Aid-Growth Association and Role of Economic Policies: New Evidence from South and Southeast Asian Countries

Abstract

The present article aims to study the implications of foreign aid on GDP per capita in the seven middle-income countries of South and Southeast Asia from 1990 to 2016. The influence of foreign aid on GDP per capita is studied in a policy-driven environment, for which a policy index using government consumption expenditure, inflation and trade openness is constructed. The outcomes of the study confirm that aid–policy interaction has a significant and positive impact on economic growth in the region. The results are calculated using the two-stage least-squares model, as foreign aid is included as an endogenous variable in the study. This approach enables us to assess the indirect effect of monetary, fiscal and trade policies on GDP per capita, as the constructed policy index is used as an instrument for foreign aid. The results of the study ascertain that besides current aid–policy interaction, the preceding years’ aid–policy interaction also has a positive and significant impact on GDP per capita in the region. The inclusion of a policy index in the analysis enables us to evaluate the effectiveness of economic policies while determining the level of GDP per capita in South and Southeast Asian countries. Therefore, the study proposes that while assessing the influence of aid-financed programmes on GDP growth of a country, the economic policies of recipient countries need to be considered. The bottom line is that foreign aid, economic policies and economic growth of aid-recipient countries are inextricably linked.

Keywords

Foreign aid economic policy economic growth 2SLS South and Southeast Asian countries

Introduction

The United Nations Millennium Declaration has considered foreign aid as a complementary source of finance in underdeveloped and developing countries and named it as Official Development Assistance (ODA) (United Nations General Assembly, 2000). The extant literature also suggests that foreign aid may have a positive impact on GDP growth of a country (Arndt, Jones, & Tarp, 2015; Asteriou, 2009; Hatemi-J & Irandoust, 2005; Ho & Iyke, 2018; Sharma, Kautish, & Kumar, 2018). The transferring of money in the form of foreign aid may generate new opportunities in the beneficiary countries, which may further lead to increased productivity and growth, and mitigate poverty through employment generation (Aboubacar, Xu, & Ousseini, 2015). However, due to infrastructure and policy distortions in developing countries, the role of foreign aid may become less effective or inconclusive (Burnside & Dollar, 2000). In a subtle policy environment, due to excessive aid-dependency, the rate of domestic saving in the public sector may decrease in the long run (World Bank, 1995). As a result, the government of the aid-dependent country may be unable to raise the tax base, which may further compel the government to invite fresh foreign aid. The new foreign aid may aggravate the existing problem for the recipient country and favour the donor country (Lensink & White, 1999). Successively, donors may become more affluent, and receivers may remain poor (Griffin & Enos, 1970; Shah, 2014). In addition, the impact and magnitude of foreign aid on GDP growth may vary across countries and time dimensions. Therefore, particularly in developing countries, where scarcity of resources has remained a persistent problem, productive use of foreign aid becomes more vital.

In the recent past, policymakers and academicians have been immersed in finding out linkages between GDP growth and foreign assistance in the Asian countries (Roe, 2013). Notably, the cumulative share of South Asia and East Asia & Pacific in net official assistance receipt accounted for 2 per cent of gross capital formation in 2015. In fact, from 2002 to 2015, this region has received $130.8 billion as foreign assistance, which is the largest share in the total aid-for-trade across the world.¹ However, during the same period, instead of spending the money on social development, 63 per cent of the total aid has been spent on infrastructural development. In terms of total aid receipt, from 2002 to 2015, the top five countries were Afghanistan, India, Vietnam, Pakistan and Indonesia, which received financial aid worth $4074.2 billion, $3876.0 billion, $3035.2 billion, $2837.9 billion and $2471.5 billion, respectively. On the other hand, in terms of aid-for-trade receipts, India ($1662.4 billion) tops the list followed by Vietnam ($1530.7 billion), Afghanistan ($1005.2 billion), Pakistan ($638.8 billion) and Indonesia ($627.7 billion) (Asian Development Bank, 2017). Since India is the second largest populated and one of the fastest growing countries in South Asia donor countries may prefer to provide more assistance to countries like India to pursue their economic interests. Besides aid-for-trade and infrastructural development, the donor countries regularly offer assistance for trade policies and administrative management, which may help in germinating the progressive economic environment in the recipient countries (Asian Development Bank, 2017).

Regardless of registering a steady GDP growth of 7.6 per cent from 1985 to 2015 and receiving regular massive development assistance, the performance of most of the developing countries of South and Southeast Asia on both balance of payments and fiscal fronts on the one hand and societal front on the other has remained lacklustre (Fensom, 2017; Park, 2017). Besides regional inequalities, approximately 400 million people of Asia and the Pacific region still live in severe deprivation (Kurian, 2017). For sustainable economic growth, governments need to remove the infrastructural and administrative bottlenecks, which can be ensured by devising target-oriented long-term plans (Sharma et al., 2018). Burnside and Dollar (2000) suggest that for economic development, the effective utilization of external resources is desirable for a developing country. In this regard, foreign aid can play a vital role, which may help in mobilizing the idle resources in the long run. However, large gestation period, sunk cost, lack of infrastructure, unproductive use of aid and distorted economic policies may subside the real effect of foreign aid in a region (Asian Development Bank, 2017). Therefore, governments need to deploy foreign aid in physical and human capital development. Investment in these areas, besides spillover effect, may lead to economic growth in succeeding years as well (Tarp, 2015).

Thus, the present work attempts to analyse the impact of foreign aid on GDP per capita, which is a proxy for economic growth, in the seven middle-income countries of Asia. Since these countries have witnessed a significant GDP growth along with a relatively low human development index (Selim, 2016) a study of aid–policy interaction may help us examine whether macroeconomic policies are conducive for aid-led growth. Correspondingly, the interaction of the time-lagged foreign aid with the time-lagged policy may be helpful in understanding whether the preceding year’s aid–policy interaction influences current GDP per capita in the region. The existing literature suggests that current GDP has an association with current foreign aid, but it may also be affected by the past year’s foreign aid if the foreign aid is used for infrastructural development, which generally has a long gestation period (Tang & Bundhoo, 2017). On the other hand, if foreign aid, especially the financial aid, is used to meet the immediate consumption requirements, it may have no or little effect on the succeeding year’s GDP growth (Albiman, 2016). Based on income criteria provided by the World Bank and availability of data pertaining to the selected determinants, the seven middle-income countries of South and Southeast Asia (India, Pakistan, Sri Lanka, Bangladesh, Malaysia, Indonesia and Thailand) have been examined for the period 1990–2016. These developing countries of Asia are the net receivers of foreign assistance. The use of the two-stage least-squares (2SLS) estimation enables us to assess the existence of endogeneity in the time series data. The article has been divided into five sections. The second section provides insight into the extant literature and the need for research on the theme. After that, the third section highlights the research methodology and research framework. The fourth section investigates and explains the results of the study. Finally, the fifth section ends with conclusion and policy implications.

Literature Survey

The literature related to aid-led growth can be segregated into theoretical and empirical investigations. The outcomes of Harrod–Domar models (1939, 1946) revealed that the effective use of investment is vital to achieving economic growth. Therefore, the appropriate use of financial aid is desirable, especially in the underdeveloped countries where domestic savings are unable to meet the investment requirements (Bulír & Hamann, 2003).The two-gap model of Chenery and Strout (1966) highlighted that, besides investment, earnings from the exchange can also be a vital determinant of GDP growth. However, in developing countries, earnings from exports may remain insufficient for development programmes, as the products of these countries are unable to compete in the international market (United Nations, 2008). Moreover, the export bills of developing countries mainly comprising primary goods, which are both demand and supply sensitive, are likely to fetch insignificant returns (Puri & Misra, 2014).

Therefore, the external financial assistance may be helpful in realizing the investment requirements of developing nations.

The empirical studies related to aid-growth are either country-specific or region-specific. The effectiveness of aid-led growth programmes may depend on the level of infrastructure and aid absorption capacity of recipient countries, which varies significantly across the regions (Durbarry, Gemmell, & Greenaway, 1998; Zoundi, 2015). Therefore, the findings of various studies are contradictory. For instance, Islam (1992) found that foreign assistance has a substantial impact on national production in Bangladesh. Further, the study perceives that as compared to foreign assistance, the endogenous resources of a country are better drivers of economic growth. Nevertheless, Bring (1994) challenged the results of Islam’s study, as the study ignored the possibility of multicollinearity in the system. Likewise, Durbarry et al. (1998) perceived that in a stable macroeconomic policy environment, the additional dose of foreign assistance may help in improving the level of national output in the long run. However, in an indifferent policy environment, the outcomes of the aid-led growth may provide inconclusive results, as the developing countries have a tendency to use foreign aid to meet their unproductive expenditure requirements. Further, Burnside and Dollar (2000), using a sample of 56 countries, observed that without macroeconomic policy consideration, foreign aid has an insignificant impact on GDP growth. By contrast, in a sound policy driven environment, the impact of foreign aid on national production is found to be direct and significant in the selected countries. The findings of the study suggest that if the government of the recipient country executes its economic policies strategically, the impact of foreign aid on GDP growth may become more decisive and consistent. Similarly, in a panel data study in 60 aidrecipient countries, Veiderpass and Andersson (2007) have intended to observe whether all the selected countries have used foreign assistance effectively to pursue their long-term goals. In this regard, the study found that in comparison to other countries, China and Nigeria have used external assistance more judicially, whereas Pakistan’s efficiency score is found lowest among all the countries. In a time series study, Rahnama, Fawaz, and Gittings (2017) witnessed that foreign aid has a widespread positive impact on high-income countries, whereas in low-income countries, its impact on national output is found negative in the long run. Further, the study perceives that the high institutional quality index (i.e., the low level of corruption) in high-income countries may ensure effective utilization of foreign aid, whereas in low-income countries, widespread corruption may dilute the magnitude and effectiveness of foreign aid. The findings of the study conducted by Rotarou and Ueta (2009) established that the inflow of foreign assistance in Tanzania has substantially helped in raising the level of national production in the long run. However, in terms of poverty reduction, its impact is found insignificant in the region. The study perceives that the positive association between foreign assistance and national output may be due to the presence of a sound socio-political and macroeconomic environment, as a stable socio-political environment may help in channelizing the idle resources rapidly (Puri & Misra, 2014). Likewise, Tang and Bundhoo (2017) carried out a study in 10 sub-Saharan African countries and found that foreign aid proved to be a significant source for meeting the investment requirements of these countries. Moreover, if the economic policies of the aid-recipient countries are growth-oriented, the efficacy of foreign assistance may increase substantially. Thereby, the study recommends that while allocating foreign aid, donor countries should consider the objectives and policies of recipient countries. Besides GDP growth, foreign aid may indirectly influence the policies and political institutions of recipient countries (Dietrich, 2015). Jones and Tarp (2016) attempted to examine whether foreign aid can influence the policies and political institutions of recipient countries. The results of their study using a dynamic structure in the panel data framework suggest that foreign aid has a mild positive impact on the index of political institutions. In the post-Cold War period, the positive association between foreign aid and political institutions has appeared to be stronger than that in the pre-Cold War period.

In a country-specific study, Ho and Iyke (2018) found that in both periods, that is, short run and long run, the level of national output in Ghana has been positively influenced by foreign assistance. On the other hand, the results of the panel data study conducted by Moreira (2005) concluded that compared to the long run, the short-run impact of foreign aid on GDP growth remains less effective in the region. Moreover, the study proposed that while assessing the impact of aid on GDP growth, the ignorance of time lags and endogeneity of factors may provide inconsistent results. Similarly, the results of the study conducted by Sharma et al. (2018) carried out in India found that in the long run foreign aid has a positive effect on national output, whereas in the short run the influence of foreign aid on national income is found to be negative and significant.

The findings of various studies suggest that foreign assistance may have a negative or inconclusive effect on GDP growth of a country (Albiman, 2016; Chirwa & Odhiambo, 2017). For example, Burke and Ahmadi-Esfahani (2006) using a simultaneous equation model in Thailand, Indonesia and Philippines observed that from 1970 to 2000, foreign aid has no significant impact on economic growth. Nevertheless, the study advocates that in order to have reliable outcomes, the aid–growth relationship needs to be examined over an extended period. Further, Mallik (2008), whose study comprises highly aid-dependent countries of Central African Republic, found that in most of the selected countries, the effect of foreign aid on growth endeavours remains undesirable and unproductive. The study concludes that distorted economic policies and the poor state of human capital may be the possible reasons for the negative association between aid and economic growth in the region. Further, the study carried out by Ekanayake and Chatrna (2010) in 87 aid-recipient countries of Asia, Africa, Latin America and Caribbean region, shows that except for Africa, in all other developing countries, production activities have not been influenced by foreign assistance substantially. The findings of the study conducted by Moyo and Mafuso (2017) for the period of 1980–2000 show that excessive aid-dependency has influenced the economic environment negatively in Zimbabwe, as most of the aid-led programmes have been supported by tied aid, which favours the economic interests of donor countries. Consequently, it has further increased the aid-dependency of the Zimbabwean government. Similarly, using macroeconomic ratios in 88 countries from 1971 to 2012, Templea and Sijpe (2017) observed that instead of investing in constructive projects, the recipient countries had used foreign aid for primary consumption. Apparently, such kinds of expenditures might have contributed to increasing inflation rate in the region.

Based on the empirical studies, it appears that if foreign assistance is used prudently for development programmes, it may have a positive spillover impact on the aid-recipient countries, as it may fulfil their financial and technological requirements. Furthermore, the literature supports that in order to get the best results from aid-driven programmes, aid agencies and recipient countries have to support each other and work according to the planned goals (Edwards, 2014; Templea & Sijpe, 2017). On the other hand, not only the unproductive macroeconomic policies but also the unstable socio-political environment may have an adverse impact on the efficacy of foreign aid in the aid-recipient countries. The influence of institutional factors (i.e., sociopolitical) is beyond the scope of the present study. However, the interaction of foreign aid with economic policy and the interaction of time-lagged foreign aid with the time-lagged economic policy are included to assess the economic growth in developing countries of South and Southeast Asia.

Need and Objectives of the Study

Most of the developing countries of South and Southeast Asia are directly or indirectly dependent on foreign assistance for their economic growth endeavours. Indeed, for sustainable economic growth, the government’s objectives and policies should be well planned, and foreign aid should be utilized constructively, as the irrational utilization of foreign aid and defective economic policies may lead to macroeconomic problems.

In the present research work, the interaction of foreign assistance and policy and the interaction of time-lagged foreign aid with time-lagged economic policy enable us to appraise the impact of foreign aid on GDP per capita. The aid–policy interaction may give answers to two crucial questions: (a) do current and time-lagged foreign aid influence GDP per capita in the region? (b) do current and time-lagged economic policies influence GDP per capita in the region? Additionally, the impact of time-lagged foreign aid on GDP per capita enables us to examine whether foreign assistance is utilized for economic development purpose, which generally bears a long-run impact, or to satisfy the immediate consumption requirements.

Research Methodology

A careful examination of the aid-led growth literature suggests that past studies have ignored the role of macroeconomic policies prevalent in aid-recipient countries while determining the efficacy of foreign assistance, especially in the developing countries of Asia. Therefore, the present work attempts to observe the impact of foreign aid and economic policy on GDP per capita in the seven developing countries of South and Southeast Asia (India, Pakistan, Sri Lanka, Bangladesh, Malaysia, Indonesia and Thailand) from 1990 to 2016. Using the growth approach of Solow (1956), besides foreign aid, allows us to measure the impact of capital and labour on GDP per capita.

The selection of countries is based on the income criteria provided by the World Bank, which enables us to preserve the homogeneity in the sample. Besides income, the data availability and net aid receipts are other conditions for the selection of countries. The annual data series related to GDP per capita, foreign assistance, labour, gross capital formation, broad money, population, inflation, trade openness (calculated using the summation of export and import to gross domestic production), government consumption expenditure and import of arms and ammunition are retrieved from the World Development Indicators (2017). The whole procedure can be divided into two parts.

At the first stage, using the ordinary least-squares (OLS), the impact of selected macroeconomic determinants, that is, LGDP, INV, LABOUR, MONEY, INF, GCON and TOP on GDP per capita is analysed. The coefficients of INF, GCON and TOP derived by the first-stage OLS estimation are used to calculate the policy index. The mechanism for deriving the policy index is mentioned in the policy index section.

At the second stage, the constructed policy index is used to employ the 2SLS method. The 2SLS estimation intends to reckon the indirect impact of foreign aid on GDP per capita in the presence of a macroeconomic policy environment, as foreign aid is taken as an endogenous variable in the estimation. Unlike the OLS, the use of 2SLS estimation enables us to seize the impact of the endogeneity problem in the framework. In the final growth equation, INF, GCON and TOP are dropped, as the impact of these variables is included in the policy index. However, in the final growth equation, besides LGDP, INV, LABOUR and MONEY, other selected variables, that is, AID, AID², (ARMS_i,t–1), POLICY, (AID_it × POLICY_it), LPOPU, AID_i,(t–n),POLICY_i,(t–n) and (AID_i,(t–n) × POLICY_i(,t–n)) have also been included. Notably, only the current period’s aid is used as an endogenous variable in the growth equation. In the 2SLS estimation, foreign aid is taken as an endogenous variable, which is instrumented by arms and ammunition (ARMS) and policy index. Further, in both models, that is, in the OLS and 2SLS, the heteroscedasticity problem is controlled by using White’s (1980) robust standard errors. The details about the incorporated variables are given in Table 1. In order to validate the results of the 2SLS estimation, various diagnostic tests have been performed, which suggests that the inclusion of foreign aid as an endogenous variable is appropriate.

The possible reasons to select the variables mentioned in Table 1 are elaborated in this section. GDP per capita (US $) represents economic growth of recipient countries, and it is used as an endogenous variable. Foreign aid (AID_it) represents the injection of extra financial resources to stimulate the economic growth activities in the region, which is a proportion of the net official development assistance to gross domestic product. The squared value of AID (AID²) assesses whether foreign aid has the diminishing returns during the study period, as in the developing countries, the returns to scale have a tendency to change over time (Barro, 1991). Gross capital formation (INV) and employed labour force (LABOUR) are used as factor inputs in the growth equation. The logarithm value of the beginning year’s GDP (LGDP) per capita for each country is used to assess the conditional convergence effects. Stating differently, the study tries to estimate the impact of beginning year’s GDP per capita on current year’s GDP per capita. If the beginning level of GDP per capita of a country is relatively low, it tends to grow faster over time than a country with a higher level of GDP per capita in the initial year (Barro, 1991). Therefore, the initial value of GDP per capita is time-invariant and expected to have a negative coefficient (Barro, 1991; Hansen & Tarp, 2001). Further, the logarithmic value of population size (LPOPU_it) is included in the system, as the literature suggests that the size of the population of recipient countries may have an impact on economic growth (Ali, Alam, Islam, & Hossain, 2015; Furuoka, 2010). The logarithm value of inflation (consumer prices, annual per cent), which is calculated by adding one each year, represents the monetary policy of recipient countries. Government consumption expenditure (percentage of final consumption expenditure of government to GDP) represents the fiscal policy of recipient countries, which may have an impact on GDP per capita in the selected countries. Collier and Dollar (2002) and Tang and Bundhoo (2017) have also used government consumption expenditure as a proxy for fiscal policy. Similarly, trade openness is used to represent the trade policy of recipient countries, which may have an influence on economic growth in the developing countries.

Table 1.

Details and Definitions of Incorporated Variables

Variable	Definition	Contribution
Endogenous variables
GDP	GDP per capita at constant 2010 US$ (a proxy for economic growth)	(Burnside & Dollar, 2000)
Foreign aid (AID)	The proportion of the Net Official Development Assistance to GDP	(Collier & Dollar, 2002)
Exogenous variables
AID²	Squared value of Aid is used to assess whether the level of foreign aid has diminishing returns	(Burnside & Dollar, 2000; Collier & Dollar, 2002; Tang & Bundhoo, 2017)
AID × POLICY	Foreign aid multiplied by policy	(Burnside & Dollar, 2000)
AID_t–1 × POLICY_t–1	One period lagged aid multiplied by one period lagged policy	(Tang & Bundhoo, 2017)
AID_t–2 × POLICY_t–2	Two-periods lagged aid multiplied by two-periods lagged policy	(Tang & Bundhoo, 2017)
Investment (INV)	Gross capital formation, which measures the growth of capital stock (percentage of GDP and used as a proxy for investment)	(Solow, 1956; Tang & Bundhoo, 2017)
Labour (LABOUR)	Labour force employed, total (percentage of the total population of the country)	(Solow, 1956; Tang & Bundhoo, 2017)
Log GDP (LGDP)	Logarithm value of GDP of the beginning year (1990) for each country (used in growth theory to assess the conditional convergence effects)	(Burnside & Dollar, 2000; Hansen & Tarp, 2001)
Log population (LPOPU)	Logarithm value of the total population of recipient countries (to examine the strategies of donor countries whether they are population influenced or not)	(Burnside & Dollar, 2000)
Inflation (INF)	Inflation (consumer prices, annual per cent) the logarithm value of inflation is calculated after adding one in each year and used as a proxy for monetary policy	(Burnside & Dollar, 2000; Collier & Dollar, 2002; Tang & Bundhoo, 2017)
Government consumption (GCON)	Percentage of final consumption expenditure of government to GDP at constant 2010 US$ (used as a proxy for fiscal policy)	(Havi & Enu, 2014; Lawal, Somoye, Babajide, & Nwanji, 2018)
Trade openness (TOP)	The summation of export and import is divided by GDP (Used as a proxy for international trade policy)	(Collier & Dollar, 2002; Frankel & Romer, 1999)
Money (MONEY_t–1)	Broad money (Money and quasi-money) is a percentage of GDP with one period lag (used as a proxy for financial development)	(King & Levine, 1993; Burnside & Dollar, 2000)
Instruments
POLICY	Calculated using the first stage OLS coefficients of inflation, government consumption expenditure, trade openness and a constant term	(Burnside & Dollar, 2000; Tang & Bundhoo, 2017)
Arms and ammunition (ARM_t–1)	The ratio of arms and ammunition import to total import with one period lagged value (used as a proxy for good or bad policy for recipient countries)	(Burnside & Dollar, 2000)

Source: The authors.

Note: All the series are related to aid-recipient countries.

Furthermore, the study has included the import values of arms and ammunition (ratio of arms and ammunition import to total import with one period lagged value) of recipient nations, which may be able to describe the strategic interest of recipient nations (Burnside & Dollar, 2000). The excessive use of foreign aid on arms and ammunition may dilute the effectiveness of foreign aid, as such kinds of expenditures may fulfil their military import requirements, which unveils the unproductive use of foreign aid.

Likewise, the lagged broad money (percentage of GDP) is included in the growth equation, which examines the influence of financial depth on GDP per capita in the recipient countries (Tang & Bundhoo, 2017). In general, the existing literature perceives that the objectives and policies of donor and recipient countries are also essential factors in determining the usefulness of foreign aid (Jones & Tarp, 2016; McKinlay & Little, 1978; Trumbull & Wall, 1994). Therefore, the study has included the policy index (POLICY_it) and the interaction of foreign aid and policy (AID_it × POLICY_it). The inclusion of the lagged values of foreign aid (AID_i(t–n)), policy (POLICY_i(t–n)) and aid–policy interaction (AID_i(t–n) × POLICY_i(t–n)) addresses the possibility of the long gestation period, as in the developing countries, it can be a crucial factor for economic growth. Nevertheless, the impact of the import of arms and ammunition and policy index is to be observed indirectly, as these are used as the instruments for foreign aid in the 2SLS estimation.

Framework

Using the Solow’s (1956) neoclassical approach to growth, investment and labour are included in the growth equation. In addition, the inclusion of economic policy in the model may help in understanding the macroeconomic environment in the selected countries. Following the framework of Burnside and Dollar (2000), Collier and Dollar (2002) and Tang and Bundhoo (2017), equation (1) enables us to examine the influence of different macroeconomics factors on GDP per capita:

_it(1)= α₀+ α₁_0it+ α₂_it+ α₃_it+ α₄_i,t–1+ α₅_it+ α₆²_it+ α₇_i,t–1+ α₈_it+ α₉_it× POLICY_it+ α₁₀_it+ α₁₁_i,(t–n)+ α₁₂_i,(t–n)+ α₁₃_i,(t–n)× POLICY_i(t–n)+ μ_it

In equation (1), i and t represent countries and time index, respectively. The definitions and details of the comprised variables are given in Table 1.

Policy Index

The literature supports that inflation rate and government consumption expenditure are the reliable determinants of monetary and fiscal policies, respectively (Havi & Enu, 2014; Lawal, Somoye, Babajide, & Nwanji, 2018; Tang & Bundhoo, 2017). Furthermore, the use of trade openness enables us to understand the vitality of foreign trade policy of a country, which is calculated using the ratio of export plus import to GDP. Therefore, GDP per capita is regressed against inflation, government consumption expenditure and trade openness. Notably, foreign aid is not used as an independent variable in equation (2). The logarithm values of inflation are calculated by adding one (1) in each year’s inflation (Collier & Dollar, 2002). By regressing inflation, government consumption expenditure and trade openness on GDP, equation (2) enables us to assess the effect of these variables, which are used as proxies for monetary (α₅), fiscal (α₆) and trade policy (α₇), respectively (Burnside & Dollar, 2000; Havi & Enu, 2014; Lawal et al., 2018; Tang & Bundhoo, 2017).

The coefficients of inflation (α₅), government consumption expenditure (α₆), trade openness (α₇) and constant term (α₀) are derived using ordinary least-squares, from equation (2):

_it(2)₀+ α₁_0it₂_it₃_it₄_i,t–1+ α₅_it₆_it₇_it_it

After that, equation (3) enables us to draw a policy index where POLICY is constructed by multiplying the coefficients of inflation, government consumption expenditure and trade openness with inflation, government expenditure and trade openness series, respectively. Notably, the coefficients used in equation (3) are derived from equation (2). Subsequently, all the right-hand-side values placed in equation (3) are added, which provides a policy series.

=(3)α₀+α₅_it+α₆×_it+α₇_it

Interpretation and Discussion of Results

The results of Table 2 (OLS 1), which are based on equation (2), show that the initial level of GDP per capita, investment and inflation has a negative and significant impact on GDP per capita. On the other hand, GDP per capita is significantly and positively influenced by labour, government consumption expenditure and trade openness. The p (0.000) and R-squared (0.653) values given in the OLS (1) suggest that at 5 per cent level, the model is statistically significant.

Table 2.

OLS Growth Regression Model (Dependent Variable: GDP Per Capita)

Variables	OLS (1)	OLS (2)
LGDP	–3.514*** (0.716)	–2.303*** (0.667)
INV	–0.215*** (0.044)	–1.800*** (0.427)
LABOUR	1.563*** (0.502)	1.351*** (0.487)
MONEY_t–1	–0.643 (0.606)	–0.555 (0.535)
INF	–1.211*** (0.199)	–0.934*** (0.186)
GCON	0.380*** (0.120)	0.230* (0.110)
TOP	0.012*** (0.002)	0.009*** (0.002)
AID	–	–0.394*** (0.076)
Prob > F	0.000	0.00
R-squared	0.653	0.695

Source: The authors.

Notes:

1. Column (2) represents the results of the OLS model (1) excluding foreign aid.

2. The coefficients of inflation (INF), government consumption expenditure (GCON), trade openness (TOP), and constant term are used to construct the policy index.

3. The results of the OLS (2) are shown in column (3), where foreign aid’s impact on GDP per capita is also considered.

4. Robust standard errors are given in parentheses.

5. The OLS (1) provides the results of equation (2).

6. 10 per cent, 5 per cent and 1 per cent level of statistical significance are denoted by *, **, and ***, respectively.

Subsequently, the outcomes of the OLS model (2), besides determinants mentioned in the OLS model (1), capture the impact of foreign aid on GDP per capita. The results of the OLS model (2) show that despite the inclusion of foreign aid, the R-squared value has not increased significantly. Furthermore, the impact of foreign aid on GDP per capita is found negative and significant in the region.

Based on the results of the OLS (1) in Table 2, a policy index is created. While constructing the policy index, the coefficients of inflation, government consumption expenditure and trade openness are used. Also, a constant value (derived from the results of model 1),which is assuming the mean effect of all other determinants on GDP per capita is introduced in equation (4).

=(4)×++

The comprised constant value, inflation, government consumption expenditure and trade openness represent the presence of various policies in the recipient nations. Once the desired policy index is framed, in the panel data framework, the impact of foreign aid and economic policies on GDP per capita in the seven developing countries of South and Southeast Asia can be assessed. The results of Table 3 using the OLS and 2SLS estimations provide the impact of various determinants on GDP per capita.

Table 3.

Results of the OLS, 2SLS, FE (Fixed Effect) and RE (Random Effect) Using Foreign Aid and Policy Index (GDP per Capita-Dependent Variable for Seven Countries)

Model	OLS	2SLS	OLS	2SLS	OLS	2SLS	FE	RE
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
LGDP	–1.741*** (0.477)	–1.982*** (0.414)	–0.629 (0.528)	–1.256*** (0.442)	–1.832*** (0.566)	–0.300*** (0.046)	–1.460*** (0.286)	–1.524*** (0.418)
INV	0.262*** (0.090)	0.307*** (0.087)	0.153* (0.086)	0.222*** (0.0811)	0.331* (0.178)	0.415*** (0.152)	0.479*** (0.136)	0.465*** (1.560)
LABOUR	0.327 (0.350)	0.120*** (0.031)	0.211 (0.438)	0.988** (0.444)	0.342 (0.340)	0.129*** (0.0326)	0.232*** (0.031)	0.234*** (0.0284)
LPOPU	–1.553*** (0.164)	–1.300*** (0.187)	–1.289*** (0.148)	–1.154*** (0.166)	–1.566*** (0.172)	–1.484*** (0.188)	2.785*** (0.315)	2.602*** (0.498)
MONEY_t–1	–0.283 (0.347)	–0.335 (0.314)	–0.247 (0.413	–0.295 (0.390)	–0.290 (0.430)	–0.357 (0.406)	–0.321 (0.450)	–0328 (0.520)
AID	–0.227*** (0.020)	–0.136*** (0.020)	–0.202*** (0.019)	–0.132*** (0.024)	–0.222*** (0.027)	–0.805*** (0.273)	–0.354*** (0.134)	–0.363** (0.152)
AID²	0.180*** (0.024)	0.836*** (0.184)	0.165*** (0.0268)	0.879*** (0.251)	0.176*** (0.028)	0.415 (0.312)	0.219* (0.120)	0.228* (0.137)
AID × POLICY	0.163*** (0.030)	0.938*** (0.244)	–	–	0.158*** (0.034)	0.565** (0.245)	0.106*** (0.019)	0.103*** (0.022)
AID_t–1 × POLICY_t–1	–	–	0.807*** (0.229)	0.441** (0.179)	–	–	–	–
AID_t–2 × POLICY_t–2	–	–	–	–	0.112 (0.282)	0.752** (0.367)	0.413*** (0.127)	0.397** (0.168)
AID_t–2	–	–	–	–	–0.376 (1.095)	–0.497*** (0.161)	–0.930 (2.815)	–0.801 (0.508)
POLICY_t–2	–	–	–	–	–0.951 (2.590)	–0.303 (0.195)	–0.263** (0.122)	–0.251 (0.180)
Prob > F (OLS) /Prob > Chi² (2SLS)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Initial R-squared
R-squared values	0.798	0.765	0.774	0.753	0.799	0.762	0.965	0.718
Wu-Hausman’s Test
p-value	–	0.000***	–	0.011**	–	0.000***	–	0.000***
Partial adjusted R-squared (Shea’s)
Aid	–	0.160	–	0.130	–	0.190	–	–
Minimum eigenvalue (Wald Test)
To check the weak instruments	–	16.801* (11.590)	–	11.240^# (8.750)	–	20.737* (19.930)	–	16.73* (13.43)
Sergans’s p-value
For over identifying restrictions	–	0.860	–	0.099	–	0.359	–	0.11
Hausman Test’s p-value	–	–	–	–	–	–	–	0.878

Source: The authors.

Notes: 1. Details of the variables according to Table 1.

2. Aid is used as an endogenous variable in the 2SLS models.

3. Arms and ammunition (ARMS) and policy are used as instruments in the 2SLS estimation.

4. Model (1), (3) and (5) provide the results of the OLS estimation, whereas model (2), (4), and (6) provide the results of 2SLS.

5. 15 per cent, 10 per cent, 5 per cent and 1 per cent level of statistical significance are denoted by ^#, *, **, and ***, respectively.

6. In parentheses, the values of White’s heteroscedasticity consistent standard errors are mentioned.

7. In the Wu-Hausman’s test the null hypothesis is: Aid is an exogenous variable.

8. In the Wald’s test the null hypothesis is: the instruments are weak. The critical values given in parentheses reject the null hypothesis.

9. In the Sergan’s test the null hypothesis is: no overidentifying restrictions.

10. FE and RE represent the fixed effects and random effect, respectively.

11. Robust standard errors may not be consistent with the assumptions of Hausman test variance calculation. Therefore, in calculation of random effect, standard errors are used.

Findings and Discussion

Aid–Growth Relation

The outcomes of the OLS approach are not considerably different from the 2SLS estimation. In all the OLS models (i.e., 1 to 5) GDP per capita has been influenced negatively and significantly by foreign aid. However, the impact of time-lagged foreign aid (AID_t–2) and time-lagged foreign policy (POLICY_t–2) on GDP per capita in the OLS model (5) is found insignificant. Similarly, in the 2SLS models (2), (4) and (6), foreign aid has a negative and significant effect on GDP per capita. The relationship between foreign aid and national output reveals that apart from the present year, the previous years’ foreign assistance also has a negative impact on national output if it is examined without macroeconomic policy interaction. Further, the results of the OLS and 2SLS models reveal that government capital formation (i.e., investment) has helped in increasing the level of national output. Generally, the expenditure on capital formation is target oriented, which is likely to improve national output in the long run. Evidently, in the past, the region has witnessed a tremendous population growth. However, the contribution of the growing population in national output has been found to be negative and significant in the developing countries of Asia. Nevertheless, the results of all the models, that is, OLS and 2SLS reveal that labour’s contribution in national output remains positive and significant, which indicates that both investment and labour have substantially contributed in increasing the level of national output in the region. Furthermore, the negative and insignificant association between broad money and GDP per capita reveals that the governments of the selected countries need to improve the contribution of the financial sector to economic growth, which can be ensured by delivering improved financial services and imbibing competitiveness in the financial system.

Unlike the OLS model (5), the results of the 2SLS model (6) reveal that the time-lagged foreign aid (AID_t–2) has a negative and significant impact on GDP per capita in the region. The empirical studies recommend that while assessing the impact of foreign aid on GDP growth, the impact of time-lagged foreign aid on GDP growth should not be ignored (Moreira, 2005; Tang & Bundhoo, 2017). The significant impact of aid in the present year and its insignificant impact in the succeeding years may be due to the unproductive use of financial resources. On the other hand, if foreign aid is used for the development of infrastructure, education and research activities, it may have a long-lasting impact, as the money invested through productive programmes may have a spillover impact in succeeding years (Puri & Misra, 2017).

Foreign aid and time-lagged foreign aid have negatively influenced GDP per capita in South and Southeast Asian countries in the absence of interaction with policy. The findings of the present study confirm the results of the past study where the preceding year’s foreign aid has a negative impact on the present GDP (Tang & Bundhoo, 2017). On the other hand, the outcomes of various cross-section and country-specific studies perceive that in developing countries, foreign aid may have a positive influence on GDP growth (Abouraia, 2014; Asteriou, 2009; Fayissa & El-Kaissy, 1999; Feeny, 2005; Hatemi-J & Irandoust, 2005). However, these studies have ignored the role of economic policy, which may be a crucial factor for the effective utilization of foreign aid in the region.

Similarly, the outcomes of the fixed and random effect models (i.e., 7 and 8) have also confirmed a negative and significant association between foreign aid and GDP per capita. However, the impact of time-lagged foreign aid on GDP per capita is found to be insignificant in both models. Except for the 2SLS model (6), in all the models, the squared term of foreign aid (AID²) has a positive and significant association with GDP per capita. On the other hand, in all the models, the association of linear foreign aid with GDP per capita is found negative and significant. The positive and significant association of the squared foreign aid (AID²) with GDP per capita indicates that the impact of foreign aid on GDP per capita varies with the level of foreign aid and exhibits a non-linear trend. The non-linear association between foreign aid and GDP per capita confirms the findings of Tiwari (2011), which are computed for Asian countries.

The results of the Wald test confirm that all the models are statistically significant. Wu-Hausman’s test outcomes approve the endogeneity of foreign assistance in the system. Therefore, foreign aid in the present year (AID_it) comprises an endogenous variable in the study. In other words, the impact of foreign aid on GDP per capita is not direct. Indeed, it carries the impact of economic policy and arms and ammunition while influencing GDP per capita. Therefore, in all the 2SLS models (2), (4) and (6) barring foreign aid, all other determinants are taken as exogenous variables. The high initial R-squared values of all the 2SLS models (2), (4) and (6) approve that foreign aid influences GDP per capita through instruments. Further, the outcomes of minimum eigenvalues confirm that the instruments used are not weak and statistically significant. Finally, based on the p-values of Sargan’s (1958) chi-square test, the null hypothesis of over-identifying restrictions cannot be rejected in all the 2SLS models. The acceptance of the null hypothesis approves that in the present study, none of the 2SLS models is over-identified. As a result, instead of using OLS results, the present study proposes the results of the 2SLS models. Thus, the findings and inferences are prepared according to the 2SLS models. Using White’s (1980) robust standard errors, the problem of heteroscedasticity is adjusted in all the models. The values in parentheses, given in Table 3 are robust standard errors.

Aid–Growth Association and Role of Economic Policies

The results of the 2SLS model (2) reveal that in the selected developing countries of South and Southeast Asia, the interaction between aid and policy in the current year (AID × POLICY) has a positive and significant impact on GDP per capita, which reveals that due to the constructive economic policies, the effect of foreign aid turns into positive and significant. Similarly, in the 2SLS model (4), the impact of the lagged aid–policy interaction on GDP per capita is found positive and significant in the region, which indicates that foreign aid has a long-lasting positive impact if combined with constructive economic policy. On the other hand, the results of both models reveal that in the absence of interaction with policy, the impact of foreign aid on GDP per capita becomes negative and significant. Therefore, it can be perceived that due to a change in the macroeconomic environment, the effectiveness of foreign aid may change substantially. Nevertheless, in comparison to the coefficient of current aid–policy (AID × POLICY), the coefficient of lagged aid–policy interaction (AID_t–1 × POLICY_t–1) has been decreased substantially. Similarly, the coefficient of aid–policy interaction with two-period lags (AID_t–2 × POLICY_t–2) is also found positive and significant. Notably, the coefficients of the aid–policy interaction with one-period lag (AID_t–1 × POLICY_t–1) and two-period lags (AID_t–2 × POLICY_t–2) have remained high and statistically significant, which indicates that the level of GDP per capita in recipient countries may improve substantially provided that a productive economic environment supports the aid-led growth programmes.

Evidently, the results of all the models (i.e., 2 to 8) suggest that in the selected developing countries of South and Southeast Asia, economic policies are complementary to foreign aid, as the association between aid–policy helps in increasing the level of national output not only in the present year but also in the succeeding years. The initial R-squared values in all the 2SLS models have remained consistent and nearly 76 per cent. Furthermore, the Wu-Hausman and Wald tests confirm the endogeneity of foreign aid and reliability of the instruments, respectively. Therefore, the study perceives that while assessing the influence of foreign aid on GDP per capita, the lagged aid–policy needs to be considered. The positive and significant coefficient of the aid–policy (AID × POLICY) interaction supports the results of the previous studies (Burnside & Dollar, 2000; Denkabe, 2004).Similarly, the aid–policy interaction with the one-period and two-period time intervals (AID_t–1 × POLICY_t–1 and AID_t–2 × POLICY_t–2) supports the findings of the study of Tang and Bundhoo (2017).

Considering the outcomes of the 2SLS models, the study recommends that foreign aid and economic policies are vital determinants of economic growth in South and Southeast Asian countries. The outcomes of the present study corroborate the outcomes of previous studies, which have recommended the inclusion of macroeconomic policies for the effective utilization of foreign aid in a country or region.

Conclusion

The present study using the sample of seven middle-income countries of South and Southeast Asia tries to examine the implications of foreign assistance and economic policies on GDP per capita for the period 1990–2016. In the subject field, by using the 2SLS estimation, foreign assistance is contained as an endogenous variable, which affects GDP per capita through instruments. The findings of the study corroborate that a good policy environment enhances the effectiveness of foreign aid. The level of GDP per capita is significantly and positively influenced by aid–policy interaction. The study has an advantage that besides current year’s aid and policy, the impact of their lagged values (AID_i(t–n) × POLICY_i(t–n)) on GDP per capita has also been examined. This approach enables us to assess whether past years’ foreign aid influences current GDP per capita. The findings of the study reveal that both current aid–policy and lagged aid–policy have a positive impact on GDP per capita. However, without interaction with policy, the impact of foreign aid on GDP per capita is found negative and significant in the region. Therefore, the study submits that a conducive-policy environment may help in utilizing the foreign aid more efficiently.

Generally, the impact of economic development projects can be witnessed over a period. The positive impact of preceding years’ aid–policy interaction on GDP per capita highlights that South and Southeast Asian countries have used their foreign aid for development purposes. Otherwise, current GDP per capita might not have been influenced by the lagged aid–policy interactions.

The study opens the scope for future research, as beside economic policies, institutional factors (i.e., sound organization, law and society and accountability) may be vital in determining the efficacy of foreign assistance. The interaction of these elements with foreign aid is beyond the scope of the present study. Nevertheless, the proposed study contributes to the existing state of literature in two ways: theoretically, it highlights that the economic policies of the recipient country are vital for aid-led growth programmes. Practically, the study establishes that foreign aid may have a long-lasting impact on GDP per capita if it is utilized for constructive purposes by the aid-recipient country.

Policy Implications

The effective use of foreign aid may be ensured if it is used for infrastructural and socio-economic development of a country, as financial aid invested in these programmes has a spillover effect over several years. The study recommends that while initiating the aid-led development programmes, the macroeconomic policies of the aid-recipient countries should not be ignored. Otherwise, foreign aid may not be able to provide the desired outcomes, and it may lead to inflationary pressure by encouraging the unproductive consumption expenditures in an aid-recipient country or region. The utilization of financial aid through target-oriented programmes such as infrastructural development, education and research, and facilitation by a conducive policy-driven environment may help in resource mobilization, which may further contribute to economic growth in developing countries.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Note

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