Foreign aid, institutional quality and economic growth: Evidence from the developing world

Journal Pre-proof Foreign aid, institutional quality and economic growth: Evidence from the developing world Admasu Maruta, Rajabrata Banerjee, Tony Cavoli PII:

S0264-9993(19)30647-9

DOI:

https://doi.org/10.1016/j.econmod.2019.11.008

Reference:

ECMODE 5066

To appear in:

Economic Modelling

Received Date: 2 May 2019 Revised Date:

30 September 2019

Accepted Date: 7 November 2019

Please cite this article as: Maruta, A., Banerjee, R., Cavoli, T., Foreign aid, institutional quality and economic growth: Evidence from the developing world, Economic Modelling (2019), doi: https:// doi.org/10.1016/j.econmod.2019.11.008. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Foreign Aid, Institutional Quality and Economic Growth: Evidence from the Developing World Admasu Maruta*, Rajabrata Banerjee† and Tony Cavoli‡

*

School of Commerce, University of South Australia, GPO Box 2471, Adelaide, South Australia 5001. Email: [email protected] Tel. +61 8 830 20315. † Corresponding author. School of Commerce, University of South Australia, GPO Box 2471, Adelaide, South Australia 5001. Email: [email protected] Tel. +61 8 830 27046. ‡ School of Commerce, University of South Australia, GPO Box 2471, Adelaide, South Australia 5001. Email: [email protected] Tel. +61 8 830 20831. Acknowledgements: The authors are grateful to the participants of African Economic Conference 2017, organised by the United Nations Economic Commission for Africa, on an earlier draft of this paper. Comments received from colleagues at University of South Australia Business School are also gratefully acknowledged.

School of Commerce, University of South Australia

Abstract: This paper examines the effect of sectoral foreign aid and institutional quality on the economic growth of 74 developing countries from Africa, Asia and South America, and covers the period 1980-2016. We consider bilateral aid flows into three sectors, namely education, health and agriculture, and find that among the three types of aid, education aid is more effective for aidreceiving countries. The effect is conditional on the current level of institutional quality and varies substantially across regions. While education aid is more effective in South America, health aid is more effective in Asia and agricultural aid is more effective in Africa. As the level of institutional quality improves, the gap between the marginal effect of education, health and agricultural aids widen. Our findings have strong policy implication for donor countries and international aid organisations, which shows that it is more desirable to shift aid flows towards the education sector as the level of institutional quality improves.

JEL Classifications: O11, O43, O57 Keywords: Foreign aid, institutional quality, economic growth

Foreign Aid, Institutional Quality and Economic Growth: Evidence from the Developing World

Abstract: This paper examines the effect of sectoral foreign aid and institutional quality on the economic growth of 74 developing countries from Africa, Asia and South America, and covers the period 1980-2016. We consider bilateral aid flows into three sectors, namely education, health and agriculture, and find that among the three types of aid, education aid is more effective for aid-receiving countries. The effect is conditional on the current level of institutional quality and varies substantially across regions. While education aid is more effective in South America, health aid is more effective in Asia and agricultural aid is more effective in Africa. As the level of institutional quality improves, the gap between the marginal effect of education, health and agricultural aids widen. Our findings have strong policy implication for donor countries and international aid organisations, which shows that it is more desirable to shift aid flows towards the education sector as the level of institutional quality improves.

JEL Classifications: O11, O43, O57 Keywords: Foreign aid, institutional quality, productivity growth

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1. Introduction The amount of foreign aid received by developing countries in real terms has increased significantly in the recent years, from US$ 127.3 billion in 2010 to 145.7 billion in 2015.1 Since Rosenstein-Rodan (1943) advocated the provision of aid to the Eastern and South-Eastern Europe, a number of studies show that foreign aid has beneficial effects on economic growth (see Burnside and Dollar 2000; Collier and Dollar 2003; Dalgaard et al. 2004). However, does foreign aid permanently increase economic growth of the recipient country? The question is important since many studies have shown controversial findings on aid effectiveness. For example, some studies argue that foreign aid has negative impacts on economic development of poor countries by aggravating corruption, civil conflicts, creating dependency syndrome and reduced level of domestic production (Easterly 2003; Djankov et al. 2008). Other studies show that foreign aid has almost no effect on economic growth of the recipient countries (e.g. Boone 1996; Rajan and Subramanian 2008). A common drawback of these existing studies is that aid effectiveness is examined by aggregating different types of foreign aid, such as, humanitarian, military, education, health, agriculture and other types of aid, into a single amount. As a result, it is impossible to draw inferences about the individual contribution of any specific type of aid on aggregate growth. Yet, the comparison is important since it allows policy-makers and international aid organisations to identify the most effective type of aid to stimulate growth. Aid is never disbursed in an aggregated manner and therefore policy implications based on aggregate aid analysis remains contentious. Among prominent studies that investigate aid effectiveness in a disaggregated manner are Dreher et al. (2008) and Mishra and Newhouse (2009). Although these studies provide some important policy directions, the focus was on sector-specific

1

Source OECD. Link: http://www.oecd.org/dac/stats/aid-at-a-glance.htm

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outcomes and not aggregate growth. With sector specific studies, the findings do not confirm a similar effect on the overall income per capita level of a country. We fill this important gap and make several contributions to the literature. First, we offer a parsimonious understanding about the relationship between foreign aid and aggregate growth by comparing the effects of three types of sectoral foreign aids, namely, education, health and agriculture. These three sectors assume a high level of importance since they receive more recent attention by international development agencies, such as the World Bank, in their effort of promoting economic growth and development of the poor countries. Among the seventeen Sustainable Devolvement Goals set by the United Nations General Assembly in 2015, eight of them are related to these sectors. Hence, the sectors comprise the highest proportions of goals set by international development communities. Moreover, the channels through which the sectors influence economic growth are fundamental for successful take-off of an aid-recipient country (Rostow, 1958). For instance, agriculture plays a key role as an ‘engine of growth’ and poverty reduction in the early stages of development (World bank, 2008). Foreign aid in agriculture, when successful in reducing poverty, could be more efficient in increasing wellbeing of poor than other sectoral aid flows and enhances economic growth of aid recipient countries (Kaya et al, 2013). Similarly, proper utilisation of education aid increases school enrolment rates, which in turn, help workers to participate in highly paid jobs that require higher skills and knowledge (Gregorio and Lee 2002; Dreher et al., 2008; Apple 2012). Consequently, education aid has long-term and permanent effects on human capital accumulation, which would increase the living standards of the aid recipient countries (Azarnert 2008; Turnovsky 2011). Likewise, health-targeted aid provides improved health services that prevent and treat diseases, lowers infant mortality and increases individuals’ productivity and wages, and thereby increases economic growth (Bloom, 2004; Mishra and Newhouse, 2009; Afridi and Ventelou, 2013).

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Second, we examine the role of institutional quality for sectoral aid effectiveness. The importance of institutional quality on economic growth and foreign capital flows is already well established (e.g. Rodrik et al. 2004; Slesman et al, 2015a, b; Maruta, 2018; Arya et al, 2019; Williams, 2019). The extant literature suggests that there are certain threshold conditions in the absence of which the benefits of foreign capital inflows are insignificant for the recipient countries (Durham, 2004; Kose et al., 2009; 2011). This is primarily due to absence of structural characteristics, such as human capital, local financial development, strong macroeconomic policy and institutions. More specifically, Slesman et al (2015a) and Arya et al (2019) show that the effects of foreign capital flows below a certain threshold level of institutional quality are insignificant due to factors such as corruption, bureaucratic incompetencies, political instability and red tape. However, not much attention has been paid until now on the threshold conditions of sectoral foreign aid and institutional quality on economic growth. In this context, better institutional quality improves the macroeconomic performance of a country by decreasing uncertainty, directing foreign aid to the most productive areas, building trust, and enhancing cooperation between the donor and the receiver countries. In a related study, Kathavate and Mallik (2012) use aggregate aid data and examines the interaction effect of aid volatility and institutional quality on economic growth of 78 countries in the period 1978-2004 and find that a negative effect of aid volatility on economic growth is mitigated by stronger institutional quality of aid recipient countries. Third, we intend to exploit the regional differences of institutional factors and types of aid to capture an international perspective. For example, technology diffusion among countries is slow due to differences in institutional quality and development levels, even though technology is available immediately and there are no added technology adoption costs (Basu and Weil, 1998). At present, there is very little evidence that the relationship between foreign aid and economic growth is stable when there are differences in social norms, public

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pressures, legal system and infrastructure availability. Developing countries differ in various socio-economic factors, such as policy environment, types of government and geographical location. Thus, based on requirements and the relationship with the donor countries, these regional disparities will influence aid effectiveness among countries. In this study, we consider 74 aid-receiving countries from three main regions, Africa, Asia and South America, respectively. Our main finding is that among the three types of foreign aid, education aid augmented by institutional quality is more effective on economic growth. The effect of foreign aid on economic growth varies substantially across the three regions. In the context of regional disparities, we find that agriculture aid is more effective in Africa, education aid is more effective in South America and health aid is more effective in Asia. In South America, as the level of institutional quality improves, the gap between the marginal effect of education, health and agricultural aids widens. Our findings are robust with different specifications, such as dropping outliers, and using alternative outcome variables. The remainder of the paper is structured as follows: Section 2 develops the hypotheses and discusses the related literature. Section 3 presents the empirical methodology. The empirical findings and their implications are presented in Section 4. Finally, Section 5 concludes this discussion. 2.

Literature review and hypothesis development The theoretical relationship between foreign aid and economic growth is best

explained through the lens of neoclassical growth model of Solow and Swan (1956) (see Appendix 1). Foreign aid flows once received by a country add to their existing capital stock. If aid flows are successful, higher capital deepening would lead to higher economic growth. However, with diminishing returns to capital stock, the growth effect of foreign aid is only transitory unless foreign aid brings positive change to the total factor productivity growth or 5

human capital deepening, which has a permanent growth effect in the long run (see Appendix 1). Empirical examinations of this relationship bring controversial findings to the literature. While many studies find positive effect of foreign aid, there is also evidence that foreign aid has a negative or insignificant or no effect on economic growth. Among the studies that find a positive effect of foreign aid, Jones and Tarp (2015) requires close attention. They claim that aid has positive effect on economic growth for the past 40 years and influence a range of proximate sources of growth and development outcomes, such as physical and human capital, poverty and infant mortality and economic transformation measures, such as agriculture and industry value added. Burnside and Dollar (2000) and Dalgaard et al. (2004) find that foreign aid stimulates economic growth, but it is conditional on good policy environment, such as when there is a good fiscal, monetary and trade policies in place. Further, Hansen and Tarp (2001) show that there are decreasing returns to aid; and its effectiveness is sensitive to other controls affecting economic growth. For example, when investment and human capital are controlled, aid has no significant impact on economic growth. Similarly, Young and Sheehan (2014) argue that aid flows do not influence growth after institutional quality is controlled. Others find that positive aid effectiveness depends on factors such as investment, terms of trade, real value of exports, internal political and civil polices and climatic shocks (Chenery and Strout, 1966; Guillaumont and Chauvet, 2001). Svensson (1999) argues that if countries have more democratic government, aid significantly promotes economic growth. Since recipient country’s government intermediates aid inflows, aids may be allocated for unproductive and unintended purposes due to their fungibility behaviour2.

2

Fungibility of aid is the possibility that aid is used by governments in ways not intended by donors when disbursing the funds (Pack and Pack, 1993)

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In contrast, Maren (1997) and Djankov et at. (2008) argue that aid reduces economic growth and deteriorates the quality of institutions by escalating the perceptions of corruption3. They further suggest that aid provides a windfall of resources, which may cause rent seeking behaviour as documented in the curse of natural resources studies.4 Moreover, Maren (1997) provides evidence that aid, specifically food aid, is the main cause of civil conflicts, particularly when people want to control and possess aid money to satisfy their own desires. Boone (1996) and Rajan and Subramanian (2008) show that there is no robust evidence to claim that aid spurs growth in a good policy environment. Aid proliferation has a negative effect on economic growth, particularly for African countries, since lack of international aid coordination leads to the weak ownership and inefficient aid-absorption capacities of receiving countries (Kimura et al, 2012). Due to these mixed findings, recently there is a growing body of literature examining the effect of sector-targeted aid on economic growth (e.g. Asiedu and Nandwa, 2007), on financial development (Maruta, 2018) and on sectoral outcomes (e.g. Mishra and Newhouse, 2009). These studies show that pooling different types of aid into one figure obscures the individual effect of each type of aid (Addison and Tarp, 2015). In addition, international aid organisations support evaluations of aid effectiveness through disaggregating aid into sectors, and even at the project levels. However, there is no literature, to our knowledge, which systematically investigates and compares the effect of different types of sectoral aid on aggregate growth. Thus, we frame our first hypothesis: H1. The effect of sectoral foreign aid on economic growth is positive.

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Tavares (2003) suggests that aid decreases corruption. Djankov et al. (2008) state that aid potentially intensifies political instabilities in developing nations than other natural resources, such as petroleum oil, do.

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Next, we look at the effect of sectoral aid conditional on institutional quality. There is a burgeoning literature examining the relationship between institutional quality and economic growth (e.g. see Hall and Jones, 1999; Acemoglu et al., 2001; Rodrik et al., 2004; Banerjee and Iyer, 2005; Faria et al., 2016; Nawaz and Khawaja, 2018). Strong private property rights, contract enforceability and lower risk of expropriation significantly promote investment and economic growth (Murphy et al., 1993; Knack and Keefer, 1995). Williams (2015) show that political institutions play a key role in reducing the negative effect of credit market deepening on economic growth of developing countries. Using firm-level data, Boubakri et al. (2015) show that tighter political constraints stimulate firm growth and promote economic growth in these countries. Higher institutional quality also improves sectoral development, particularly finance and health sectors, and thereby improves economic growth (Porta et al., 1998; Marmot, 2005). Eslamloueyan and Jafari (2019) show that institutional quality is a key driver of investment and protected the East Asian countries from financial crises in the past. Other studies have shown that there is a threshold level of institutional quality above which the effect of foreign capital flows, such as foreign direct investment, debt flows and equity flows, on economic growth of recipient countries are positive (Slesman et al, 2015a, 2015b; Arya et al, 2019). Kathavate and Mallik (2012) develops a theoretical model showing institutional quality affects the distribution of aid allocated by a government in a recipient country. Empirical evidence suggests that aid is less volatile and allocated toward investment with stronger institutional characteristics, which dampens the negative effect of aid volatility on economic growth. Thus, it is expected that if the recipient countries have better institutional quality, the marginal effect of sectoral aid will be significantly positive. Conversely, if a country has poor institutional quality, such as higher degree of corruption, political instability and less responsiveness and accountability to citizens’ interests, the marginal effect of sectoral aid will be negative (Acemoglu and Johnson, 2005). Overall, there is enough

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evidence that shows that better institutional quality in the recipient countries increases aid effectiveness (Bräutigam and Knack, 2004; Maruta, 2018). Thus, we frame our second hypothesis: H2: The effect of sectoral foreign aid on economic growth is conditional on the level of institutional quality of the aid-receiving countries. Finally, we compare the individual effect of sectoral aids and their interaction with institutional quality across three regions. There is evidence explaining that numerous factors trigger regional differences. For example, the average per capita GDP growth is significantly different across regions. African countries have lower average per capita GDP growth than Asia and South America (World Bank, 2015). Collins et al. (1996) show that African countries have lower capital stock growth (4.8%) than East Asia (9.9%) and South America (5.4%) during the period 1960-1994. African countries also have lower average years of schooling and educational attainment (1.6) than East Asian (2.7) and South American (3.0) countries during the same period. East-Asian countries attain higher labour quality index and total factor productivity than African and South America countries. It is also evident that regions are different regarding health sector development indicators. For example, Bloom and Canning (2000) state that East-Asian countries achieve better health improvements than African countries, which is manifested by declining child and infant mortality, higher life expectancy and availability of safe water and sanitation. Similarly, South American countries have significantly increased public health expenditure since 1995, which potentially reduce the percentage of stunted children than Africa and Asia (Black et al. 2008). In addition, the level of institutional quality also varies between regions. Bräutigam and Knack (2004) suggest that poor institutional quality such as weak rule of law, absence of accountability and transparency, tight controls over information, and high level of corruption are the longstanding problems of African countries. Conversely, Asian and South American 9

countries have better institutional quality in the areas of health, infrastructure, energy, agriculture and education sectors. Thus, we frame our third hypothesis: H3: The combined effect of sectoral foreign aid and institutional quality on economic growth significantly varies across aid-receiving regions. 3. Empirical Methodology 3.1.

Data Description

Our sample contains 2,442 country-year observations from 74 aid-receiving countries from Africa, Asia and South America, and covers the period 1980-2016. Detailed data descriptions and measurement issues of our key variables, such as sectoral aids, institutional quality measured by International country Risk Guide (ICRG) index and the control variables are discussed in Appendix 2. A List of countries are provided in Appendix 3. [Insert Table 1] In Table 1, we present the summary statistics of our key dependent variables and control variables. The mean and median values of education aid are 0.80 and 0.61, respectively. The mean and median values of health aid are 0.90 and 0.40, respectively. The mean and median values of agriculture aid are 1.35 and 1.23, respectively. This indicates that health and education aid, respectively, comprise the highest and lowest share of real GDP in our full sample. The differences in the mean values of sectoral aid imply that there may be a substantial variation among sectors in connection with using aid provided to each sector, and thereby promote growth. We also note that, some countries have zero reported value on sectoral aid. We decided not to remove the countries with zero sectoral aid for two reasons: (1) to avoid self-selection bias, and (2) zero sectoral aid means a country doesn’t receive aid at that particular period of time (i.e. we cannot assume zero value of sectoral aid as a missing 10

data).5 In summary, we find that the key variables (sectoral aid and ICRG) used in Table 1 fall in the resealable range of variables. 6 Figure 1: Aid flows in education, health and agriculture sectors: 1980-2016

Note: Data is collected from Aid Database. Based on authors’ own calculations. The figures reflect the total aid flows (in billion US$) to Africa, Asia and South American countries in the period 1980-2016. For specific country list, please see Appendix 2.

Figure 1 shows the total amount of education, health and agriculture aid flows in the three regions namely, Asia, Africa and South American countries in period 1980-2016. We find that the education and health aid flows have increased over time, particularly in the post2000 period. Agricultural aid flows, on the other hand, shows a steady trend during this period with a small increase after the year 2002. Also, health aid dominates the other two types of aid flows, with a steep increase in its share after the year 2000. This is likely because of the Millennium Development Goals (MDGs) as proposed by the United Nations that

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This argument is usually held in the aid literature (see Arndt et al. 2010). We checked our estimation results considering zero sectoral aid as missing data. The result shows that there is a higher difference in the magnitude and significance level of the coefficients of each variable. 6 We have conducted two panel unit root tests (Levin-Lin-Chu unit-root test and Harris-Tzavalis unit-root test) for growth of per capita income, aid targeted to education, health and agriculture sectors and the ICRG index. In unreported results, we find strong evidence against the null hypothesis of a unit root and therefore conclude that the dependent and the key explanatory variables are stationary.

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impose pressure on donors to increase the amount of health aid provisions, particularly to reduce child mortality and to improve maternal health in developing countries. It is also interesting to note that the share of education aid demonstrates a sustainable increase over time as compared to agricultural aid flows. While the share of education aid flows was significantly below the total amount of agricultural aid flows at the start of the sample period in 1960, by the year 2000, education aid flows surpassed the agricultural aid flows. This is likely again due to the MDGs that aim to achieve universal primary education in all developing countries in the twenty-first century.

Figure 2: Education, health and agriculture aids in three regions: 1980-2016

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Note: Data is collected from Aid Database. Based on authors’ own calculations. The figures reflect education, health and agricultural aid flows (in billion US$) to Africa, Asia and South American countries in the period 1980-2016. For specific country list, please see Appendix 2.

Figure 2 further disaggregates the aid flows by regions. While panel A shows the time trend of all three types of aid flows in Africa, Panel B and Panel C shows the same for Asia and South American regions, respectively. In both Africa and Asia, health aid continuously dominates the other two types of aids during the entire sample period 1980-2016. In contrast, health aid shows irregular spikes in South America. We also find that the share of agricultural aid is comparatively higher in Africa. This is intuitive since Africa is predominantly more agricultural than the other two regions and received higher aid flows in the sector. In both Asia and South America, while the share of agricultural aid has decreased over time, education aid share has increased in recent years. This shows some support in favour of education sector development in countries with higher growth rates. The above evidence also shows distinct characteristics of aid flows in all three regions. 3.2.

Empirical Methodology

Following Burnside and Dollar (2000) and Collier and Dollar (2003), we utilise panel two-stage least squares (2SLS) estimation technique on the following model.
 = ∅ + ∅  + ∅   + ∅

 + ∅ ( ×  ) + ∅ ( ×  ) + ∅ ( ×  ) + ∅! (

 × "#$% ) + ∅& (

 × "#$% ) + ∅' (

 × "#$% ) + ∅ "#$% + ∅ ( + ) + *+ + ,

(1)

where + denotes the country (+ = 1, … , 74) and 2 denotes the time (2 = 1980, … ,2016).
, shows the growth of per capita income of a recipient country. "#$%, is the ICRG index that measures the institutional quality of a recipient country, ( is a vector of control variables, ) is time fixed effect, * is unobserved country fixed effect and , is the idiosyncratic error.  ,   and

 show the amount of education, health and agriculture aids, 13

respectively, in the previous periods. We consider four-year lags for education aid as theoretically, the effect of education on growth takes a longer timespan to take effect through the channel of building human capital (Savvides and Stengos, 2008). Since, the biggest influence on growth is expected to come from tertiary education by generating higher skills and ideas, a 4-year lag is justified to capture the effect of sectoral aid on education. In contrast, we use one-year lag for health and agriculture aids since the realisation of their effects on growth is relatively contemporaneous in nature.7 Following hypothesis H2, we expect ∅ to be positive as institutional quality increases economic growth. Further, we expect ∅ , ∅ and ∅ to be positive as higher institutional quality significantly increases aid effectiveness. Moreover, we include the squared term of the aid variables as the extant literature suggests that aid may have a potential non-linear relationship with economic growth of the recipient countries. For example, in their aid and growth regressions, Hansen and Tarp (2001) find that aid has decreasing returns in which the coefficient of aid squared appears significantly negative in all specifications. Consistent with the existing evidence, we expect ∅! , ∅& and ∅' to be negative as the squared form of each type of aid has significantly negative effect on growth. We further apply these models to examine how the joint effect of sectoral aid and institutional quality on growth is varied among three regions: Africa, Asia and South America as described in hypothesis H3. An important issue to consider is the reverse causality relationship between economic growth and sectoral foreign aids. This is because current foreign aid provisions may be determined by future economic growth projections. Thus, if the current trend of economic growth is low, and a similar forecast is believed to continue for future years, the donors will 7

In other unreported results, we changed the lag structure of the aid terms and compared our results. We find the coefficients of aid terms are qualitatively similar. The ICRG index and the control variables are also lagged to minimise possible endogeneity with the dependent variable.

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restrict further aid provision to that country. As a result, the positive effect of aid would be underestimated. Additionally, the measurement error can also be a potential source of bias as donors may misreport the amount of committed and/or disbursed aid to the AidData database8. We instrument foreign aid in eq. (1) by the affinity of nations index proposed by Gartzke (2009). This is based on the argument that political proximity between donors and recipient countries is an important driver of bilateral aid allocation among the recipient countries, which becomes an imperative means of donors’ foreign policy. The affinity index gives a metric that reflects the similarity of state preferences based on voting positions of pairs of countries in the United Nations General Assembly (UNGA). The political proximity is plausible exogenous drivers of a donor country to provide aid and that is unlikely to be correlated with the recipients’ growth of per capita income except through the received aid. We use the affinity index between each recipient and four largest bilateral donors which include United States, Canada, United Kingdom and France.9 Moreover, as presented in Table 2, USA, Canada, UK and France are among the largest donors of foreign aid to developing countries over the period 1980-2016. [Insert Table 2]

The main hypothesis is that, all else equal, donors provide large amount of aid targeted to education, health and agriculture sectors to countries which have strong political affiliation (as reflected by higher affinity index) with them. Thus, it is rational to consider 8

The presence of aid elements in the error term of model 5 violates one of the Gauss-Markov assumptions; for example, it violates the expected values of the aid variables and the error term are equal to zero, and thereby creates an endogeneity issue (see Cragg and Donald, 1993). 9 We also run regressions using the affinity index of other bilateral donors including Australia, Germany, Italy, Japan, Korea, Netherlands, Norway, Spain and Sweden as instrumental variables of the aid variables. However, these donors have many missing data on affinity index, as compared to USA, Canada, UK and France, which may potentially lead to ambiguous conclusions. Therefore, our analysis mainly focuses on the findings obtained from the use of the affinity indices of USA, Canada, UK and France as instruments of the aid variable.

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that the affinity index of nations is a plausible instrument of foreign aid in the aid-receiving countries.10 However, we find that countries listed in the UNGA did not change their voting positions significantly on various issues in the period of our study.11 This finding indicates that most of the variability in our instrument comes from cross-country variations. Hence, to address this issue, we include country fixed effects in the first stage of the IV regressions. Formally, the affinity index is calculated as follows:

899+:+2;< = 1 − (2>(? , ?< )/>ABC )

(2)

where + and D denote the countries dyadic, and 2 denotes the time (2 =1980, …, 2016). >E? , ?< F is the sum of metric distances between votes by dyad members in terms of UNGA votes (1 = approval; 2 = abstain; 3 = disapproval) in each year. >ABC is the largest possible metric distance for those votes for a given year. For example, if there have been 100 determinations in a year, >ABC = 200. Thus, the index ranges from −1 (minimum affinity) to +1 (maximum affinity). In each year 2, we calculate four variables for each recipient country + by using the four major bilateral donors of aid targeted to education, health and agriculture sectors as counterpart countries D, which includes, United States, Canada, Unites Kingdom and France. If, for example, USA (donor) and Senegal (recipient) have the total vote distance of 200 in three UNGA determinations, their affinity index will be calculated as 1((2*200)/200) = -1, showing that USA and Senegal have dissimilar preferences which may

10

We test for validity of our instrument by checking whether it satisfies two important conditions: (1) Relevance i.e. being sufficiently correlated with sectoral foreign aid; (2) Exogeneity/orthogonality i.e. being distributed independently of the error process. In unreported results, we find that that the instrument is strongly correlated with the sectoral foreign aid and weakly correlated with the dependent variable, economic growth, satisfying the ‘relevance’ condition. We also fail to reject the null hypothesis that the excluded instruments are valid instruments. Thus, our instrument passes both the conditions of a valid instrument. 11 To check this, we calculate the annual percentage change of the affinity of nations index for all countries. We find that all countries in our sample have less than 30% annual change in their voting positions in the UNGA for the period of our study.

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result small amount of aid delivered from USA to Senegal, and vice versa if these countries have higher affinity index. 4. Empirical Results 4.1. Main findings The results for the full sample generated from panel 2SLS estimation are presented in Table 3. While specification 1 captures the effect of education, health and agriculture aids on GDP per capita growth without interaction with institutional quality, specifications 2, 3 and 4 show the individual and joint effects of education, health and agriculture aids, respectively, interacted with institutional quality, on income per capita growth. In specification 5, we implemented the complete model, which shows the individual and the interaction effects of all sectoral aids with institutional quality. [Insert Table 3] Our findings show that for each specification, the estimated coefficient on the ICRG is significantly positive in all specifications. A one percentage point increase in institutional quality contributes in the range 0.0194 to 0.0351 percentage points increase in economic growth. Consistent with Hansen and Tarp (2001) the squared terms of each type of sectoral aid have significantly negative effect on economic growth of the recipient countries. The aid literature suggests that there are diminishing returns to foreign aid due to absorptive capacity constraints. For example, Feeny and de Silva (2012) review extensive aid literature and identify five types of absorptive capacity constraints that limit the effectiveness of additional aid inflow to developing countries, such as human and physical capital constraints, policy and institutional constraints, macroeconomic constraints, deficiencies in the manner in which the

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international donor community delivers its foreign assistance and social and cultural constraints. Specification 1 shows that a one percentage point increase in education aid and health aid contributes 0.2072 and 0.0564 percentage point increase, respectively, in growth of per capita GDP. This finding supports our hypothesis H1 that individual sectoral aid influence economic growth positively. However, agriculture aid has insignificant effect on growth of per capita GDP. Specifications (2), (3) and (4) show the interaction effects of sectoral aids with institutional quality are also positive and significant. It is important to note that in specification (4) the effect of agriculture aid turns to significantly positive when it is interacted with institutional quality. The findings support hypothesis H2 and show that better institutional quality facilitates the positive effects of sectoral aids on growth. In specification (5), we include all types of sectoral aids and interacted them with institutional quality. We find that our results in the complete model are robust and consistent with previous specifications (2), (3) and (4). The individual (except agriculture aid) and joint effects of all sectoral aids are positive and significant; however, the effect of education aid is maximum (0.1301 and 0.7483, respectively). Thus, for the full sample, the effect of education aid is higher than health and agricultural aids. The coefficients of the control variables show the expected signs. The coefficients of logarithm of initial income, trade openness, inflation, life expectancy and logarithm of M2/GDP are significant and of the expected sign in most of our specifications. We include country and time dummy variables to capture country and timespecific effects, respectively. Cragg-Donald Wald F statistic test for weak identification test show that our instruments are plausible; and the estimates are unbiased and robust across all specifications. [Insert Table 4]

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Next, Table 4 reports the results of the effect of sectoral aid, and their interaction effects with institutional quality on growth in three regions: Africa, Asia and South America, respectively. Since in Table 3, the significance and signs of individual and joint effects of sectoral aid and institutional quality in specification (5) are robust with specifications (2), (3) and (4), respectively, hereafter we concentrate on specifications (1) and (5) in each region. In most of our specifications, the squared term of each type of sectoral aid are negative. Specifications 1 and 2 show the results for African region. In Africa, agriculture aid, interacted with institutional quality, has higher positive and significant effect on economic growth than education and health aids. This is a very important finding for policy perspectives. Since the African region is relatively poorer than Asia and South America, the stages of development vary immensely across these regions. According to Rostow (1965), growth of the agricultural sector is the first stage of development in all countries for a successful take-off. Most of the African countries are trapped by absolute deterioration of institutional quality for many years as evidenced by a very low annual percentage change in the measure of institutional quality. However, foreign aid comprises the highest proportion of government budgets of African countries (Knack, 2004). If foreign aid enhances the productivity of the agricultural sector, resources could then be transferred to other sectors, such as manufacturing and services, for a successful transition to a higher growth path. Since our findings show that agriculture aid, interacted with institutional quality, is more effective in Africa, a first attention should be given to improve institutional quality, and then donors could provide more aid flows in the agricultural sector to improve production. In contrast, we find in specification 3 that Asian countries are more efficient in using health aid (0.0627, t-stat 2.14). Similarly, the individual (0.0677, t-stat 2.08) and interaction (3.5724, t-stat 1.83) effects of health aid and institutional quality are positive and significant in Asia as reported in specification 4. This result is not surprising, as many factors

19

significantly contribute for the efficient use of health-targeted aid in Asian countries. For example, in the last few decades, more of the Asian countries paid more attention to health sector reforms to achieve efficiency and to generate new resources for basic and general health care services (WHO, 2014). In addition, increasing health expenditure as a percentage of GDP leads to lowest proportion of maternal and child morality in Asia than Africa and South America (Alkema et al. 2016). We also find that the coefficients of education and health aids (in specifications 5 and 6) are positive and significant in South America. Specification 5 shows that a one percentage point increase in education aid and health aid contributes 0.6444 and 0.0312 percentage point increase, respectively, in growth of per capita GDP growth. In specification 6, comparing the individual and joint effects, we find that education aid is more effective (individual effect: 0.5361, t-stat 2.60; joint effect 1.1072, tstat 2.04) as compared to health and agricultural aids in South America. All these findings are in favour of hypothesis H3 that there is a significant variation of aid effectiveness among regions. 4.2.

Marginal effect of institutional quality and foreign aid

To compare the effects among sectoral foreign aids, we use continuous data on institutional quality, based on the derivative term and plot them against the marginal effects of all three different types of aids in a single graph. For example, following eq. (1), the derivative of growth with respect to education aid could be calculated from Tables 3 and 4 as below: GHIJ GKBIJLM

= ∅ + ∅ × "#$% + 2 × ∅7 × +2−4 × "#$%+2

(3)

Similarly, we can calculate marginal values for health and agricultural aids. In Figures 3a-3d show the incremental effect of all three types of foreign aid on economic growth as

20

institutional quality increases for the full sample and the three regions, respectively. The marginal effect is found linear and increasing as value of institutional quality improves. These results are in line with previous studies that show a threshold effect of institutional quality to realise the positive effect of foreign capital flows on economic growth (Slesman et al., 2015a; 2015b; Arya et al., 2019).12 The main findings can be summarised as follows. First, for the full sample, the marginal effects of education aid are consistently higher than agricultural aid and health aid. There is a hierarchical preference order among sectoral aid effectiveness, where education aid effectiveness is maximum, followed by health aid and then agriculture aid. Second, as the level of institutional quality improves, the marginal effects of health aid significantly converges to the marginal effect of education aid. Thus, at very low level of institutional quality, all three types of aids are effective, however, as the level of institutional quality improves, education and health aids become more effective and at the very high level of institutional quality, the effect of agriculture aid is significantly lower than the other two types of aids.

12

The optimum size of institutional quality across different studies are not readily comparable. This is because the studies use different set of countries in their sample, examine different types of capital flows and due to scaling issues pertaining to ICRG index, which measures institutional quality in these countries. However, all these studies show the same trend that better institutional quality improves the efficiency of foreign capital flows in improving economic growth of the recipient countries.

21

Figure 3: Marginal effects of sectoral aid on economic growth

Note: Following equation (3), continuous data on institutional quality and the coefficients derived in Table 3 (specification 5) for full sample and Table 4 (specifications 2, 4 and 6) for regions are used to calculate the marginal values of all three types of aid. The marginal values are plotted against the continuous values of ICRG index in a scale of 0 to 100. The graphs uniformly assign zero starting value to both axes for comparison purposes.

The effect is again heterogeneous across regions, which supports our hypothesis H3. The marginal effects of education, health and agriculture aids on growth increase as the level of institutional quality increases in Africa, Asia and South America (Figure 3b-3d). However, consistent with the full sample, the marginal effects of education and agriculture aids are higher at different levels of institutional quality in Africa and South America. On the other hand, the marginal effect of health aid is higher than the marginal effects of education and agriculture aids in all level of institutional quality in Asia (Figure 3c). The findings are similar in South America (Figure 3d) as compared to Africa (Figure 3b). The marginal effect of education aid dominates, both at the very low and high levels of institutional quality, 22

showing that education aid is more important for this region. Moreover, the marginal effects of education, health and agriculture aids potentially diverge at the high levels of institutional quality. An important difference between South America and Africa, is that the rate at which the marginal effects of each type of aid widens is significantly more in South America. A stronger positive slope of the marginal effects for education aid suggest that it is more sensitive to improvements in institutional quality. As a result, the benefits of education aid relative to agriculture and health aids, are accelerated by better institutions. All else equal, countries in these regions can optimise the influence of aid flows by directing it into the education sector. This is a significant finding and provides a fresh perspective in understanding the complex relationship between foreign aid and institutional quality. Overall, these results have strong policy implications. Based on the level of institutional quality, the relative importance of foreign aid differs across regions. This strongly supports our hypothesis H3. For example, while improving the institutional quality is absolutely necessary in all three regions, African and South American countries will be benefited more if foreign aid is directed towards the education sectors. In contrast, Asia economies will be benefited if foreign aid flows to the health sectors. Our findings show that providing large amount foreign aid by itself is not the best solution to tackle poverty, and thereby increase economic growth. It is better that international aid organisations set conditions and identify the relative importance of sectoral foreign aids that may initiate reforms in economic, financial and political environments before they deliver aid in a region. This is supported by the fact that in all regions, the marginal effect of sectoral aid on economic growth is significantly improved when the level of institutional quality increases. Thus, consistent with the hypothesis H2, our findings reveal that making sectoral aid more systematically conditional on institutional quality would significantly promote its effect on economic growth of the recipient countries.

23

4.3.

Robustness checks

In this section, we conduct a series of robustness checks of our baseline findings. First, we re-estimated all specifications using labour productivity growth as an alternative outcome variable. Labour productivity growth is measured by percentage change in output per worker in the economy. More productive labour implies less utilisation of resources, highly skilled workers, healthy labour force and association with better technological progress. This implies higher income per capita growth in the long run. From growth accounting purposes, based on neoclassical growth models, the GDP per worker captures a direct contribution of skilled labour towards the living standards of an economy (Bosworth and Collins, 2003). Thus, labour productivity growth directly contributes to output growth (i.e. GDP per capita growth) and provides a pathway to sustain improvements in quality of life (Krugman 1994; Tang and Wang, 2004). Other studies that use labour productivity growth as a measure of income differences across countries and as a measure of economic growth are Broadberry (1998), Broadberry and Irwin (2006), McLean (2007) and Acemoglu and Dell (2010). We report the individual and interaction effect of sectoral aid and institutional quality on labour productivity growth for the full sample. Consistent with our findings in Table 3, all specifications in Table 5 show that the individual and interaction effect of sectoral aid and institutional quality are positive and highly significant. Specification 1 shows that a one percentage point increase in education aid and health aid contributes 0.2413 and 0.0956 percentage point increase, respectively, in growth of per capita GDP growth. Moreover, in specification 5 the estimated coefficient on education aid is larger than the estimated coefficients on health aid (0.0902 and 0.0188, respectively). The coefficient of ICRG index is

24

positive and significant in four out of five specifications, suggesting that institutional quality plays an important role in augmenting labour productivity growth.13 [Insert Table 5] Second, we consider system GMM14, proposed by Blundell and Bond (1998), which enables us to utilize “internal” instrumental variables. It allows us to estimate a regression equation both in differences and levels simultaneously and the inclusion of lagged dependent variable to capture possible persistence. It specifically uses lagged levels as instruments in the differenced equation and uses lagged differences as instruments in the level equations. The results from system GMM are reported in Table 6. The findings are consistent with those reported in Table 3 showing that aid capital targeted to education sector has higher significantly positive effect on per capita income growth as compared to health and agriculture aids. Moreover, education aid has higher effect on growth after interacted with institutional quality. The coefficient of institutional quality is positive and significant in all specifications in Table 6. [Insert Table 6] Third, to utilise an alternative measure of institutional quality, we have proxied institutional quality by an indicator of government ideology: democracy15. The extant aid literature suggests that the effectiveness of foreign aid can be determined by the level of democracy in the recipient countries. Higher is the degree of democracy in the aid recipient countries, foreign aid is expected to stimulate economic growth further (Svensson, 1999). 13

In unreported results, we also re-estimated the specifications for each region separately, and the results are consistent with our earlier findings in Table 4. 14 Following Bruno (2005), we also ran Least Squares Dummy Variable Corrected (LSDVC) estimations for both full and regional samples as alternative estimations procedures, and the results are consistent with the baseline findings. These results are presented in Appendix 4. 15 We obtained data for democracy from Polity IV database. The database provides a 10-point scale of democracy based on evaluations of how higher government officials are elected, and of whether there are any effective institutional checks on their power. The values 10 and 0 indicate the government is strongly democratic and autocratic respectively.

25

Table 7 presents that the effect of sectoral aids on economic growth when they are interacted with the level of democracy in the recipient countries. Consisted with the literature and our baseline findings in Table 3, education aid, after interacted with democracy, has higher significantly positive effect on economic growth. [Insert Table 7] Fourth, we examine the interaction effect of sectoral aid with sub-indices of the ICRG index, namely economic, financial and political risk ratings, respectively. The reasons we breakdown aggregate ICRG index into sub-indices are two-fold: first, this procedure prevents the loss of important information caused during the aggregation method of the ICRG index. The aggregate index is a weighted average of 23 different sub indicators that can be broadly categorised into political, economic and financial risk components.16 Second, each type of risk rating could have a varying effect on economic growth when interacted with a specific channel of foreign aid. While all types of foreign aid is conditional on the institutional characteristics, from a policy perspective the effects of political risks could supersede the effects of economic or financial risks if the countries are politically unstable. The same could be true if economic and financial risks are higher in the aid recipient countries. To address this issue, we check the consistency of our baseline findings by interacting each sectoral aid with three different types of risk ratings based on the ICRG index. Consistent with the baseline findings, Table 8 shows that the effects of education and health aids on economic growth increase after interacted with each component of the ICRG index.17 Interestingly, agriculture aid has insignificant effect on economic growth when it is interacted with economic and financial risk ratings. However, it has significantly positive effect after interacted with political risk rating. Moreover, each sectoral aid has higher effect on 16

The details on the sources and composition of ICRG index is provided in Appendix 2. In other unreported results, we examine the interaction effect of sectoral aids with democracy and sub-indices of the ICRG index at regional level and the results are qualitatively similar with the ones reported in Table 4. 17

26

economic growth after interacted with political risk rating than economic and financial risk ratings. Our results suggest that political risk ratings are more important for aid effectiveness in developing countries than other types of risk ratings. Thus, international aid organisations need to pay more emphasis on the political environments of the recipient countries before they deliver the aid capital. [Insert Table 8] Finally, following Burnside and Dollar (2000), we check the robustness of our results from the panel 2SLS regressions by excluding countries, which have highest per capita GDP growth in our full sample and across regions18. This is to check whether these outliers lead to over-estimate or under-estimate of the individual and interaction effects of sectoral aid and institutional quality on economic growth in the range where most of our observations are located. Next, Table 9 reports the effect of sectoral aid and the measure of institutional quality on growth after excluding the outliers. Our results are qualitatively similar with the findings reported in Table 3 that the estimated coefficients of the individual and interaction of education aid and institutional quality are larger than that of health and agriculture aids. Specifications 5 shows a one percentage point increase in education aid contributes 0.1988 percentage point increase in growth of per capita GDP which is statistically significant at the 5% level. The interaction effect of education aid and institutional quality (1.6514, t-stat 1.74) is also higher than the interaction effect of health and agriculture aids with institutional quality.19 Overall, the effects of institutional quality and sectoral aids are robust to alternative outcome variable and omission of outliers from the sample. 20

18

The list of excluded countries is provided in Appendix 3 In unreported results, we also re-estimated the specifications for each region separately after removing the outliers, and the results are consistent with our earlier findings in Table 4. 20 In other unreported results, we have excluded the sectoral foreign aid provided in the years 2008 and 2009 to capture the financial turmoil during the Global Financial Crisis. We find that the qualitative nature of the baseline results remains unchanged. 19

27

[Insert Table 9] 5. Conclusion In this paper, we address two important issues. First, instead of using aggregate aid, we use sectoral foreign aids and examine their effects on economic growth. From a policy perspective, this is pertinent since aid is never disbursed in an aggregated form. The underlying assumption is in developing countries, each sector is structurally different and experience different productivity levels. Thus, by comparing the effects of sectoral aids on aggregate growth and across regions, policymakers will be able to decrease uncertainty and direct foreign aid to the most productive sector in the region. The second but related issue is aid effectiveness is conditional on institutional quality. With better rule of law, lower corruption and stable economic and financial thresholds, countries face less uncertainty, which enhances cooperation between the donor and the receiver countries. Thus, the relative importance of each sectoral aid differs based on how each type of aid interacts with institutional quality. We examine these issues with a comprehensive dataset covering 74 countries from Africa, Asia and South America, and covers the period 1980-2016. Consistent with our hypotheses H1 and H2, we find that sectoral aids influence economic growth positively and conditional on the level of institutional quality of the aid recipients. However, among the three types of aid, education aid is more effective on economic growth of developing countries. The marginal value of education aid conditional on institutional quality significantly improves the growth in long run output and labour productivity. Also, consistent with our hypothesis H3, our findings show that certain types of aid are more effective in certain regions. While education aid is more effective in South America, health aid is more effective in Asia and agricultural aid is more effective in Africa. Thus, there is further opportunity for policymakers to determine why some aid channels are

28

less effective and how to make them more productive in future. Moreover, we find the relative importance of aids varies at high and low levels of institutional quality. We find that as the level of institutional quality improves, the gap between the marginal effect of education, health and agricultural aids widens. Thus, it would be desirable to shift aid towards education sector, as the marginal effect is relatively higher on economic growth at the high levels of institutional quality. This is a very important observation that may help donors to direct foreign aid to the most effective sector based on the level of institutional quality. Policy-makers also need to be mindful of the sensitivity of foreign aid to the quality of institutions in developing countries. Foreign aid is more effective in the agriculture sector in Africa, where institutional quality is relatively weaker as compared to other developing regions. However, countries with better institutions may find it more desirable to promote higher growth by directing aid flows to education (South America) and health sectors (Asia). Since the relative importance of foreign aid changes for regions, the focus on aid effectiveness also shifts from one sector to another. This is a significant finding and has strong policy implications for international aid organisations. Future research should be directed towards capturing this heterogeneous effect of regional foreign aids based on project-based

case

studies

in

each

sector.

29

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35

Variables

Mean

Std. Dev.

Median

Min

Max

Growth of per capita income

1.30

3.76

0.03

-0.97

3.87

Education aid

0.80

0.72

0.61

0.00

3.80

Health aid

1.35

1.01

1.23

0.00

5.87

Agriculture aid

0.90

0.80

0.80

0.00

5.07

ICRG index

40.47

8.16

41.29

8.68

74.00

Initial income

8.13

1.01

8.11

5.12

10.84

Average years of schooling

9.62

3.05

10.00

2.10

17.40

Trade openness

64.97

42.82

55.32

0.02

531.74

Life expectancy

59.20

9.90

59.27

27.08

80.59

Inflation

1.49

1.45

1.61

-4.37

9.37

Broad money (M2/GDP)

3.41

0.59

3.35

1.51

5.03

Gov. expenditure (%GDP)

14.68

7.64

13.34

1.36

129.26

Ethnic fractionalization

0.53

0.25

0.60

0.04

0.91

Population

16.23

1.60

16.38

11.07

20.93

Interest rate differential

-4.99

40.11

-2.02

-571.77

104.34

Table 1: Summary Statistics Notes: We calculate education, health and agriculture aid by dividing the total amount of aid provided to each sector to real GDP, and multiply by 100. Both aid and real GDP are measured based on the constant $US 2010. We notice three surprising values. (1) The largest deviation of the maximum value of openness from its mean value. This value is observed in Equatorial Guinea in 2007. (2) The largest deviation of the minimum value of life expectancy from its mean value. This value is observed in Rwanda in 1993, and the main causes were civil conflicts and deep-rooted diseases (see Binagwaho et al. 2014). (3) The largest deviation of the minimum value of interest rate differential from its average value. This is observed in Zimbabwe in 2007 mainly due to hyperinflation (McIndoe, 2009).

Table 2: Total aid commitment by major donors in constant 2010 US$ (billions): 1980-2016 Donor

Total committed aid

Shares from total aid

United States

601.2

0.184

Japan

488.2

0.165

Germany

301.8

0.110

France

222.5

0.059

United Kingdom

147.1

0.041

Netherlands

158.0

0.038

Canada

87.1

0.030

Italy

69.0

0.022

Sweden

74.2

0.027

Norway

58.9

0.017

Spain

49.1

0.015

Australia

53.0

0.017

Korea

16.3

0.007

36

Notes: Source: AidData database. The shares of total aid are calculated as a fraction of total aid committed by all donors.

37

Table 3: The Impact of Sectoral Aid and Institutional Quality on Growth: Panel 2SLS estimates Without interaction

Education aid with ICRG

Health aid with ICRG

Agriculture aid with ICRG

(1)

(2)

(3)

(4)

0.2072** (2.10) 0.0564* (1.69) -0.0574 (-0.72)

0.1473** (2.45)

Full Sample

Variables Education aid Health aid Agriculture aid

0.0219* (1.98) 0.1451 (1.34) 0.8833** (2.00)

(Education aid) x ICRG

0.5504** (2.10)

(Health aid) x ICRG

0.5270* (1.71)

(Agriculture aid) x ICRG -0.2864 (-1.03)

(Education aid)2 x ICRG

-0.0004 (-0.14)

(Health aid)2 x ICRG (Agriculture aid)2 x ICRG ICRG index Initial income Average years of schooling Trade openness Life expectancy Inflation Broad money (M2/GDP) Government expenditure (%GDP) Population Weak ident. test (Wald F stat.)

0.0194* (1.80) -1.0887*** (-5.01) 0.2259 (0.48) 0.0023 (0.02) 0.0462*** (4.81) -0.0943 (-1.34) 0.5356*** (3.35) 0.1290 (0.36) -0.0701 (-0.61) 15.45

0.0241*** (4.08) -1.1892*** (-8.13) 0.2573* (1.69) -0.0073 (-0.07) 1.8780*** (4.86) -0.0851* (-1.70) 0.5805*** (3.92) 0.5578*** (3.36) 0.0321 (0.50) 14.42

0.0223*** (2.67) -0.9647*** (-9.02) 0.0109* (1.66) 0.1546* (1.65) 2.6633*** (4.31) 0.0132 (0.30) 0.3154*** (2.75) 0.1935 (0.71) 0.0097 (0.20) 19.035

-0.3413 (-0.49) 0.0351*** (3.65) -0.9935*** (-9.01) 0.1344 (0.21) 0.0696 (0.67) 3.0454*** (2.78) -0.1265 (-1.17) 0.4322* (1.72) 0.5615*** (3.03) 0.0434 (0.30) 21.700

(5) 0.1301* (1.77) 0.1163* (1.68) 0.1029 (1.23) 0.7483** (2.11) 0.4191*** (3.10) 0.0651* (1.69) -0.0003 (-0.98) -1.0285 (-0.90) -0.0001 (-1.52) 0.0259* (1.84) -0.6184*** (-2.63) 0.0931* (1.71) 0.2419 (0.84) 1.2787 (0.99) 0.0305 (0.50) 0.2610 (1.11) -0.0333 (-0.13) -0.0742 (-0.30) 15.010

Notes: The dependent variable is growth of per capita income. Heteroscedasticity and autocorrelation robust t-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5% and 10% level respectively. Education is lagged by 4 years and health and agriculture aids are lagged by 1 year. Similarly, ICRG index, initial income, trade openness, life expectancy, inflation, broad money and gov. expenditure are lagged to address the endogeneity issue. A constant, time and country fixed effects are included in our regressions. In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. We include 2,072 observations and 74 countries in all models.

38

Table 4: Impact of Sectoral Aid and Institutional Quality on Growth in Africa, Asia and South America: Panel 2SLS estimates Variables

Education aid Health aid Agriculture aid

Africa

Asia

South America

Without interaction

Sectoral aid with ICRG

Without interaction

Sectoral aid with ICRG

Without interaction

Sectoral aid with ICRG

(1) 0.1061* (1.74)

(2)

(3)

(4)

0.0803* (1.68)

0.0345* (1.71)

0.0430* (1.68)

(5) 0.6444** (2.01)

0.5361** (2.60)

0.0228* (1.99) 0.0261 (0.48) 0.2704 (1.70) 0.0044** (2.10) 0.3678** (2.03)

0.0627** (2.14) 0.0400 (0.17)

0.0677** (2.08) 0.7207 (1.09) 0.0787** (2.02) 3.5724* (1.83) 0.0148* (1.65)

0.0816* (1.66) 0.0991 (1.08)

(Education aid) x ICRG (Health aid) x ICRG (Agriculture aid) x ICRG

0.0312* (1.91) 0.0789* (1.67)

(6)

-0.1833 (-2.08) 0.1385* (1.68) 1.1072** (2.04) 0.4594*** (3.99) -0.0700 (-0.86)

(Education aid)2 x ICRG

-0.0001 (-0.44)

-0.0001 (-0.50)

0.0296* (1.65)

(Health aid)2 x ICRG

-0.0131 (-0.02)

-1.6560 (-1.12)

0.0954** (2.08)

(Agriculture aid)2 x ICRG

-0.0001 (-0.89)

-0.0004 (-0.96)

0.0453*** (3.68)

ICRG index Initial income Average years of schooling Trade openness Life expectancy Inflation Broad money (M2/GDP) Government expenditure (%GDP) Population

0.0305*** (2.79) -0.9261*** (-3.34) 0.0460** (2.03) 0.2554 (1.21) 0.0402*** (3.46) -0.1554 (-1.42) 0.6668** (2.40) 0.0333 (0.12) -0.1063 (-0.74) 19.76

0.0202* (1.65) -0.9199*** (-5.87) 0.4530 (0.67) 0.1181 (0.45) 2.3181*** (2.63) 0.0525 (0.73) 0.0893 (0.38) -0.0061 (-0.02) 0.2052 (1.33) 15.08

0.0180** (2.00) -1.1308*** (-3.86) 0.8410* (1.70) -0.1109 (-0.61) 0.0413 (1.34) 0.0633 (0.52) 0.5951* (1.82) 0.1980 (0.46) 0.1282 (1.23) 16.66

0.0156 (0.69) -0.7104*** (-2.82) 0.8093 (0.42) -0.5900 (-1.17) 1.8173* (1.69) 0.1276 (0.81) 0.6300* (1.72) 0.1905 (0.31) -0.1985 (-0.93) 16.87

0.0038 (2.00) -1.1452*** (-3.91) 0.0196 (0.12) -0.1509 (-0.28) 0.0590** (2.13) -0.1497 (-0.64) 0.9235* (1.69) -1.6897* (-1.90) -0.2178 (-1.22) 14.83

0.0090* (1.77) -0.6281*** (-9.84) 0.0678*** (2.67) 0.3514*** (10.10) 3.1500*** (6.98) 0.0851*** (4.60) 1.1569*** (10.05) 0.7631*** (7.07) -0.1421*** (-2.91) 17.30

Weak ident. test (Wald F stat.) Notes: The dependent variable is growth of per capita income. Heteroscedasticity and autocorrelation robust t-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5% and 10% level respectively. Education is lagged by 4 years and health and agriculture aids are lagged by 1 year. Similarly, ICRG index, initial income, trade openness, life expectancy, inflation, broad money and gov. expenditure are lagged to address the endogeneity issue. A constant, time and country fixed effects are included in our regressions. In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. We include 1,317observations and 45 countries in Africa, 559 observations and 19 countries in Asia, and 296 observations and 10 countries in our models.

39

Table 5: Impact of Sectoral Aid and Institutional Quality on Labour Productivity growth: Panel 2SLS estimates Variables

Without interaction

Education aid with ICRG

Health aid with ICRG

Agriculture aid with ICRG

Full Sample

(1)

(2)

(3)

(4)

(5)

0.2413* (1.99) 0.0956* (1.81) 0.0499 (0.46)

0.4944*** (4.05)

0.0902** (2.17) 0.0547** 0.0188* Health aid (2.19) (1.66) 0.2880 0.0352 Agriculture aid (0.37) (0.25) 0.7480* 0.2708** (Education aid) x ICRG (1.77) (2.00) 0.0573** 0.2010* (Health aid) x ICRG (2.06) (1.69) 0.0075** 0.0066* (Agriculture aid) x ICRG (1.90) (1.80) -0.6605 -0.0005 2 (Education aid) x ICRG (-1.17) (-1.09) -0.0002 -0.9253 2 (Health aid) x ICRG (-0.27) (-0.47) -0.1600 -0.0001 2 (Agriculture aid) x ICRG (-1.08) (-0.06) 0.7274** 0.0210* 0.0142 0.0347** 0.0548** ICRG index (2.07) (1.75) (0.81) (2.57) (2.28) -0.9564*** -1.4879*** -0.7374*** -0.4534*** -0.3917*** Initial income (-4.13) (-5.01) (-2.65) (-4.30) (-2.88) 0.0309* 0.2630 0.0040 0.0296 0.5053** Average years of schooling (1.77) (0.36) (0.04) (0.35) (2.00) 0.2291* 0.1442 0.1411 -0.0027 0.3813 Trade openness (1.93) (0.69) (0.77) (-0.01) (0.77) 0.0040 3.2269*** 0.0150 5.0484*** 2.4260 Life expectancy (0.34) (4.12) (0.73) (4.01) (1.10) -0.0233 -0.2536** -0.0691 -0.0266 -0.0218 Inflation (-0.23) (-2.49) (-0.76) (-0.20) (-0.21) 0.4222*** 0.0723 0.6644*** 0.1481 0.2499* Broad money (M2/GDP) (2.63) (0.24) (2.88) (0.58) (1.88) 0.1767 -0.1972 -0.3985 -0.2277 0.0371 Government expenditure (%GDP) (0.40) (-0.59) (-0.60) (-0.79) (0.09) 0.0791 0.1751 -0.4218*** -0.2320 0.0154 Population (0.47) (1.34) (-3.76) (-1.75) (0.04) 14.85 16.66 15.02 21.02 12.80 Weak ident. test (Wald F stat.) Notes: The dependent variable is growth of per capita income. Heteroscedasticity and autocorrelation robust t-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5% and 10% level respectively. Education is lagged by 4 years and health and agriculture aids are lagged by 1 year. Similarly, ICRG index, initial income, trade openness, life expectancy, inflation, broad money and gov. expenditure are lagged to address the endogeneity issue. A constant, time and country fixed effects are included in our regressions. In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. We include 2,072 observations and 74 countries in all models.

Education aid

40

Variables

Lag 1 of growth Education aid Health aid Agriculture aid

Aid without ICRG [1]

Education aid with ICRG [2]

Health aid with ICRG [3]

Agriculture aid with ICRG [4]

Full Sample [5]

0.8988*** (14.04) 0.0082*** (10.00) 0.0010*** (8.22) 0.0002 (15.00)

0.7106*** (10.67) 0.0035*** (9.77)

0.7073*** (14.71)

0.960*** (5.03)

0.7012*** (5.00) 0.0054*** (3.30) 0.0022** (2.04) 0.0520 (0.33) 0.0415*** (7.61) 0.0103** (2.49) 0.0222* (1.75) -0.0012** (-2.24) -0.0007* (-1.85) -0.0001* (-1.69) 0.0021*** (7.85) -0.0509*** (-19.66) 0.1480 (0.84) 0.0005*** (9.93) 0.0061*** (14.2) -0.0001*** (-21.80) 0.0001 (0.51) -0.0022*** (-13.84) -1.2320*** (-15.29) 271 0.977 0.165

0.0024*** (5.76) 0.0012 (0.78) 0.0174*** (6.25)

Education aid * ICRG

0.0111*** (3.44)

Health aid * ICRG

0.0130** (2.50)

Agriculture aid * ICRG (Education aid)2 * ICRG

(-0.0014* (-1.89)

(Health aid)2 * ICRG

-0.0009* (-1.69)

(Agriculture aid)2 * ICRG ICRG index Initial income Average years of schooling Trade openness Life expectancy Inflation Broad money (M2/GDP) Government expenditure (%GDP) Population Number of instruments Hansen-test of over id. restrictions AR (2) (test for serial correlation)

0.0020*** (11.638) -0.0514*** (20.01) 0.1483*** (2.71) 0.0005*** (11.22) 0.0059*** (13.93) -0.0001*** (-20.25) 0.0002 (1.59) 0.0018*** (12.49) -1.3953*** (-20.50) 365 0.759

0.0019*** (10.14) -0.0454*** (-12.77) 0.0966 (0.56) 0.0006*** (8.87) 0.0052*** (9.34) -0.0001*** (-19.41) 0.0001 (0.85) 0.0019*** (11.28) -1.3749*** (-19.72) 255 0.893

0.0015*** (5.74) -0.0474*** (-16.32) 0.1936 (1.12) 0.0005*** (15.21) 0.0067 (13.45) -0.0001 (-18.09) 0.0001 (0.14) 0.0016*** (11.24) -1.3823*** (-22.45) 285 0.668

-0.0001** (-2.05) 0.0018*** (12.32) -0.0493*** (-14.60) 0.1496 (0.86) 0.0005*** (14.26) 0.0063*** (15.38) -0.0001*** (-16.34) -0.0001*** (-0.32) 0.0018*** (12.09) -1.2618*** (-32.57) 263 0.938

0.467

0.332

0.968

0.354

Table 6: The effect of sectoral aid and institutional quality on growth: System-GMM (Blundell-Bond procedure)

41

Notes: The dependent variable is growth of per capita income. Except, ethnic fractionalisation, all variables are considered as endogenous. All variables are expressed in logarithmic form. ***, **, and * denote significant at the 1%, 5% and 10% level respectively. Heteroscedasticity and autocorrelation robust t-statistics are in parentheses. A constant and country and time fixed effects, which are not reported in the table, are included to eliminating cross-sectional dependence following Sarafidis et al. (2009). In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. This study includes 2,072 observations and 74 countries in all specifications.

42

Variables Education aid

[1]

[2]

[3]

0.493* (1.67)

Health aid

0.410* (1.68)

Agriculture aid (Education aid) x democracy

0.647** (2.10)

(Health aid) x democracy

-0.192 (-0.39) 0.496** (2.00)

(Agriculture aid) x democracy (Education aid)2 x democracy (Health aid)2 x democracy

0.365* (1.65) -0.032 (-0.13) -0.023 (-0.09)

(Agriculture aid)2 x democracy Democracy Initial income Average years of schooling Trade openness Life expectancy Inflation Broad money (M2/GDP) Government expenditure (%GDP) Population Weak ident. test (Wald F stat.)

0.053* (1.74) -0.651*** (-3.25) 0.181* (1.71) -0.181 (-0.72) -0.021 (-1.63) -0.158 (-1.52) 0.260* (1.69) -0.180 (-0.49) 0.029 (0.65) 17.01

0.479*** (3.77) -0.585*** (-5.63) 0.165 (0.83) -0.180 (-0.60) -0.016 (-1.15) -0.180 (-0.60) 0.296* (1.87) -0.244 (-0.65) 0.028 (0.41) 10.87

-0.156 (-0.86) 0.542*** (4.08) -0.700*** (-6.98) 0.309* (1.73) -0.129 (-0.32) 0.027* (1.97) 0.034 (0.64) 0.402** (2.38) -0.284 (-0.90) 0.054 (1.19) 10.66

Table 7: The effect of sectoral aid and democracy on growth: Panel 2SLS estimation Notes: The dependent variable is growth of per capita income. Heteroscedasticity and autocorrelation robust tstatistics are in parentheses. ***, **, and * denote significance at the 1%, 5% and 10% level respectively. Education is lagged by 4 years and health and agriculture aids are lagged by 1 year. Similarly, democracy, initial income, trade openness, life expectancy, inflation, broad money, expected years of schooling and gov. expenditure are lagged to address the endogeneity issue. A constant, time and country fixed effects are included in our regressions. We also run regressions following specifications [1] and [5] of Table 3, and we find consistent results. In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. We include 1,101 observations and 74 countries in all models.

43

Variables Education aid

Interacted with economic risk rating

0.0437* (1.91)

0.3584* (1.77) 0.0441** (2.00)

Health aid

Interacted with political risk rating 0.5937* (1.88)

0.4689* (1.70) 0.4391 (0.16)

Agriculture aid (Education aid) x economic risk rating

Interacted with financial risk rating

0.0132** (2.11) 0.1884 (0.16)

0.4109 (0.14)

0.9536* (1.80) 0.8627** (2.31)

(Health aid) x economic risk rating

0.0264* (1.68)

(Agriculture aid) x economic risk rating

0.7046** (2.00)

(Education aid) x financial risk rating

0.5933** (2.09)

(Health aid) x financial risk rating

0.2970 (0.14)

(Agriculture aid) x financial risk rating

0.9726* (1.80)

(Education aid) x political risk rating

0.7035*** (3.00)

(Health aid) x political risk rating

0.0979** (2.00)

(Agriculture aid) x political risk rating Economic risk rating

0.1145** (2.06)

0.1586* (1.65)

0.0226 (0.11) 0.0450* (1.70)

Financial risk rating

0.0494 (0.79)

0.0274 (0.20)

-0.6488*** (-4.45) 0.2776 (1.20) 0.2230 (0.94) 1.2432** (2.24) -0.1352 (-1.11) 0.2917* (1.83) 0.5427 (1.81) 0.0113 (0.22)

-0.6699*** (-5.68) 0.5963* (1.65) 0.3526 (1.77) 1.1331* (1.96) -0.0500 (-0.86) 0.2065 (1.24) 0.4906** (2.04) 0.0543 (1.16)

-0.6690*** (-5.68) 0.4169* (1.97) 0.3433** (2.19) 2.0567*** (3.40) 0.0158 (0.27) 0.3760* (1.97) 0.2209 (0.60) 0.0920 (1.31)

-0.6786*** (-5.92) 0.5052* (1.68) 0.3404* (1.70) 1.1122* (1.96) -0.0534 (-0.90) 0.2122 (1.27) 0.4858** (2.03) 0.0539 (1.12)

-0.6366*** (-4.14) 0.1856 (0.82) 0.2283 (0.97) 1.1758** (2.02) -0.1480 (-1.16) 0.2810* (1.68) 0.6028* (1.90) 0.0118 (0.22)

-0.6595*** (-6.42) 0.3549* (1.81) 0.3473** (2.13) 2.1231*** (3.43) 0.0308 (0.49) 0.3835** (2.10) 0.2195 (0.66) 0.1038 (1.40)

0.2250* (1.74) -0.5758*** (-3.44) 0.9097* (1.68) 0.1382 (0.52) 1.5295** (2.22) -0.1534 (-1.11) 0.0124 (0.05) 0.3457 (0.96) -0.0149 (-0.19)

11.77

10.78

10.49

12.01

10.87

12.04

10.36

Political risk rating Initial income Average years of schooling Trade openness Life expectancy Inflation Broad money (M2/GDP) Government expenditure (%GDP) Population Weak ident. test (Wald F stat.)

0.1618*** (3.35) -0.6212*** (-3.90) 0.1465 (0.47) 0.2514 (0.98) 1.2187** (2.41) -0.0323 (-0.52) 0.1776 (1.24) 0.4463* (1.71) 0.0807 (0.76)

0.9597 (0.70) -0.5955*** (-3.31) 0.1235 (0.29) 0.2824 (1.42) 2.1032*** (2.95) 0.0449 (0.82) 0.2667 (1.53) 0.1877 (0.71) -0.1034* (-1.75)

10.28

12.07

Table 8: The effect of sectoral aid and sub-index of ICRG index on growth: Panel 2SLS estimation Notes: The dependent variable is growth of per capita income. Heteroscedasticity and autocorrelation robust t-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5% and 10% level respectively. Education is lagged by 4 years and health and agriculture aids are lagged by 1 year. Similarly, risk ratings, initial income, trade openness, life expectancy, inflation, broad money, expected years of schooling and gov.

44

expenditure are lagged to address the endogeneity issue. A constant, time and country fixed effects are included in our regressions. We also run regressions following specifications [1] and [5] of Table 3, and we find consistent results. In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. We include 2,014 observations and 74 countries in all models. We also included sectoral aid squared interacted with each risk rating and the results are consistent with those reported in Table 3. We did not report the results to save space.

Table 9: Impact of Sectoral Aid and Institutional Quality on output growth (omitting outliers): Panel 2SLS estimates Variables

Education aid Health aid Agriculture aid

Sectoral aid without ICRG

Education aid with ICRG

Health aid with ICRG

Agriculture aid with ICRG

Full Sample

(1) 0.2359** (2.08) 0.0683* (1.68) 0.0176 (0.18)

(2) 0.1184** (2.08)

(3)

(4)

(5) 0.1988** (2.00) 0.1158* (1.97) 0.0078 (0.39) 1.6514* (1.74) 0.6193** (2.00) 0.3286* (1.88) -0.8837 (-1.58) -0.6744 (-1.27) -0.0001 (-0.39) 0.0275* (1.80) -0.5978*** (-3.65) 0.6895 (1.31) 0.1078 (0.51) 1.5826 (1.26) -0.0002 (-0.11) 0.5484** (2.37) -0.3259 (-1.35) -0.0946 (-0.58) 19.99

0.0291** (2.00) 0.0061 (0.91) 0.7994* (1.88)

(Education aid) x ICRG

0.2998** (2.11)

(Health aid) x ICRG

0.0432** (2.11)

(Agriculture aid) x ICRG -0.5510 (-1.57)

(Education aid)2 x ICRG

-0.0001 (-0.67)

(Health aid)2 x ICRG (Agriculture aid)2 x ICRG ICRG index Initial income Average years of schooling Trade openness Life expectancy Inflation Broad money (M2/GDP) Government expenditure (%GDP) Population Weak ident. test (Wald F stat.)

0.0121** (2.00) -1.0728*** (-4.54) -0.4098 (-0.81) 0.0770 (0.41) 0.0510*** (4.63) -0.0909 (-1.21) 0.4404** (2.58) 0.1841 (0.31) -0.0717 (-0.61) 30.97

0.0254* (1.85) -1.0847*** (-8.06) 0.0093 (0.02) -0.0106 (-0.10) 2.1047*** (4.72) -0.0913* (-1.72) 0.6181*** (3.70) 0.5294*** (3.06) 0.0252 (0.38) 55.40

0.0059 (0.38) -1.0483*** (-4.04) -0.1649 (-1.09) 0.2584 (1.58) 4.7095** (2.12) -0.0598 (-0.71) 0.0157 (0.04) 0.3234 (0.49) 0.0270 (0.27) 20.00

0.0001 (1.52) 0.0042 (0.38) -0.8585*** (-14.18) -0.2749 (-0.62) -0.0289 (-0.28) 3.3586*** (4.21) -0.0787 (-1.28) 0.2373 (1.15) 0.1610 (0.61) -0.2129** (-2.41) 24.50

45

Notes: The dependent variable is growth of per capita income. Heteroscedasticity and autocorrelation robust t-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5% and 10% level respectively. Education is lagged by 4 years and health and agriculture aids are lagged by 1 year. Similarly, ICRG index, initial income, trade openness, life expectancy, inflation, broad money and gov. expenditure are lagged to address the endogeneity issue. We include 1, 960 observations and 70 countries in all models. Following Burnside and Dollar (2000), four countries (Botswana, Equatorial Guinea, Bhutan and Cambodia) are excluded for robustness checks due to their outlier per capita GDP growth rate.

46

Appendix 1: Foreign aid and aggregate economic growth Consider the classic steady state equilibrium of Solow-Swan model21 as described in Figure A1. While Figure A1(a) shows the transitory growth effects of foreign aid, Figure A1(b) shows the permanent growth effects after foreign aid facilitates investment in human capital and technology embodied capital stock. In Figures A1(a) and 1(b), suppose a developing country is endowed with low level of capital stock and thus positioned at the far left-hand side of the capital stock axis (at the point K0 and corresponding output level at point Y0). At K0, since investment rate is higher than the depreciation rate, the country is expected to grow faster over time towards the steady state equilibrium K* and corresponding output level Y* following the principle of transition dynamics.22 Foreign aid could be perceived as a gift from a foreign nation and adds to the capital stock of the recipient country. Figure A1: Output, investment, depreciation and foreign aid

Fig A1(a): Transitory effects of foreign aid

Fig A1(b): Permanent effects of foreign aid

21

For detailed understanding of the Solow-Swan neoclassical model, see Jones (2016). The principle of transitional dynamics states that farther below its steady state an economy is, in percentage terms, the faster the economy will grow and vice versa. The principle is dictated by the diminishing returns of savings or investment rate and constant returns of depreciation rate in the long run. 22

47

Since investment and depreciation rates are not affected at K0 when this country receives foreign aid, the physical capital stock of the recipient country immediately increases by the amount of the aid, which pushes the economy forward to K1 in Figure A1(a). As a result, output increases from point Y0 to point Y1. Since at K1 the investment rate is still higher than the depreciation rate, the economy continues to grow, however, due to diminishing returns to capital stock in the long run, the output growth rate falls as it gets closer to the steady state equilibrium, K* and Y*. In this setting, foreign aid has a transitory effect and augments the speed at which the economy was approaching the steady state levels of capital stock and output. In contrast, if foreign aid facilitates higher technological progress by building new ideas, better education and higher productivity, the growth effect is permanent in the long run. This is represented by upward shifts of investment and output lines in Figure A1(b). Since the model captures the relationship between economic growth and aggregate foreign aid instead of sectoral aid, the underlying assumption is sectoral aid adds to the capital stock uniformly such that the slope of the production function is unaltered after addition of foreign aid.23 Alternatively, if aid effectiveness in one sector is higher, for example education sector, the extent of the shift would be bigger for education aid as opposed to other types of aids.24 The initial response is increase in capital stock from K0 to K1. However, technological progress and human capital building shift the production path permanently to a new higher level and the steady state level of output increases from Y* to Y**. In this scenario, foreign aid still boosts the speed of reaching the steady state but converges to a higher level of output (Y**) with the same old level of steady state capital stock (K*). The country moves to a new growth path permanently. 23

The aid-augmented capital stock is assumed to depreciate at a constant rate over time. To this date, there is no empirical evidence of which type of sectoral aids are more effective in bearing transitory and/or permanent effects on economic growth of the recipient countries. 24

48

It is also important to note that foreign aid could have a detrimental but transitory effect on economic growth, particularly when the level of capital stock is very close to the steady state (K*) and there is no effect on productivity or technological progress. The effect is shown in Figure A1(a), where the initial equilibrium of the recipient economy is already at the steady state equilibrium level K*. In this case, receipt of foreign aid increases the level of capital stock to K2 and the growth rate becomes negative. This is because depreciation rate is now higher than the investment rate at all levels of capital stock on the right -hand side of the steady state equilibrium K*. The falling growth rates in the long run could be interpreted as a result of higher corruption and dependency syndrome associated with foreign aid. Over time, the economy returns to the steady state equilibrium (K* and Y*) following the principle of transition dynamics. The above theoretical model helps us to understand that the initial position of the recipient country is important to determine whether foreign aid is effective in the long run. Since recipient countries are expected to be at different stages of economic growth and could be endowed with capital stock that is farther below or closer to their steady state equilibrium level, the overall effect on economic growth will vary across countries and over time.

49

Appendix 2: Data Sources and measurement issues Table A1: Variable descriptions and sources Variable Name Real per capita GDP growth

Sectoral aids

Variable Description Annual percentage growth rate of real per capita GDP which is measured by constant $USD 2010. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Indicates the amount of aid capital provided to each sector

Labour productivity

Indicates the ratio of real GDP measured by constant $USD 2010 to the level of employment

Initial income

Log of per capita GDP measured based on the constant $US 2010 at the beginning of the relevant time period. Indicates the average number of years of education received by people ages 25 and older in their lifetime based on education attainment levels of the population converted into years of schooling based on theoretical duration of each level of education attended. It is measured by the consumer price index which reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. It is calculated as the sum of merchandise exports and imports divided by the value of GDP which is measured by constant $US 2010. It indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. It is proxied by the broad money (M2) to real GDP which is measured based on constant $US 2010 It indicates the probability that two randomly selected individuals in a country belong to different ethnolinguistic groups. Expense is cash payments for operating activities of the government in providing goods and services. It includes compensation of employees (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rent and dividends. It is calculated as the difference between the real interest rate of USA (using as the frontier economy) and the real interest rate of each recipient country. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.

Average years of schooling

Inflation

Trade openness Life expectancy

Financial depth Ethnic fractionalisation Government expenditure

Interest rate differential

Population

Source WDI

AidData database The Conference Board Total Economy WDI UNDP

WDI

WDI WDI

WDI Easterly’s Web site WDI

WDI

PWT, version 9.1

Sectoral Aid We have identified three aid-receiving sectors (namely health, agriculture and education sectors) to examine sectoral aid effectiveness. The primary reason for choosing these three sectors is that on average they have received the highest amount of aid by countries in our sample as compared to all other sectors between 1980 and 2016. For example, in the period 1980-2016, developing countries received foreign aid on education,

50

health and agriculture of $USD 63.56 billion, $USD 212.4 billion and $USD 92.97 billion, respectively. These amounts are higher than all other sectors of foreign aid received by these countries, such as energy aid ($USD 6.44 billion) and women empowerment aid ($USD 3.03 billion). These sectors are also considered as the potential stimulators of economic growth in developing countries as evidenced by numerous studies (Rostow, 1990). In addition, among the seventeen global goals of Sustainable devolvement Goals (SDGs) set by the United Nations General Assembly in 2015, eight of them are related to these sectors. More specifically, education-related goals have several targets, such as (1) ensuring all girls and boys complete free, equitable and quality primary and secondary education, (2) ensuring all girls and boys have access to quality early childhood development, care and pre-primary education that can potentially help them to successfully transfer to primary education, (3) building and upgrading education facilities, and creating conducive learning environments for all children. Similarly, health-related goals comprise several targets that can significantly promote children and maternal health outcomes, and end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases. Agriculture-related goals have several targets that can potentially enhance food security particularly in the world poorest countries. The targets in the agriculture sector also include increasing investment in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries (see Le Blanc, 2015). Data on foreign aid is taken from the current (static) research release of the AidData database (version 2.1; see Tierney et al., 2011) spanning 1980-2016. This database combines the broadly used data from bilateral donors disclosed by the OECD’s Creditor Reporting Service (CRS) with many non-OECD bilateral donors and a variety of multilateral financial

51

institutions including regional development banks (most of which are not in the CRS) and the World Bank. It also includes sector-specific funds from the Bill and Melinda Gates Foundation (BMGF) and Global Alliance for AIDS and Vaccinations (GAVI). This database also provides a more comprehensive view of aid across all types of sectors for an extended period relative to the standard CRS. It provides more detailed information about aid to specific sectors, purposes, and projects. It records both bilateral and multilateral aid for each sector in a consistent format. This recording system highly supportive to examine the effect of specific type of aid on overall economic performance of developing countries. Here, aid is defined as the commitments of concessional loans and grants from all donors, including multilateral organisations. Following Wilson (2011), our analysis made no distinction between loans and grants. Since most loans are long-term with low interest rates, we assume that loans will have roughly equivalent effects to grants in the medium to long terms. We use data on aid commitments because, historically, purpose-related information on aid has only been available for commitments, not actual disbursements (Jones and Tarp, 2016). Using the AidData codes, we obtain sectoral aid commitments provided to education, health and agriculture sectors25. We “scaled” sectoral aid as a share of real GDP which is measured at constant $USD 201026. Thus, education aid is calculated as the “sum” of aid transferred to primary, secondary, post-secondary and level unspecified education as a percentage of real GDP. Similarly, health aid is calculated as the “sum” of basic health aid, general health aid and population policies and reproductive health aid as a percentage of real GDP. Finally, agriculture aid is derived as the total amount of aid received by the agriculture sector as a percentage of real GDP.

25

Following Arndt et al. (2010), we treat zero-valued aid observations as zeroes, rather than missing. The standard scaling procedures are aid over real GDP, per capital aid (i.e. aid over population) and aid over government expenditure. However, aid over real GDP scaling procedure is frequently used in the aid-growth nexus literature (see Alesina and Weder, 1999). 26

52

International Country Risk Guide (ICRG) Index We use ICRG index to measure institutional quality in the recipient countries and examine how it moderates the effect of sectoral aid on growth (Bräutigam and Knack 2004; Tavares, 2003). The extant aid literature examines the effect of aggregated aid contingent on policy index, which comprises fiscal, monetary, and trade policies of the recipient countries. However, this index has a potential caveat that it only takes three policies into account. Thus, to address this limitation we use ICRG index which is a broader and well-accepted measure of country-level risk (Knack and Keefer, 1995).27 It includes more than 22 variables in three subcategories of risk: political, financial and economic. It is calculated as a weighted average of political, financial and economic risk in a country. In ICRG index, the highest overall rating (theoretically 100) shows the lowest risk, and the lowest rating (theoretically zero) shows the highest risk. Thus, it is pragmatic to assume that if a recipient country has stable political, financial and economic environments, sectoral aid will significantly improve the long run economic growth of the recipient countries. The data on ICRG is obtained from the international country risk guide index calculated by the Political Risk Services (PRS) group. The data for ICRG is available for more than 140 countries starting from 1984. Since our study covers the period 1980-2016, we use each country’s 1984 figure for the year 1980 to 1983, based on the assumption that institutional factors change slowly over time28.

27

Fiscal and trade policies are explicitly included in the economic risk ratings, and the monetary policies are included in the financial risk ratings. 28 This assumption is commonly used in the literature (see Burnside and Dollar, 2002). We check our assumption by calculating the annual change of ICRG index in each country; and the result shows that there is a slow annual change of ICRG index in each country. For detailed methodology of the calculation of the ICRG index, please consult the following link: https://www.prsgroup.com/about-us/our-two-methodologies/icrg

53

Other Control variables Following the aid-growth literature, we include control variables in our models to investigate the contributions from other factors in the presence of foreign aid, which are significant determinants of growth. The following controls are included: log of initial income, log of inflation, trade openness, life expectancy, average years of schooling, log of broad money (M2) as a percentage of GDP, ethnic fractionalisation, government expenditure (%GDP), interest rate differential, and log of population. It is standard in the empirical growth literature that convergence effect is captured by allowing growth during period t to depend on the log of real per capita GDP at the beginning of the period. Following Fischer (1993), we include log of inflation as a measure of monetary policies. Trade openness facilitates the use of advanced technologies among the trading countries, thereby increases growth (Banerjee and Roy, 2014). It also promotes investments through the use of intermediate goods, new inputs and products (Yanikkaya, 2003). However, other studies, such as Grossman and Helpman (1991) argue that trade reduces growth if the trading countries are “asymmetric” in the sense that they have different technological advancements and resource endowments. Due to the endogeneity issue, our analysis includes one-year lag of trade openness. Life expectancy is added as a control to capture the effect of health on growth. There are two strands of literature about the effect of life expectancy on growth. Cervellati and Sunde, (2011) argue that life expectancy positively contributes to growth. On the other hand, De la and Licandro (1999) finds that life expectancy has positive effect on growth when life expectancy is relatively low; however, the effect is negative in developed countries as the positive effect of a longer life on growth could indeed be offset by an increase in the average age of the workers. Next, financial development contributes to growth through different channels, such as enable small savers to pool funds, creates a wider range of instruments that 54

increase savings, redirects saving from individuals to slow-growing sectors and reduces the problem of adverse selection in the credit markets (Ang, 2010). Since foreign aid acts as a channel of investment in economies, it is important to control for financial sector development in our empirical model. To capture the effect of financial sector development we include the level of broad money (M2) as a percentage of GDP. To capture the effect of human capital on economic growth in presence of foreign aid, we include average years of schooling (Barro, 2001; Benhabib and Speigel, 1994). Also, controlling for education is important in the empirical specification as this captures a direct effect of human capital on economic growth, while the indirect effect on economic growth is captured through the channel of education aid. Ethnic fractionalisation is included to control for the long-term characteristics of countries that affect the growth of a country. Easterly and Levine (1997) argue that ethnic fractionalisation correlate with bad policies, and thereby reduces the growth of a country. Fölster and Henrekso (2001) and Burnside and Dollar (2002) show that government expenditure increases the growth of a country if it is spent for productive purposes. Thus, we include the share of gross government expenditure as a percentage of GDP to capture the effect of government expenditure lagged one year. In recent years, there is a significant difference between developed countries interest rates and developing countries interest rates. To spur investment, developed countries keep their interest rate very low relative to developing countries. On the other hand, developing countries keep their interest rate very high to limit capital outflow and economic instability. However, high interest rate reduces the level of investment, and thereby economic growth

55

(Lutz, 1945). Hence, to capture the effect of shocks in the international markets on the aidreceiving economies, we include interest rate differential in our model29. Lastly, the extant literature shows that donors tend to provide more aid to countries, which have small population size. This is because if countries have small population size, the per capita aid in these countries will be higher than other countries with high population. Thus, donors are more likely to give aid to countries with small population size, which in turn increases donors’ expectations to have more influence over these countries (Rajan and Subramanian, 2008). Thus, population in the aid recipient country has significant impact on economic growth of a country. Madsen et al. (2010) show that while technological progress has a positive effect, the effect of population growth on per capita growth is negative. Similarly, Becker et al. (1999) state that in poorer, mainly agricultural, economies with limited human capital and rudimentary technology, higher population significantly reduces economic growth. To capture this relationship, we include the size of population of the recipient countries in our empirical model.

29

We calculate the interest rate differential as the difference between the real interest rate of USA (using as the frontier economy) and the real interest rate of each recipient country.

56

Appendix 3: Table 3A1. Sample of Countries Africa

Asia Azerbaijan Bangladesh Bhutan e Cambodia e India Indonesia Iran Iraq Jordan Kyrgyz Republic Malaysia Nepal Pakistan Philippines Sri Lanka Syria Thailand Viet Nam Yemen

South America Argentina Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela

Algeria Malawi Angola Mali Benin Mauritania Botswana e Mauritius Burkina Faso Morocco Burundi Mozambique Cameroon Namibia Cape Verde Niger Central African Rep. Nigeria Chad Rwanda Cote D'Ivoire Senegal Egypt Seychelles Equatorial Guinea e Sierra Leone Ethiopia South Africa Gabon Sudan Gambia Swaziland Ghana Tanzania Guinea Togo Guinea-Bissau Tunisia Kenya Uganda Lesotho Zambia Liberia Zimbabwe Madagascar e indicates the country is excluded for robustness check due to their outlier growth rate. We also exclude China as it has aid data both in the donor and recipient side. We exclude countries, such as South Sudan, Eritrea, Somalia, Suriname, Guyana, Korea, Singapore, Hong Kong and others due to the absence of data for many variables especially the aid data.

57

Appendix 4: Alternative estimation method: Least Squares Dummy Variable Corrected (LSDVC) Variables Lag 1 of growth Education aid Health aid Agriculture aid

Aid without h ICRG

Education aid with ICRG

Health aid with ICRG

Agriculture aid with ICRG

(1)

(2)

(3)

(4)

(5)

0.030* (1.91) 0.158** (2.00) 0.144*** (4.03) -0.115 (-0.75)

0.035* (1.92) 0.015* (1.77)

0.044* (1.71)

0.007 (0.16)

-0.052 (-0.42) 0.014* (1.71) 0.003* (1.66) 0.001 (0.02) 0.390* (1.68) 0.138* (1.88) 0.241* (1.92) -0.075 (-0.29) -0.121 (-0.49) -0.102 (-0.34) 0.292 (0.31) -0.736** (-2.48) 0.3034** (2.55) 0.113 (0.30) 0.051*** (2.92) 0.046 (0.40) 0.266 (0.82) -0.167 (-0.40) -0.041 (-0.28)

0.003* (1.70) 0.004 (0.39) 0.088** (2.22)

(Education aid) x ICRG

0.032** (2.00)

(Health aid) x ICRG

0.044*** (2.75)

(Agriculture aid) x ICRG -0.030*** (-6.70)

(Education aid)2 x ICRG

-0.105* (-1.78)

(Health aid)2 x ICRG (Agriculture aid)2 x ICRG ICRG index Initial income Average years of schooling Trade openness Life expectancy Inflation Broad money (M2/GDP) Government expenditure (%GDP) Population

0.149 (0.22) -0.776*** (-4.22) 0.3668* (2.01) 0.095** (2.12) 3.151*** (5.81) -0.052*** (-4.41) 0.301 (0.56) -0.156 (-1.60) -0.080** (-2.47)

0.178* (1.77) -0.770*** (-4.13) 0.3729 (0.11) 0.097* (1.68) 3.179*** (6.13) 0.050*** (5.33) 0.307 (0.59) 0.174** (2.18) -0.074*** (-2.69)

0.185 (1.35) -0.749*** (-4.32) 0.3620 (0.14) 0.113 0.880** (2.97) (2.48) 0.042 (0.48) 0.300 (0.90) -0.194 (-0.69) -0.043 (-0.56)

1.039*** (4.91) -0.767*** (-13.85) 0.0794 (0.03) 0.003*** (4.41) 0.044*** (5.08) -0.001* (-1.71) 0.007*** (8.90) -0.007 (-0.58) 0.010 (0.22)

Full Sample

Notes: The dependent variable is growth of per capita income. We use consider standard one-step AB estimator and 50 repetitions for bootstrapping. Heteroscedasticity and autocorrelation robust t-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5% and 10% level respectively. Education is lagged by 4 years and health and agriculture aids are lagged by 1 year. Similarly, ICRG index, initial income, trade openness, life expectancy, inflation, broad money and gov. expenditure are lagged to address the endogeneity issue. A constant, time and country fixed effects are included in our regressions. In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. In addition, two control variables (i.e. ethnic fractionalisation and interest rate differential) are included. The results are not reported to save space; however, they are available upon request. We include 2,072 observations and 74 countries in all models.

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Highlights:

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Empirical relationship between sectoral foreign aid, institutional quality and economic growth is examined. Foreign aid is instrumented by similar voting positions in the UN General Assembly. The marginal effect of foreign aid is improved by the level of institutional quality. Aid in the education sector is more effective as the level of institutional quality improves. Education aid is more effective in South America, health aid in Asia and agricultural aid in Africa.