 with *** when pvalue<0.01, ** when pvalue<0.05, * when pvalue<0.1

4.1. Discussion of the results

The findings showed that official development assistance had a negative and non-significant impact on total factor production. This result is in line with Bird and Choi (2020) who investigated the effectiveness of external sources of finance in engendering economic growth. The authors found no clear effect of foreign aid on economic growth for 51 developing countries. The effectiveness of aid is a highly debated topic. Mosley et al. (1990) suggested that aid is ineffective if it encourages mismanagement and benefits only a certain oligarchy in developing countries at the expense of population needs. Mbah and Amassoma (2014) showed that aid had a negative effect on the government’s decisions in recipient countries. However, the effectiveness of aid depends on numerous factors. Guillaumont and Chauvet (2001) suggested that aid effectiveness depends on not only on the quality of institutions and economic policies but also on what that aid finance.

Most of the studies on aid effectiveness focused on the link between aid and economic growth or poverty reduction. Hirano and Otsubo (2014) found that social aid (education, health water, and sanitation spending) and economic aid (transportation, energy and communication, and financial infrastructure spending) significantly increase economic growth and reduce income inequalities. Arndt et al. (2015) found that aid improve economic growth and social welfare such as education, employment, and life expectancy. Moreover, the authors found that aid could raise investment. However, a limited number of studies have analyzed the impact of aid on productivity but didn’t consider the impact of aid by sector on the main parts of total factor production.

Thus, to go deeper in this study, mainly focusing on aid allocation, we divided official development assistance based on sectors to assess their effect on determinants of total factor production. The results found showed that aid in agriculture has a positive and significant impact on human capital and employment while aid in education had a positive impact on employment and capital stock. Aid in the sector of industry, mining, and construction has a positive impact on capital stock, human capital, and 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 low-income 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 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).

Additional information

Notes on contributors

Ezako Jean Tony

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. Thus, this paper on ‘Analysis of Official Development Assistance and Total Factor Production relationship in developing countries’ is part of a research project consisting of four papers that I initiated to focus on the role of financial economics in the development process. The other three papers are; analyze of inflation and economic growth relationship in Burundi: The nexus between human development, ODA, carbon emissions and governance in developing countries: analyze of globalization and total factor production in developing countries.

Appendix A

Table A1. Country name.

Country name

1. Angola, 2. Argentina, 3. Azerbaijan, 4. Bangladesh, 5. Benin, 6. Bhutan, 7. Bosnia and Herzegovina, 8. Brazil, 9. Burkina Faso, 10. Cambodia, 11. Cameroon, 12. Colombia, 13. Congo, Dem. Rep., 14. Costa Rica, 15. Cote d‘Ivoire, 16. Dominican Republic, 17. Ecuador, 18. Egypt, Arab Rep., 19. El Salvador, 20. Ethiopia, 21. 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