Does Aid Fragmentation Affects Sustainability and Growth: The Case of Nepal

Abstract

Nepal’s strategic position and open border with India and China have attracted unusually high attention, particularly during the Cold War era, not only from these two large neighbours but also from European countries, the USA and the former Soviet Union. However, despite decades of aid inflows, Nepal remains one of the poorest countries. While debate over aid–growth nexus remains unsettled, our empirical results do suggest that aid fragmentation is detrimental to growth, perhaps due to increased administrative burden to manage a large number of small projects and meet reporting requirements of the donor community. While we find that aid is good for growth, attracting uncoordinated and fragmented aid without the need for assessment can, in fact, do more harm than good. These findings point to the importance of coordinated aid approach not only at the country level but also among the donor community.

Keywords

Foreign aid aid fragmentation poverty economic growth

Introduction

The relationship between foreign aid and economic growth in developing countries remains controversial despite over half a century of its history. Some studies find that aid is good for growth, while others find no evidence of aid effectiveness. By and large, there is overwhelming evidence to suggest that aid retards growth.¹ One of the reasons for aid ineffectiveness appears to be linked with aid fragmentation and donor proliferation, which not only increases the risk of duplication in activities but also creates high transaction costs, making aid ineffective (Annen & Moers, 2017; Bürcky, 2011).² Both aid fragmentation and donor proliferation put a considerable pressure on administrative resources in recipient countries, where these are scarce and would be better employed elsewhere. Having to deal with a plethora of donor missions and their requirements considerably reduces the value of aid for recipient countries (Panday, 1999). Consequently, reducing aid fragmentation and donor proliferation have been high on the donors’ policy agenda and have been prominently featured in the 2005 Paris Declaration and the 2008 Accra Agenda.³ Unfortunately, despite this recognition, aid fragmentation and donor proliferation have rapidly increased (Klingebiel et al., 2016; Nunnenkamp et al., 2016; Ohler, 2013). As Annen and Moers (2017) correctly point out, in 1960, there were less than 3 bilateral donors in a typical developing country, which rose to almost 23 by 2011. The higher number of donors are new bilateral donors from the South (countries such as India, China, Poland, Lithuania, Brazil and South Africa) and the emergence of philanthropic groups, such as Bill & Melinda Gates Foundation, Open Society Foundations and others. While donors are aware of the need for greater coordination among themselves, it is increasingly evident that no one likes to be coordinated, resulting in continuation of aid fragmentation and donor proliferation.

While development implications of aid fragmentation have received some attention in recent years, empirical findings are mixed. For instance, Gehring et al. (2017) conclude that ‘the generally expected negative effect of fragmentation is less robust than commonly assumed’, while Djankov et al. (2009) find that ‘aid fragmentation is detrimental to growth’. The contradicting findings of these cross-sectional studies are not surprising, given that they lump together countries from different backgrounds, which naturally ignore country-specific settings. Since developing countries differ significantly among themselves what may be true for one country may not hold for another. This problem associated with the cross-sectional studies can be overcome through a case-study approach, considering each country’s structural features and the policy history (Sharma, 2015; Sharma & Bhattarai, 2013). Unfortunately, such studies are sparse.⁴

Our aim in this article is to redress this gap by undertaking an in-depth study of Nepal, which has been one of the largest aid recipients in South Asia. Development assistance has been an important part of its annual budget.⁵ Nepal’s strategic position and open border with India and China have attracted unusually high attention not only from these two large neighbours but also from European countries, the USA and the former Union of Soviet Socialist Republics (USSR), leading to huge aid inflows in a range of activities.

As discussed in more detail later in the text, aid flows to Nepal has been largely dictated by donors’ strategic interests rather than the country’s development needs. Aid fragmentation and donor proliferation have been a key feature of development assistance to Nepal, putting considerable pressure on its already weak institutions. Consequently, despite decades of aid inflows, it remains a poor country (Sharma, 2006, 2020). In this study, using historical data from Nepal, we investigate whether aid fragmentation is detrimental to growth.

The rest of the article is organized as follows: the second section presents an analytical context to place the study in the context. In the third section, we estimate aid fragmentation in Nepal, develop a model, which is empirically estimated, and results are discussed. The article concludes with policy remarks in the fourth section.

The Context: Aid Flows to Nepal

The Context

Aid fragmentation and donor proliferation are not new in Nepal. During the Cold War era, not only two largest neighbours—India and China—but also the USA, the former USSR and several European countries offered aid to Nepal. With a few large infrastructure, health and rural development projects, most foreign-aided projects were small and scattered in different parts of the country to serve strategic and commercial interests of bilateral donors. Being an aid-dependent country, Nepal has always been in a weak position to negotiate or even refuse aid offers with fears of losing some of its assistance altogether, while the donor community often ignored its aid absorption capacity and development needs. This resulted in the acceptance of many projects, which were not in line with the country’s needs.⁶ Ironically, ruling elites did not mind accepting any kind of aid simply because such assistance enabled them to reward their own supporters (Bienen et al., 1990). Consequently, it not only put considerable burden on those who managed these projects at the country level but also perpetuated corrupt behaviour, significantly undermining institutions rather than building them, which are sine qua non for growth. Lacklustre growth even led to the eruption of civil war in the county in early 2000 that lasted for over a decade (Figure 1).

With the help of development assistance, while some progress has been made in developing key infrastructure and social service sectors, they have neither addressed Nepal’s basic economic problems nor have contributed to a solid foundation for development. Massive investments in these sectors (nearly 70% of the total aid between 1975 and 2015) have served only about 7% of the population and, of course, provided opportunity to bilateral donors (namely, India and China) to improve their access to Nepal for strategic reasons, if required. Huge aid flows caused a heavy financial burden on the government budget for maintenance and repairs. In the absence of maintenance funds, most of these infrastructures are not regularly maintained, resulting in significant economic inefficiencies (see, Khadka, 1991). A similar experience is recorded in the agricultural sector, which is the backbone of the economy in terms of its contribution to the economy and rural employment, but its impact has also been tempered by inefficient allocation.⁷ As correctly point out that the aid for agriculture has been invested (rather wastefully) in relatively accessible locations and used for purchasing expensive vehicles and constructing high-quality buildings to house staff and experts rather than for benefitting poor farmers in rural locations. Consequently, Nepal’s human development index remains very low despite over one-fifth of aid flows to social services sector during the period from 1975 to 2015. For instance, over 60% of the population is illiterate, the incidence of water borne disease is high and rising, and around half of the population does not even have access to safe drinking water. Here, a major problem is that once a project is handed over to the government, it runs into financial trouble mainly due to mismanagement. No one is accountable and questioned for the failure. Once a hospital is established or a water supply system is developed, it should not be too difficult to run and maintain these services, but this appears to be the case in Nepal as the government is simply not interested to reform the system or broaden the tax base to sustain the life of development projects (Panday, 2000). Neither the autocratic regime of the past decades nor the democratic governments of the recent years have taken bold decisions to undertake reforms to sustain and maintain infrastructure and service projects developed under aid programmes. Instead of cleaning its own backyard through reforms in institutions and governance, the ruling elites are simply interested in accumulating more money through aid and rewarding their supporters, further lowering the quality of institutions and governance (Sharma, 2011). As Khadka (1991) correctly points out, proliferation of projects established through aid programmes has failed to alleviate poverty in the absence of institutional reforms.

Figure 1.

Source: Estimated by the authors based on data from OECD online database.

Recently, several cross-sectional studies have demonstrated that aid fragmentation reduces effectiveness of aid programmes by putting pressure on administrative capabilities in recipient countries. Knack and Rahman (2007) find that aid fragmentation reduces bureaucratic quality in recipient countries as more aid agencies poach qualified staff, resulting in ineffectiveness of aid programmes, while Annen and Kosempel (2009) and Djankov et al. (2009), using an interaction term between aid flows and aid fragmentation in an aid-growth equation, have shown that aid effectiveness is significantly reduced due to fragmented aid.

Historical Setting and Current Trends in Aid Flows to Nepal

Nepal has been a major aid recipient from bilateral and multilateral sources for several decades. Among bilateral sources, India, the USA, the UK, Canada, Switzerland and China are the major donors, together accounting for over 90% share in the total bilateral aid to Nepal by 2006.⁸ The USA was the first Western country to engage in Nepal mainly to keep the communist influence out of the country. To achieve this goal, it offered budgetary support, helped construct the East–West High highways, executed rural development projects and supported agricultural development and extension programmes. In addition, the USA also sent thousands of economic and policy advisors to run government departments as well as Peace Corps volunteers to rural Nepal to drive Chinese influence out of the country (Sharma & Bhattarai, 2013). By the mid-1960s, the USA was a major donor, and it alone contributed over 50% of the total development assistance to Nepal.

Indian aid began to flow, following the invasion of Tibet and Aksai Chin (an Indian territory) by China in September 1949, which made India suspicious about China’s expansionist policy and a possible security threat through the Nepalese soil. Consequently, India intensified its aid programme, especially in developing transport and communication network in Nepal. It channelled a significant amount of aid in developing highways (between the key Indian borders and the major Nepalese towns), developed communication systems and constructed airports in strategic locations, including in Kathmandu (Mihaly, 1965). By the late 1960s, India became Nepal’s major development partner despite being an aid-recipient country itself.

With the increased Indian presence in the Nepalese soil, China and the former Soviet Union began intensifying their influence from the early 1970s by setting up several import-substitution industries in the country, including cement, cigarette, textile, sugar, shoes and modern brick factories with a view to reduce Nepal’s economic dependence on India. Furthermore, China took interest in developing highways (namely Arnica and Kodari Highways), connecting Tibet with Kathmandu, and it offered commodity aid for nearly two decades to reduce Nepal’s import dependence on India. Not only in the past but even today both China and India compete to achieve their strategic goals by increasing development assistance to Nepal (Bhatta, 2018).

In addition to attracting aid from two regional powers (India and China) and two superpowers (the USA and USSR), Nepal has also received aid from philanthropic groups and several non-governmental organizations (NGOs) and international non-governmental organizations (INGOs). For instance, from 1977 to 2014, a number of registered NGOs and INGOs surged from 189 to 39,759 (Karki & Comfort, 2016). Similarly, the total number of bilateral donors rose from 5 in the 1950s to 26 by the 2000s (see Figure 2). This has led to an increase in per capita aid (in constant price), from about US$6 in 1960 to US$37 in 2016 (Figure 3), but most funded projects were smaller, with a few large infrastructure projects. For example, in 2004–2005 alone, there were 76 foreign-aided projects in several sectors, with an average investment of about US$2.1 million. On an average, each development partner was engaged with nine counterpart agencies/ministries, and some were even involved in more than 20 ministries/agencies (Government of Nepal, 2016/2017b, pp. 22–23).⁹ This, together with significant fluctuations in aid flows between the years, as shown in Figure 4, created a greater degree of uncertainty in project planning and implementation.

There is widespread perception that the proliferation of the donor community has created considerable administrative burdens and weakened governance, resulting in aid ineffectiveness in Nepal (Sharma, 2006). As shown in Figure 3, there have been significant fluctuations in per capita aid between the years surging sharply until 1988 and then plunging before rising again in 2015, in response to quick recovery and reconstruction.¹⁰

Figure 2.

Source: Computed by the authors based on OECD database.

Figure 3.

Source: Computed by the authors based on WDI and OECD online databases.

Over the years, foreign aid percentage of Nepal’s GDP increased from less than 2% of GDP in the late 1960s to over 10% by the end of the 1990s (Table 1). A large proportion of development assistance has gone into transport, power and communication sectors, attracting over 47% of the total aid during the period from 1975 to 2015 (Table 2). This was followed by social services—which included health, education and drinking water—and agriculture, attracting 22.9% and 22.7%, respectively, each in the same period.

Table 1.

Total Aid, Bilateral Aid and Grants: 1960–2015.

Year	Total Aid (% of GDP)	Bilateral Aid (% of total aid)	Grants (% of total aid)
1960–1969	1.86	96.65	99.89
1970–1979	3.87	68.24	71.38
1980–1989	10.10	54.65	64.17
1990–1999	10.25	60.87	67.68
2000–2009	6.84	71.82	81.52
2010–2015	6.53	68.57	90.13
1960–2015	6.57	70.75	77.52

Source: Estimated by the authors based on OECD online database.

Table 2.

Sectoral Distribution of Foreign Aid as a Percentage of Total Aid: 1975–2015.

Year/Average	Agriculture	Industry and Commerce	Transport, Power and Communication	Social Services	Others
1975–1980	18.88	9.45	57.68	13.66	0.4
1981–1985	30.5	7.64	42.96	18.3	0.76
1986–1990	24.56	9.64	46.78	18.02	1.04
1991–1995	29.08	8.56	42.56	19.44	0.38
1996–2000	19.5	1.14	51.94	27.4	0.188
2001–2005	14.18	1.376	40.78	41.6	2.052
2006–2009	13.89	1.0	41.30	43.50	0.31
2010–2015	14.07	1.15	41.5	42.28	1.03
1975–2015	22.9	6.5	47.4	22.7	0.9

Source: Estimated by the authors based on data from CBS (various issues, 1991, 2001, 2015).

High aid allocation towards transport, power and communication sectors was partly motivated by donors’ strategic interests and partly motivated by the ruling elite’s vested interests to improve physical facilities in major urban centres. While some good progress has been made in education, health and other facilities in the urban centres, overall, they too suffer from inefficiencies—particularly in rural areas, where over 60% of the population live—resulting in disappointing health and educational outcomes in the country. For instance, overall literacy, particularly female literacy, remains very low, and the health situation has not improved significantly in the rural areas (Sharma, 2006). Similarly, in the agricultural sector—which is the backbone of the economy in terms of employment and income—productivity continues to decline, despite nearly 30% of aid allocation. Even these investments rarely benefit small farmers in the remote areas because of the bias in agricultural investment towards relatively accessible regions and utilization of a significant amount of funds for overseas studies and trips, construction of official buildings and buying expensive motor vehicles. A decline in agricultural productivity not only led to frequent food shortages and malnutrition but also resulted in a significant drop in the farm income (in the absence of non-farm work in rural areas), making a large number of households landless (Khadka, 1991; Sharma, 2006). As a result, poverty in the rural areas—where the basic amenities such as drinking water, health services and transport facilities are lacking—has worsened. This has led to a significant increase in migration. Annually, about 4 million Nepalese youth leave for temporary employment, and most of them go to India. Consequently, remittances have been a major source of income for a vast majority of the population, surging from just under 5% of GDP in 2001 to nearly 30% in 2013, although it has declined in recent years due to the global downturn (see Figure 4).

Figure 4.

Source: Compiled by the authors based on data from NRB (various issues), CBS (various issues) and MoF (various issues).

Measuring Aid Fragmentation: The Model and Results

In this section, we present an estimate of aid fragmentation and donor proliferation in Nepal, using data from 1965 to 2016.

The most commonly used indicator of aid fragmentation is based on the Hirschman–Herfindahl Index (HHI), which is generally an indicator of market concentration. To estimate aid fragmentation, we sum the square shares of each donor’s share (P) in total aid. The HHI is presented as follows:

H = \sum_{i = 1}^{h} P_{i}^{2.}

(1)

The index captures both the number of donors and their relative shares. The lower (higher) the index is, the more (less) ‘fragmented’ is the aid in the recipient country. Figure 5 presents aid fragmentation index for Nepal.

Figure 5.

Sources: Estimated by the authors’ bed on OECD online database.

The lower value of aid fragmentation index suggests that foreign aid to Nepal has been increasingly fragmented.¹¹

The Model

Following Burnside and Dollar (2000), we use the following augmented model to investigate the effect of aid fragmentation on Nepal’s growth performance. Our variable of interest is aid fragmentation. Since other factors also affect growth, they are also included in our model. The expected signs of the coefficients of variables are given below the equation in parentheses.

\begin{array}{l} RGDP = β_{0} + β_{1} AID + β_{2} AIDFRG + β_{3} AIDAIDFRG + β_{4} DC + β_{5} LAB + β_{6} POL \\ (?) (-) (-) (+) (+) (+) \\ \begin{array}{l} + β_{7} DUM + μ \\ (+) \end{array} \end{array}

where RGDP represents real GDP and AID is foreign aid. AIDFRG measures aid fragmentation, while AIDAIDFRG is an interaction term between AID and AIDFRAG. DC and LAB represent domestic credit and labour force, while the policy variable (POL) captures the effect of macroeconomic policy, measured by inflation rate. DUM is a dummy to capture the effect of democracy on growth. The value of DUM is 0 for the 1965–1989 period (autocratic regime) and 1 for 1990 onwards (democratic regime). µ is an error term. Appendix 1 presents the definition of variables and data sources.

The aforementioned model is tested using autoregressive distributed lag (ARDL) approach to cointegration. One of the advantages of the ARDL approach is that it can be used irrespective of whether the regressors are I(1) or I(0), and thus it avoids the pretesting problem of unit root (Pesaran & Pesaran, 1997).

The approach uses the error correction version of the ARDL, as follows:

Δ Y_{t} = α + \sum_{i = 1}^{m - 1} b_{i} Δ Y_{t - i} + \sum_{i = 0}^{m - 1} c_{i} Δ X_{t - i} + d_{1} Y_{t - m} + d_{2} X_{t - m} + μ_{t}

where α is a vector of constants, Y_t is vector of endogenous variables, X_t is a vector of explanatory variables and b and c are matrices of parameters.

There are two stages in the ARDL approach to cointegration. The hypothesis of no cointegration is tested in the first stage. The null hypothesis is that the coefficients on the lagged regressors in the error correction form of the underlying ARDL model are jointly 0. That is, there exists no long-run relationship between them. The null hypothesis is defined by H₀: d₁ = d₂ = 0 and tested against the alternative of H₁: d₁ ≠ 0, d₂ ≠ 0. The approach uses the F-test, although the asymptotic distribution of the F-statistic in this context is non-standard, irrespective of whether the variables are I(0) or I(1).¹² If a long-run relationship between the variables is found, in the second stage, the long-run and short-run parameters are estimated using the ARDL method. We set the lag length equal to 2 on all variables in the ARDL equation, which is considered a standard practice in empirical analysis. The model is tested using the Schwarz Bayesian criterion (SBC) for lag selection, and diagnostic tests are performed for all equations (see Tables 3). We use EViews version 10 to estimate the model.

Empirical Results

This section presents an empirical evidence on the effect of aid fragmentation, using historical data from 1965 to 2016. The main sources of data are OECD (2019) database. We begin our analysis with the investigation of the existence of a long-run relationship between the variables under investigation. In each case, the F-statistic exceeds the critical value of the upper bound, and the null hypothesis of no cointegration between the variables is rejected at the 5% level, irrespective of the order of their integration (F-statistics are presented in Table 3). Once the evidence of cointegration is found (i.e., having established a long-run relationship between variables in each model), the regression results are reported. The long-run coefficients for the ARDL models are presented in Table 3.

Our empirical analysis begins with a simple two-variable model that includes aid fragmentation (AIDFRG) and an interaction term between aid and aid fragmentation (AIDAIDFRG) (see model A in Table 3). As expected, results of the model A suggest that aid fragmentation has statistically significant and a negative impact on growth, perhaps due to an increased administrative burden, which naturally reduces aid effectiveness and promotes misuse of aid resources, especially when governance is weak. Surprisingly, the interaction term between aid and aid fragmentation (AIDAIDFRG) is statistically insignificant and has a negative sign. In model B, we introduced aid (AID), and an interaction term between aid and aid fragmentation (AIDAIDFRG). In line with theoretical expectations, the coefficient of aid variable is statistically significant, suggesting that aid is good for growth in developing countries like Nepal. However, the coefficient of the interaction term between aid and aid fragmentation (AIDAIDFRG) is negative and statistically significant, suggesting that when aid is fragmented, it is detrimental to growth. As noted earlier, this could be due to increased administrative burden to oversee a large number of fragmented aid projects (see model B in Table 3). We include POL to capture the effects of macroeconomic policy, in addition to AID and AIDFRG variables in model C. As expected, the coefficients of aid and policy variables are statistically significant and have a positive sign, while the coefficient of aid fragmentation variable is statistically significant and has a negative sign. These findings suggest that both foreign aid and sound macroeconomic policy are good for growth in developing countries like Nepal, while aid fragmentation retards growth as expected. In model D, we include the DC variable to capture its growth impact, in addition to AID and AIDFRG variables. As expected, coefficients of both AID and DC variables are statistically significant, with a positive sign, while the coefficient of AIDFRG variable is statistically significant with a negative sign. These findings tend to suggest that while foreign aid and access to domestic credit enhance growth, aid fragmentation retards growth, perhaps due to increased administrative burden to manage a large number of small projects and meet reporting requirement of the donor community.¹³ We introduce the POL variable, in addition to DC, AID and AIDFRG variables in model E. As expected, coefficients of aid and policy variables are statistically significant, with a positive sign, while the coefficient of AIDFRG is statistically significant and has a negative sign. These results suggest that development assistance and sound macroeconomic policy are good for growth, but not fragmented aid, possibly due to increased administrative burden associated with the management of many small projects. In model F, the POL variable is included together with foreign AID, and an interaction term between AIDAIDFRG. Results are consistent with theoretical expectations, indicating that foreign aid and sound macroeconomic policy enhance growth. However, the coefficient of the interaction term between AIDAIDFRG is statistically significant and has a negative sign. This tends to suggest that when aid is fragmented, it retards growth, possibly due to increased administrative burden, resulting in mismanagement of aid resources. Our final model, model G, includes AID plus DC and an interaction term between AIDAIDFRG. As expected, results suggest that access to domestic credit and foreign aid are good for growth. However, when aid is fragmented, it is detrimental to growth. Our findings are broadly consistent with previous studies by Sharma and Bhattarai (2013), Hansen and Tarp (2000) and Sachs (2005).

Table 3.

Estimated Long-run Coefficients from the ARDL Model.

Dependent Variable	Model A	Model B	Model C	Model D	Model E	Model F	Model G
Real GDP Growth
AID		0.041	0.031	0.030	0.031	0.042	0.042
		(2.75)*	(4.43)*	(4.35)*	(4.55)*	(7.42)*	(7.64)*
AIDFRG	−0.87		−0.22	−0.20	−0.19
	(−4.28)*		(−2.39)**	(−2.97)*	(−2.16)**
AID*AIDFRG	−0.03	−0.56				−0.05	−0.06
	(−0.35)	(−4.92)*				(−2.03)**	(−2.66)**
POL			0.005		0.003	0.005
			(19.05)*		(3.08)*	(21.50)*
DC				0.011	0.003		0.011
				(19.11)*	(1.42)		(20.93)*
Constant	7.06	7.01	6.55	6.53	6.54	6.50	6.48
	(143.34)*	(76.83)*	(134.68)*	(141.50)*	(132.61)*	(192.38)*	(189.06)*
F-test (for cointegration)	19.30*	26.45*	4.97*	5.06*	4.69*	4.96*	15.31*
Adjusted R-square	0.49	0.33	0.62	0.71	0.70	0.62	0.72
Serial correlation	0.58	0.70	0.79	0.11	0.047	0.94	0.15
CUSUM Test	Stable	Stable	Stable	Stable	Stable	Stable	Stable
Normality	0.002	0.000	0.001	0.001	0.001	0.001	0.001
Heteroscedasticity	0.065	0.050	0.14	0.11	0.07	0.12	0.12

Source: The authors.

Notes: numbers in parenthesis are t-statistics. * and ** Indicate statistical significance at the 1% and 5% levels, respectively. The F-test (bound test) for cointegration is based on the critical values provided by Pesaran et al. (2001). The test for serial correlation is the Lagrange multiplier (LM) test for autocorrelation, and the model stability test is based on CUSUM test at the 5% level of significance.

Concluding Remarks

The relationship between foreign aid and economic growth in developing countries remains controversial despite growing literature on the issue. Some studies find that aid is good for growth, while others find no evidence of aid effectiveness. While there is growing consensus to suggest that aid fragmentation retards growth by placing considerable pressure on already limited administrative resources in developing countries and lowering the quality of governance and institutions, empirical findings are contradicting. This is perhaps due to the cross-sectional nature of available empirical studies, which put together countries from different backgrounds, ignoring country-specific settings. Since developing countries differ significantly among themselves, what may be true for one country may not hold for another. This problem associated with the cross-sectional studies can be overcome through a case study approach, which considers each country’s structural features and the policy settings.

In this study, we contribute to the literature on aid fragmentation and growth by undertaking an in-depth case study of Nepal, which has been the largest aid recipient in South Asia. Despite huge aid flows for nearly half of a century, it remains a poor country. Our empirical results suggest that aid fragmentation is detrimental to growth, perhaps due to increased administrative burden, resulting in misutilization of resources. While aid is good for growth, attracting uncoordinated and fragmented aid can, in fact, lower the quality of governance and institutions as argued by Bauer (1976). Attracting more aid without country’s need assessment and developing appropriate institutional capability can do more harm than good for growth. These findings point to the importance of coordinated aid programmes at the country level as well as among the donor community.

Appendix 1.

Definition of Variables and Data Sources.

Variables	Definition	Data sources
RGDP	Real GDP based on 2010 US$	World Bank (2019)
AID	Aid is net ODA as a percentage of GDP	OECD (2019)
AIDFRAG	Aid fragmentation based on HHI	Estimated by the authors using Hirschman–Herfindahl Index, based on OECD (2019)
AIDAIDFRAG	AID interaction with Aid fragmentation	Computed by the authors
LAB	Labour force	World Bank (2019)
DC	Domestic credit to private sector as a percentage of GDP	World Bank (2019)
POL	Macroeconomic policy, captured by inflation	World Bank (2019)

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iD

Kishor Sharma

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