The nexus between institutions, foreign aid and foreign direct investment

In both of the explanations, foreign aid should be a significant factor of private capital inflows, which are generally accepted to vigorously promote growth, technology, and employment

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY THE HAGUE

VIETNAM THE NETHERLANDS

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE NEXUS BETWEEN INSTITUTIONS,

FOREIGN AID, AND FOREIGN DIRECT

INVESTMENT

BY

LE M TUE

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, JULY 2015

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY THE HAGUE

VIETNAM THE NETHERLANDS

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE NEXUS BETWEEN INSTITUTIONS,

FOREIGN AID, AND FOREIGN DIRECT

INVESTMENT

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

BY

LE M TUE

Academic Supervisor:

Dr DINH CONG KHAI

HO CHI MINH CITY, JULY 2015

ACKNOWLEDGMENT

I am grateful to Dinh Cong Khai, my academic supervisor, and Pham Khanh Nam, a member

of the VNP scientific committee, for helpful and detailed comments

ABSTRACT

This paper examines the mutual relationship between foreign aid and foreign direct investment (FDI), which might be ambiguous by reverse causality or simultaneity problems Using the dual-approach dynamics-balanced (DADB) model, we are able to point out that both bilateral and multilateral aid could lead to more FDI, and the impact of the latter could

be even larger than that of the former The institutional effect of multilateral aid is proposed

to explain this phenomenon Interestingly, the role of political stability could surpass those of democracy and control of corruption in having more aid disbursements

1 INTRODUCTION 1

1.1 Practical Motivation and Research Problems 1

1.2 Research Objectives 2

1.3 Structure 2

2 LITERATURE REVIEW 3

3 MODEL AND DATA 6

3.1 Dual-Approach Framework 6

3.2 Dual-Approach Dynamics-Balanced Model 8

4 RESULTS 12

4.1 Independent Marginal Effects between Institutions, Foreign Aid, and FDI 12

4.2 Reliability and Robustness Checks 14

5 CONCLUDING REMARKS 29

5.1 Empirical Findings 29

5.2 Policy Implication 29

5.3 Research Contribution, Implication, and Limitations 30

5.4 Future Research 30

REFERENCES 31

APPENDIX A 33

APPENDIX B 34

LIST OF FIGURES

Figure 1: The Nexus between Institutions, Foreign Aid, and FDI 8

LIST OF TABLES Table 1: Descriptive Statistics 10

Table 2: Independent Marginal Effects between Institutions, Foreign Aid, and FDI, 1996-2012 18

Table 3: The Impacts of Different Institutional Measures on FDI and Foreign Aid 22

Table 4: Independent Estimation of the Dynamic FDI Equation 25

Table 5: Independent Estimation of the Dynamic Aid Equations 27

LIST OF APPENDICES Table A 1: Variables and Data Sources 33

Table B 1: Regression Result of Table 2, column (1) 34

Table B 2: Regression Result of Table 2, column (2) 35

Table B 3: Regression Result of Table 2, column (3) 36

Table B 4: Coefficient of INSA index in Table 3, Panel A, column AA 37

Table B 5: Coefficient of INSA index in Table 3, Panel A, column BA 38

Table B 6: Coefficient of INSA index in Table 3, Panel A, column MA 39

Table B 7: Regression Result of Table 4, column (7) 40

Table B 8: Regression Result of Table 4, column (8) 40

Table B 9: Regression Result of Table 4, column (9) 41

Table B 10: Regression Result of Table 5, column (7) 41

Table B 11: Regression Result of Table 5, column (8) 42

Table B 12: Regression Result of Table 5, column (9) 43

Figure B 1: Scree Plot of Eigenvalues of Components for Five Variables of INSF index 44

Figure B 2: Scree Plot of Eigenvalues of Components for Three Variables of INSA index 44

1 INTRODUCTION

1.1 Practical Motivation and Research Problems

From the behavior aspect, when a country receives more aid from a donor, it will

acknowledge the generosity of that donor, easily cooperate with that sovereign partner, and create a concessionary legal environment for the enterprises of that donor (Kimura & Todo, 2010; Rodrik, 1995, p 25) From the effectiveness aspect, when a country receives more aid

from all donors, it is able to improve the social and economic infrastructures, thus the human

capital as well as the total factor productivity increase accordingly (Harms & Lutz, 2006) Hence, the recipient country would be able to attract more FDI from any other countries due

to its increasing competitiveness In both of the explanations, foreign aid should be a significant factor of private capital inflows, which are generally accepted to vigorously promote growth, technology, and employment in the host country Nevertheless, research studies have not found a robust relationship between foreign aid and FDI (Alesina & Dollar, 2000; Harms & Lutz, 2006)

Harms and Lutz (2006) suggest that one should consider the role of political and institutional characteristics when quantifying this relationship Indeed, institutional quality of the host country itself is an important direct magnet of private capital inflows Abundant empirical studies have pointed out the negative causality of a bad institution to the inflows of FDI Ironically, several countries which are perceived as having high corruption and low political, institutional profiles still have large inflows of FDI (Habib & Leon, 2002)

In the aspect of modeling, the influence of foreign aid on FDI is difficult to estimate due

to the problems of simultaneity and reverse causality By using lagged variables as instruments, 2SLS and GMM methods, to some extent, could alleviate such endogeneity However, the treatment is purely technical and does not reflect the nature of the problems Asiedu, Jin, and Nandwa (2009) propose a simultaneous equations model that could solve these problems In this approach, foreign aid and FDI are determined at the same time, and each of them is the determinant of the other While the dual approach is undoubtedly a superb idea, the applied model and the results of this research nonetheless contain some flaws and contradictions First, there is no institutional determinant in the aid equation Second, in the aid equation, the positive coefficient of FDI could be interpreted that while foreign aid reduces FDI, FDI could, however, increase foreign aid

1.2 Research Objectives

This paper aims to modify the simultaneous equations model of Asiedu et al (2009) and visualize the intricate relationship between foreign aid and FDI, which might comprise simultaneity and reverse causality With regard to purposes, while Asiedu et al (2009) focus

on the alleviating role of foreign aid on the adverse effect of expropriation risk on FDI, we concentrate on the effect of foreign aid on final FDI With regard to samples, we use both low-income and middle-income subsamples to support our analysis, whereas it is only the low-income countries in Asiedu et al (2009) In comparison with Harms and Lutz (2006), we apply a different model with different proxies of variables and a more recent period to assess the effect of foreign aid on FDI

After setting up the framework and specifying the according model, we use the data to illustrate the mutual relationship between foreign aid and FDI Using this result, we expect to figure out whether multilateral aid could actually lead to more FDI As for researchers concerned with the determinants of FDI and foreign aid, this paper provides more empirical evidence on the role of institutions In particular, we reexamine whether better institutions could attract more FDI as postulated in theory and found in many research studies By the way, we also appraise the importance of different institutional measures on FDI In the other side, do democracy (freedom), control of corruption, and political stability help a country receive more foreign aid?

1.3 Structure

In Chapter 2, this paper briefly reviews a trade theory which is widely used to explain the investment decision of foreign investors and some empirical results based on this theory We mainly concentrate on the papers that have institutions and foreign aid as the determinants of private capital inflows In Chapter 3, we explain the dual-approach framework and the regression model The variables and data sources are also described in this chapter The empirical findings and associated explanations are located in Chapter 4 Chapter 5 recapitulates the results for making policy and research

2 LITERATURE REVIEW

We first review the OLI theory on the investment decision of foreign investors; then we

come into the papers mentioning institutions and foreign aid as separate explanatory

variables of FDI; next, we have a look on the research which embeds the political and

institutional factors into the influence of foreign aid on FDI Lastly, we summarize the

institutional measures which might affect foreign aid

Dunning (1988, 1998, 2001) built up the OLI paradigm as a general framework to explain the activities of foreign investors The ownership advantages are classified as the O component and emphasize the comparative advantages of firms which can expand their business abroad Analyzing the O component provides us with information about the nature

of products and the ability of firms The location advantages define the L component and are related to the human and natural resources, the favorable conditions for production, business, research activities, and the market size in the host country The internalization advantages belong to the I component and focus on the aspect of how to lower transaction costs, as firms decide whether importing intermediate products from markets or internalizing foreign suppliers into their production chain The I component could be taken into analysis by firms

at the time of choosing the destination

Political and institutional factors of the host country are considered as the location advantages in the OLI framework The influence mechanism of these factors on foreign investors are mentioned in the papers such as Habib and Leon (2002) and Dunning and Lundan (2008) On the empirical side, Habib and Leon (2002) find out a negative relationship between corruption levels in the host countries and their inflows of FDI According to Habib and Leon, foreign investors might see corruption as violating social and professional ethics and increasing unnecessary costs.1 Moreover, paying bribes is strictly prohibited in the home countries of some foreign investors such as the United States (Hines, 1995)

Busse and Hefeker (2007) examine the impacts of government stability, law and order, absence of internal and external conflicts, lack of ethnic tensions, control of corruption,

democracy, and bureaucratic performance on FDI inflows to developing countries in the

period 1984-2003 The paper does show positive relationships between such measures and private capital inflows With the same period of research, Bénassy-Quéré, Coupet, and Mayer

1

Wei (2000) views corruption as a kind of tax on foreign investors

(2007) use the gravity model to test the influences of various institutional data on FDI stocks

In general, a country owning higher institutional quality would have a larger aggregate stock

of FDI, and higher institutional distance between the home country and the host country lowers the FDI stock of the former in the latter In other words, bad institutional measures would hinder FDI to a country For example, one of the results in Asiedu et al (2009) is that expropriation risk restrains the investment decision of foreign enterprises The samples of this research are low-income and Sub-Saharan African countries

On the other hand, the number of research studies that find out bad institutions as an incentive of FDI is quite limited Egger and Winner (2005) show the empirical evidence on the positive relationship between corruption and inward FDI The research uses a sample of both developed and developing countries in the period 1995-99 to strengthen the position of Leff (1964): bribing could reduce uncertainty among low-informational countries and safeguard foreign investors under major economic and political changes

In contrast to institutions, the role of foreign aid per se on FDI is ambiguous when analyzing the data As hypothesized by some authors, foreign aid does not only have the positive effect on FDI, but also the adverse effect The two simultaneous contradictory effects could be infrastructure effect and rent-seeking effect (Harms & Lutz, 2006), or complement and substitution (Selaya & Sunesen, 2012) As a result, many research studies could not find

a significant influence of aggregate aid on FDI (Bird & Rowlands, 1997; Harms & Lutz, 2006; Kimura & Todo, 2010) Selaya and Sunesen (2012) is a sparse research which finds out the dominance of the positive effect However, the role of bilateral aid in attracting FDI is more robust and could be found in Rodrik (1995) and Kimura and Todo (2010) For multilateral aid, there has been no empirical evidence that it could instantly enhance FDI (Rodrik, 1995) In the worst case, negative coefficients of aggregate aid, bilateral aid, and multilateral aid in the FDI regressions are exposed in the research of Asiedu et al (2009) for low-income and Sub-Saharan African countries

Aiming to address the vague impact of foreign aid on FDI, Harms and Lutz (2006) allow the marginal effect of foreign aid in the FDI equation to be dependent upon institutional variables, use different estimation techniques, time periods and country groups, and even disaggregate the types of foreign aid and private foreign investment Unfortunately, in line with previous papers, the authors still conclude that: on average, there is no effect of foreign aid on FDI Yet, the authors discover a remarkable result: only in the countries with high regulatory hindrance, i.e low institutional quality, foreign aid has a positive effect on FDI

As noted by the authors, this phenomenon emerges because the role of foreign aid as

reducing expropriation risk, thus increasing foreign investment, becomes conspicuous in those countries Similarly, the positive coefficient of the interaction term between expropriation risk and foreign aid in Asiedu et al (2009) means that expropriation risk is positively added to the effect of foreign aid on FDI Given the negative coefficient of foreign aid at the beginning, the increase of expropriation risk level, to some extent, could make foreign aid moving in the same direction with FDI, although expropriation is perceived as a bad practice

In regard to institutional determinants of foreign aid, Alesina and Dollar (2000), Dollar and Levin (2006) have consensus on the positive impact of democracy Corruption level, however, is not found to ultimately affect the decision of a majority of donors (Alesina & Weder, 2002)

3 MODEL AND DATA

3.1 Dual-Approach Framework

Figure 1 graphically illustrates the influence of institutions on foreign aid and FDI, and the interaction between these two foreign inflows The former induces simultaneity and the latter causes reverse causality Solid arrows are the foci of this paper In the bottom of the figure, institutions are the major factors of both FDI and foreign aid The OLI theory suggests that a better institution could have more FDI inflows Similarly, foreign aid might be disbursed more to a country with better governance Hence, α2 and β2 are expected to be positive

The amount of foreign aid received by a recipient could affect aggregate private capital inflows with an average magnitude α In case of multilateral aid, it aims to enhance the 1

social and economic infrastructures and is typically thought as creating the “infrastructure

effect.” As a matter of fact, this effect does not cover all the cases and is likely to occur in the long run In the short run, suppose that country A and country B nearly have the same conditions as well as the institutional measures When country A receives more multilateral aid than country B in a period, it does not necessarily mean that country A could improve its infrastructure instantly and have better infrastructure than country B, and thus could attract more foreign investors This argument especially makes sense insofar as multilateral aid, in general, is not targeted to support a specific industry Rather, it is the information and conditional policy functions of multilateral aid that could invisibly protect foreign investors

in destination countries.2 With this reasoning, this paper proposes the “institutional effect” of multilateral aid Two features of the institutional effect are that it could safeguard any foreign investor, but it could not change the institutional nature in the short run

In case of bilateral aid, besides productive sectors, a number of its projects are also for infrastructural development, and it should also have the same infrastructure effect as that of multilateral aid In addition, bilateral aid has its own special effect which is known to grease bilateral private capital inflows and is referred to as the “vanguard effect.” It is noticed that the vanguard effect of bilateral aid is quite similar to the institutional effect of multilateral aid, but only applied for bilateral investors The coefficient α1 is conjectured to be positive in both cases of foreign aid The impact level is expected to vary systematically among

2

See Rodrik (1995) for detailed discussion on the roles of multilateral aid

governments having different performances Therefore, the marginal effect of foreign aid could be expressed as a linear function of institutions, i.e α δ1+ institution The idea of putting political and institutional variables into the effect of foreign aid on FDI was first employed by Harms and Lutz (2006)

The mechanism through which FDI affects foreign aid could be direct or indirect The

direct mechanism is suitable to explain the inflows of bilateral aid occurring in the short run

It means that, when having more FDI in a host country, the government of home country has more motivation to disburse aid to that location Of course, it is not mandatory for the government of home country to do so because any country would welcome FDI However, as economic activities somewhat reflect the intimacy in diplomatic relationship between two countries, the use of FDI as a determinant of bilateral aid is still acceptable On the other hand, multilateral donors certainly are not concerned about FDI in recipient countries To sum up, in the short run, the coefficient β1 of FDI inflows could be positive in the bilateral aid equation and neutral in the multilateral aid equation

In the long run, it is the indirect mechanism that FDI could lead to the change in both bilateral and multilateral aid FDI might enhance GDP growth and GDP per capita, and, in turn, these outcomes decide the amount of foreign aid.3 In other words, if the inflows of FDI are actually transformed into the wealth of nation, that host country, eventually, would not rely on concessionary loans or grants of donors anymore With this argument, the β 1coefficients of FDI inflows in the bilateral and multilateral aid equations should turn negative

in the long run

Also from FDI to foreign aid, the marginal effect of the former on the latter could be adjusted by the institutional quality Different measures of institutions, such as democracy and corruption, are found to affect GDP growth and GDP per capita (Acemoglu, Naidu, Restrepo, & Robinson, 2014; Barro, 1996; Mauro, 1995) By adding institutional factors into the coefficient of FDI inflows, i.e β γ1+ institution, we could, in principle, evaluate how an economy absorbs FDI and makes itself less relied on the support of foreign donors

3

Some conditions that FDI causes economic growth are human capital (Borensztein, De Gregorio, & Lee, 1998), absolute and relative nature of FDI (Alfaro, 2003; Mello, 1999), and trade policy (Balasubramanyam, Salisu, & Sapsford, 1996)

Figure 1: The Nexus between Institutions, Foreign Aid, and FDI

3.2 Dual-Approach Dynamics-Balanced Model

From the dual-approach framework above, we have the general dual-approach model:

This model augments the simultaneous equations model of Asiedu et al (2009) by adding the political and institutional determinant, insa in the aid equations The detailed model for ,regression is

balanced, thus the model is referred to as dual-approach dynamics-balanced model.4

Most of the macro and demographic determinants are inherited from Asiedu et al (2009), but some adjustments are made In the FDI equation, the proxy of economic infrastructure is the total density of communication utilities (utilcom which is comprised of telephone lines, ),

4

Some papers using the first lag of FDI as an explanatory variable are Asiedu et al (2009), Busse and Hefeker (2007), Jensen (2003), and Gastanaga, Nugent, and Pashamova (1998).

mobile cellular, and internet subscribers However, the increase of current FDI inflows might provoke the usage of communication system Thus, we lag this variable to avoid the interaction with the dependent variable and its first lag in the right-hand side of the equation

The trade openness ( tradeopen as measured by the total exports and imports over GDP is )also lagged two periods because there is a direct contribution of FDI to this variable in the same year As foreign investors expand their business activities to other countries, either in the form of greenfield or merger and acquisition, they are more likely to export machines, equipment, materials, and experts to, and import final products from the host country Also the consumption of expatriates raises the in-border-exports component of the destination country

As depicted in Figure 1, FDI inflows could affect the GDP growth of a country within that year, so gdpgrow is also lagged two times to eliminate the reverse contribution of FDI

inflows into this explanatory variable Finally, the institutions that might affect FDI inflows (insf are regulatory quality, control of corruption, government efficiency, rule of law, and )political stability The impact of these institutional measures will be examined separately in the regression

In the aid equations, to avoid the component relationship of foreign aid and FDI in the current GDP per capita, we lag the variable lngdppc Apart from the projected negative .relationship between the income level of a country and the amount of aid that it receives, we also expect that the decreasing rate does not hold constant, in particular diminishes Hence,

the quadratic component of lngdppc is added The second macro variable which is debt over

GDP (debtgdp is lagged due to the inclusion of foreign aid to the current sovereign debt )Moreover, the debt in the past is more appropriate to be a determinant of aid disbursements in the future

The political and institutional variables of foreign aid (insa are supposed to be )democracy, control of corruption, and government efficiency As a demographic factor, population (lnpop is used because it could create the small-country effect on the inflows of )foreign aid With the same institutions and other things equal, a country with lower population is more homogenous and more likely to receive the support of foreign community

In comparison with the trade openness used in the FDI equation, a smaller population represents a larger social openness at the country level.5

3.3 Sample and Data

Table 1: Descriptive Statistics

Net FDI inflows (% GDP) 3.693 7.374 3,711 3.628 9.731 3.733 5.498 Aggregate aid (% GDP) 9.129 13.28 3,827 16.14 14.77 4.787 10.06 Bilateral aid (% GDP) 6.119 10.42 3,827 10.27 10.96 3.546 9.178 Multilateral aid (% GDP) 3.020 4.596 3,815 5.871 5.825 1.247 2.227 Regulatory quality -0.494 0.714 1,922 -0.866 0.614 -0.264 0.674 Control of corruption -0.480 0.642 1,928 -0.737 0.557 -0.321 0.639 Government efficiency -0.499 0.656 1,922 -0.865 0.582 -0.272 0.595 Rule of law -0.493 0.733 1,957 -0.796 0.717 -0.308 0.680 Political stability -0.382 0.948 1,909 -0.638 1.017 -0.222 0.864

ln(GDP per capita) 7.185 1.093 3,888 6.257 0.872 7.737 0.798 GDP growth (%) 3.842 7.497 3,921 3.993 8.582 3.751 6.764 ln(Population) 15.38 2.162 4,302 15.34 1.862 15.41 2.333 Debt (% GDP) 63.12 58.33 2,223 76.17 81.03 56.21 39.93 Comm utilities 49.47 59.58 2,666 23.97 37.60 64.15 64.74 Trade openness (% GDP) 78.52 43.03 3,746 70.98 45.91 83.06 40.54

the sources of these subjective variables Table 1 summarizes the descriptive statistics More details of the variables and the data sources are shown inTable A1 (Appendix A)

4 RESULTS

4.1 Independent Marginal Effects between Institutions, Foreign Aid, and FDI

Table 2 shows the regression results based on the DADB model Panel A contains the base regression, which follows exactly the specification of the DADB model, for each type of foreign aid—aggregate (AA), bilateral (BA), and multilateral (MA) The three variables of interest are FDI inflows, foreign aid, and institutions In the FDI equation, five available measures of institutions—which are rule of law, regulatory quality, control of corruption, government efficiency, and political stability—are examined one by one Each variable does have a positive impact on FDI inflows The coefficients as well as their significance levels within the full sample are reported in Panel A of Table 3 Except for the government performance, all other institutional and political measures are valuable to attract more FDI The significance levels are at least 5-percent Surpassing other measures in the category, the quality of regulation in the private sector has the largest impact on the investment decisions

of multinational enterprises (MNEs) A one-point higher in this measure of sound policy results in 0.66 percentage-of-GDP higher of FDI inflows after controlling for the persistence

of these inflows and other conditions

Since all of the institutional measures have a positive impact on the dependent variable,

we then use the principal component analysis (PCA) to construct a composite institutional index which represents the role of government policies in promoting FDI This index is denoted by INSF and will be used in later FDI-determined regressions Apart from reducing the workload, another reason behind this composition is that these measures often move together A sound government normally has high scores on rule of law, regulatory quality, control of corruption, and government efficiency, and there would be little space for political instability, residential violence, and external conflicts

In column (2) of Table 2, bilateral aid, as expected, has a positive coefficient in the FDI equation This result is similar to Rodrik (1995, p 44) It could be interpreted that when the inflows of bilateral aid is higher by 1 percentage of GDP, the inflows of FDI is approximately higher by 0.18 percentage of GDP, ceteris paribus Interestingly, in column (3), the effect of multilateral aid is twice larger than that of bilateral aid with a coefficient of 0.39 The explanation for the more profound impact of multilateral aid is that while bilateral aid only improves the inflows of FDI from the countries of bilateral donors, multilateral aid, on the other hand, attracts foreign investors from all countries In short, the institutional effect

outperforms the “extended” vanguard effect To check the robustness of the results by using smaller samples, the impacts of institutions and foreign aid on FDI within the low-income and middle-income subsamples are reported in the first half of Panel B and C of Table 3 As observed, the effect of foreign aid on FDI within the low-income countries is more than double within the middle-income countries, but there is no signal of the effect of institutions

on FDI within the former subsample whatsoever There are two possible reasons for these differences First, there might be a virtual institutional threshold under the perception of foreign investors When institutional measures of host countries are below a “threshold value,” then any difference between them would not be considered As a matter of fact, the descriptive statistics in Table 1 show that, on average, the quality of institutions of low-income countries is lower than that of middle-income counterparts in any aspect Instead, the extended vanguard effect of bilateral aid and the institutional effect of multilateral aid have a more important role in the low-income countries, and it forms the second reason In the need

of low-interest development finance, less developed countries have to comply with regulations and requirements of international organizations Incidentally, these obligations somewhat affect policies toward foreign private sectors, and in reverse, foreign investors could have more confidence in these countries This phenomenon is especially obvious within the low-income countries, of which the average of foreign aid is much higher than that

of middle-income countries

Other control variables in the FDI equation are statistically significant and have the expected signs When GDP growth or trade openness increases by 1 percentage of GDP, the net inflows of FDI after two years could be higher by 0.06 or 0.01 percentage of GDP, respectively When the total number of telephone lines, mobile subscribers, and internet users

is higher by 10 units per 100 people, private capital inflows in the next two years could be larger by 0.1 percentage of GDP

In the aid equations, the insignificant coefficients of FDI inflows in the second part of columns (1), (2), and (3) of Table 2 illustrate the non-impact of FDI on foreign aid It means that a country with more FDI does not necessarily have more or less of bilateral or multilateral aid In other words, although FDI could motivate more aid from some specific donors, the totals of bilateral aid inflows between recipient countries are not ultimately different due to their total received FDI This result is in line with the statement of Alesina and Dollar (2000): there is no relationship between FDI and foreign aid

The democracy index, which includes political rights and civil liberties, has an important role in the distribution of foreign aid, both bilateral and multilateral The negative coefficients mean that when a country is appraised to have more freedom, it would have more foreign aid than others Similarly, we also point out that control of corruption and political stability could influence the aid disbursements from both bilateral and multilateral donors at the aggregate level The coefficients and the significance levels of these explanatory variables within the full sample are attached in Panel A of Table 3 The empirical result that corruption reduces the amount of foreign aid coming to a country contrasts with the finding of Alesina and Weder (2002) Interestingly, the coefficients of political stability are much larger than those

of control of corruption in the same context Like INSF index for FDI, we also construct a composite institutional index, which is INSA, to record which of governance indicators could help a country in receiving more foreign aid The impacts of FDI and institutions on foreign aid within the low-income and middle-income subsamples are consolidated in the second half

of Panel B and C of Table 3

Other control variables in the aid equations have statistically significant impact on the distribution of foreign aid The squared component of the income level implies not only a negative marginal effect but also a diminishing rate of this effect Additionally, the income level is much more sensitive and significant in the multilateral aid equation than in the bilateral aid version This figure proves the target of reducing poverty of multilateral organizations Finally, a country with more debt or lower population is more open and likely

to look for the help of foreign counterparts and international organizations

4.2 Reliability and Robustness Checks

In this part, we check the reliability and the robustness of the specified model The reliability check is done by not lagging the control variables of the two equations Besides the smaller-sample approach mentioned above, the robustness check is undertaken by three more experiments First, a control variable is removed from the specification Second experiment is the insertion of some irrelevant variables Third, different methods of regression—which are POLS, difference GMM (Arellano & Bond, 1991), and system GMM (Blundell & Bond,

1998)—are applied separately to each of the equations

Panel C [columns (7)-(9)] of Table 2 reports the results when we do not lag the control variables There are several reasons that the lag version is more reliable than the non-lag version of the specification First, the effect of GDP growth in the non-lag specification is attenuated to 0.05, in comparison with 0.06 in the lag specification This attenuation is caused

by the feedback of the dependent variable The procedure of reverse causality is explained as following GDP growth, which represents the production and consumption capabilities of an economy, is one of the most important criteria when MNEs choose a country to invest The higher GDP growth of a country, the more opportunities are that the investment could be absorbed and turned into profits In turn, when foreign investors decide to expand their business to a higher-growth country, they also contribute to the production of goods and services within that country As a result, GDP growth is spirally affected by FDI inflows

To mathematically illustrate the attenuated coefficient of a positively feedback-affected explanatory variable x,let denote a self-change of this assumed exogenous variable as 0,

Second, in contrast to GDP growth, the coefficient of trade openness in the non-adjusted specification is much larger and more significant than that in the adjusted specification This

phenomenon occurs when x or y is the component of each other Suppose that the effect of x

on y is β, thus the effect relationship is y=βx In addition, due to the componential nature

between y and x, the component relationship is y=bx. The multivariate regression of y on x

would generate a combination as y=(β+b x) Therefore, the coefficient of the endogenous

variable x increases in the value if the component relationship is positive ( b>0) Moreover, the significance levels always increase as a result of direct components, either positive or negative, in the regression In our case, we have β =0.01 and β+ =b 0.03 Similarly, a small component relationship could also be found between the amount of debt and the inflows of

aid in the aid equations of the non-adjusted specification Foreign aid is a component of sovereign debt within the same year

Third, a model or a specification is considered more reliable when its regression coefficients are stable despite changes in the proxies of other variables On this criterion, the two-lag specification is a little better than the non-lag counterpart The coefficient of lag of FDI variable in the former is stable at the value 0.56, in spite of the foreign aid proxies, whereas that in the latter fluctuates between 0.54 and 0.55 Fourth, we do not empirically prefer the non-lag specification because it is less robust than the two-lag version As seen in

Panel D (Table 2), the coefficient of INSF index becomes insignificant after utilcom is

removed from the model

Next, we turn our attention to the robustness checks First, we remove some control

variables from the model, which are utilcom in the FDI equation and lnpop in the aid equations The results are reported in Panel B, G, and H of Table 2 In the case of utilcom, all

of the control variables which might share its role in the FDI equation—GDP growth, trade openness, and the lag of FDI inflows—increase significantly in their impacts In contrast, the coefficients of our variables of interest—INSF index and foreign aid—are downward biased The coefficient of INSF index becomes unstable Nevertheless, their impacts on FDI inflows remain significant, and the impact of multilateral aid still doubles that of bilateral aid There

is not much change in the three aid equations with this omission of utilcom In the case of lnpop, the absence of this demographic variable just induces the role of control of corruption

and political stability in having more foreign aid

Second, we add some irrelevant or weakly relevant variables into the base specification of the DADB model In particular, the FDI equation is added with the money stock M2

(m2gdp), and the aid equations are added with the inflation rate (infc) Young- and

old-dependency ratios of the working-age population are also included in these placebo tests Panel E and F show the results While the domestic financial development, the inflation, and the younger dependents are utterly irrelevant, the older dependents reveal some concealed or indirect relevance The old-dependency ratio, to some extent, reflects the human capital, the living conditions, and the ability to pay debt in the future On one hand, the human capital and the living conditions are the attractive factors of foreign investment On the other hand, the low ability to work and pay debt of the old population might concern bilateral donors In regression, the coefficients of old-dependency ratio in the FDI and bilateral aid equations are 0.09 and -0.06, respectively, with weak significance levels

In column (14), even though the coefficient of freedom in the bilateral aid equation reduces in the size, from -0.15 to -0.08, compared to the base regression in column (2), its significance level also decreases Thus, the role of freedom could be plausible in bilateral aid disbursements Such situations also occur with INSF index and communication utilities in the FDI equation [Panel F, column (16)].7 For other cases in Panel E and F, the coefficients and their significance levels are almost unchanged

7

The coefficients (t-statistics) of communication utilities are 009 (2.67) in column (1) and 005 (1.38)

in column (16)

Table 2: Independent Marginal Effects between Institutions, Foreign Aid, and FDI,

(6.19) (5.13) (6.73) (5.47) (4.48) (6.29) Net FDI inflows (-1) 0.56*** 0.56*** 0.56*** 0.68*** 0.69*** 0.68***

(24.19) (24.40) (24.47) (28.57) (28.68) (28.52)

(1.94) (1.95) (1.97) (4.08) (4.09) (4.19) Comm utilities (-2) 0.01*** 0.01** 0.01***

(2.67) (2.29) (2.60) Trade openness (-2) 0.01*** 0.01*** 0.01*** 0.02*** 0.02*** 0.02***

(3.02) (2.76) (3.29) (4.49) (4.31) (4.78)

(-0.39) (0.33) (-0.64) (-2.59) (-2.16) (-3.04) Foreign aid

(-1.13) (-1.13) (-0.84) (-1.46) (-0.88) (-0.98) Freedom -0.26*** -0.13** -0.13*** -0.29*** -0.17*** -0.15***

(-3.29) (-2.25) (-3.89) (-3.26) (-2.60) (-3.68) Foreign aid (-1) 0.56*** 0.60*** 0.50*** 0.63*** 0.61*** 0.53***

(30.99) (33.41) (23.97) (31.83) (29.99) (23.90) ln(GDP per capita) (-2) -8.73*** -3.43*** -5.71*** -7.18*** -3.07*** -5.41***

(-7.72) (-6.61) (-6.75) (-5.15) (-4.87) (-4.72) Constant 50.90*** 24.08*** 28.49*** 40.43*** 20.97*** 26.25***

(9.18) (6.02) (11.39) (6.56) (4.64) (9.00)

Note: 3SLS estimation, t-statistics in parentheses, *** p<0.01, ** p<0.05, * p<0.1, AA is aggregate

aid, BA is bilateral aid, MA is multilateral aid (percentage of GDP)