INTRODUCTION
Practical Motivation and Research Problems
Increased foreign aid from donor countries leads recipient nations to recognize the donor's generosity, foster cooperation, and create favorable legal conditions for the donor's enterprises (Kimura & Todo, 2010; Rodrik, 1995) This aid enhances social and economic infrastructures, boosting human capital and total factor productivity, which in turn attracts more foreign direct investment (FDI) and enhances competitiveness (Harms & Lutz, 2006) Consequently, foreign aid plays a crucial role in facilitating private capital inflows that drive growth, technology advancement, and job creation in host countries However, research has shown an inconsistent relationship between foreign aid and FDI (Alesina & Dollar, 2000; Harms & Lutz, 2006).
Harms and Lutz (2006) emphasize the importance of political and institutional characteristics in measuring the relationship between these factors and private capital inflows The quality of institutions in a host country significantly influences foreign direct investment (FDI), with numerous studies indicating that poor institutional frameworks negatively impact FDI inflows Interestingly, some nations known for high levels of corruption and weak political institutions continue to attract substantial FDI (Habib & Leon, 2002).
Estimating the impact of foreign aid on foreign direct investment (FDI) is challenging due to simultaneity and reverse causality issues While methods like 2SLS and GMM can partially address these endogeneity concerns through lagged variables, they do not fully capture the underlying problems Asiedu, Jin, and Nandwa (2009) introduce a simultaneous equations model that addresses these complexities by treating foreign aid and FDI as mutually influential Despite the innovative nature of this dual approach, the model reveals significant flaws, such as the absence of institutional determinants in the aid equation and the contradictory interpretation of the positive coefficient of FDI, suggesting that while foreign aid may reduce FDI, FDI could simultaneously increase foreign aid.
Research Objectives
This study seeks to enhance the simultaneous equations model established by Asiedu et al (2009) to better illustrate the complex interplay between foreign aid and foreign direct investment (FDI), addressing issues of simultaneity and reverse causality Unlike Asiedu et al (2009), which emphasizes the mitigating impact of foreign aid on the negative consequences of expropriation risk for FDI, our focus is on the direct influence of foreign aid on FDI levels We analyze data from both low-income and middle-income countries, expanding the scope beyond the low-income focus of the original study Additionally, in contrast to Harms and Lutz (2006), we utilize a distinct model with alternative variable proxies and a more recent timeframe to evaluate the effects of foreign aid on FDI.
This paper explores the intricate relationship between foreign aid and foreign direct investment (FDI) by analyzing data within a specified framework We aim to determine if multilateral aid can effectively increase FDI, contributing empirical evidence on the influence of institutional quality Our research reexamines the hypothesis that stronger institutions attract more FDI, as supported by existing studies, while also evaluating the significance of various institutional measures Additionally, we investigate whether factors such as democracy, corruption control, and political stability enhance a country's ability to secure foreign aid.
Structure
Chapter 2 reviews a prominent trade theory that elucidates foreign investors' investment decisions, focusing on studies that highlight institutions and foreign aid as key determinants of private capital inflows Chapter 3 outlines the dual-approach framework and regression model, detailing the variables and data sources utilized Empirical findings and their interpretations are presented in Chapter 4, while Chapter 5 summarizes the results to inform policy-making and future research.
LITERATURE REVIEW
This article begins with an examination of the OLI theory regarding foreign investors' investment decisions It then explores literature that identifies institutions and foreign aid as distinct factors influencing foreign direct investment (FDI) Following this, the discussion shifts to research that integrates political and institutional elements into the relationship between foreign aid and FDI Finally, the article concludes by summarizing the institutional metrics that may impact foreign aid.
The OLI paradigm, developed by Dunning (1988, 1998, 2001), serves as a comprehensive framework for understanding foreign investment activities It consists of three components: Ownership (O), which highlights the competitive advantages of firms that enable them to expand internationally; Location (L), which pertains to the availability of human and natural resources, favorable production conditions, research opportunities, and market size in the host country; and Internalization (I), which focuses on minimizing transaction costs and determining whether to import intermediate goods or integrate foreign suppliers into the production process Analyzing these components helps firms make informed decisions about international expansion.
Political and institutional factors in the host country serve as significant location advantages within the OLI framework, as highlighted in studies by Habib and Leon (2002) and Dunning and Lundan (2008) Empirical evidence from Habib and Leon indicates a negative correlation between corruption levels in host countries and foreign direct investment (FDI) inflows They suggest that foreign investors often view corruption as a breach of social and professional ethics, leading to increased costs Additionally, in some home countries, such as the United States, paying bribes is strictly illegal, further discouraging investment in corrupt environments (Hines, 1995).
Busse and Hefeker (2007) analyze the effects of government stability, law and order, absence of conflicts, ethnic harmony, corruption control, democracy, and bureaucratic efficiency on foreign direct investment (FDI) inflows to developing countries from 1984 to 2003 Their findings indicate a positive correlation between these factors and private capital inflows Similarly, Bénassy-Quéré, Coupet, and Mayer conducted research during the same timeframe, reinforcing the importance of stable governance and institutional quality in attracting FDI.
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
Countries with higher institutional quality tend to attract more foreign direct investment (FDI), while significant institutional distance between home and host countries negatively impacts FDI stock Poor institutional measures can deter foreign investment, as evidenced by Asiedu et al (2009), which found that expropriation risk discourages foreign enterprises from investing, particularly in low-income and Sub-Saharan African nations.
Research on the impact of poor institutions as an incentive for foreign direct investment (FDI) is relatively scarce However, Egger and Winner (2005) provide empirical evidence demonstrating a positive correlation between corruption and inward FDI Their study, which encompasses both developed and developing nations from 1995 to 1999, supports Leff's (1964) assertion that bribery can alleviate uncertainty in low-information countries, thereby protecting foreign investors during significant economic and political shifts.
The impact of foreign aid on Foreign Direct Investment (FDI) is complex and often contradictory, as it can yield both positive and negative effects Some studies suggest that foreign aid may enhance FDI through infrastructure development, while others indicate adverse outcomes due to rent-seeking behaviors Consequently, many researchers, including Bird & Rowlands (1997) and Harms & Lutz (2006), have found no significant correlation between aggregate aid and FDI Although Selaya and Sunesen (2012) identified a predominance of positive effects, the influence of bilateral aid on attracting FDI is more consistently supported in studies by Rodrik (1995) and Kimura & Todo (2010) In contrast, there is a lack of empirical evidence supporting the immediate benefits of multilateral aid on FDI, with some research, such as that by Asiedu et al (2009), revealing negative correlations in low-income and Sub-Saharan African countries.
Harms and Lutz (2006) explore the complex relationship between foreign aid and foreign direct investment (FDI) by considering the influence of institutional variables and employing various estimation methods, time frames, and country classifications Despite their comprehensive analysis, they find that, on average, foreign aid does not significantly impact FDI However, a noteworthy finding emerges: in countries characterized by high regulatory barriers and low institutional quality, foreign aid positively influences FDI.
Foreign aid plays a crucial role in mitigating expropriation risk, which in turn enhances foreign investment in certain countries As highlighted by Asiedu et al (2009), the interaction between expropriation risk and foreign aid demonstrates a positive relationship, indicating that higher expropriation risk can amplify the impact of foreign aid on foreign direct investment (FDI) Despite the initial negative perception of foreign aid, an increase in expropriation risk may lead to a scenario where foreign aid aligns more closely with FDI, even though expropriation is generally viewed unfavorably.
Research by Alesina and Dollar (2000) and Dollar and Levin (2006) highlights the positive influence of democracy on foreign aid allocation Conversely, the level of corruption does not significantly impact the decisions of most donors, as noted by Alesina and Weder (2002).
MODEL AND DATA
Dual-Approach Framework
Figure 1 illustrates the impact of institutions on foreign aid and foreign direct investment (FDI), highlighting their interrelated dynamics The model indicates that foreign aid can create simultaneous effects, while FDI may exhibit reverse causality The solid arrows represent the primary focus of this study, emphasizing that institutions are crucial determinants for both FDI and foreign aid According to the OLI theory, countries with stronger institutions are likely to attract higher FDI inflows, and similarly, better governance may lead to increased foreign aid allocation Therefore, it is anticipated that the relationships represented by α2 and β2 will be positive.
The impact of foreign aid on private capital inflows can vary significantly, particularly regarding multilateral aid, which is intended to improve social and economic infrastructure, often referred to as the "infrastructure effect." However, this effect may not be immediate or universally applicable For instance, if country A receives more multilateral aid than country B under similar conditions, it does not guarantee that country A will enhance its infrastructure quickly or attract more foreign investors This is largely because multilateral aid is generally not directed towards specific industries; instead, its informational and conditional policies can provide a protective framework for foreign investors in recipient countries Consequently, this paper introduces the concept of the "institutional effect" of multilateral aid, highlighting that while it can offer protection to foreign investors, it does not alter the institutional framework in the short term.
Bilateral aid not only supports productive sectors but also contributes significantly to infrastructural development, mirroring the infrastructure impact of multilateral aid Additionally, bilateral aid fosters private capital inflows, a phenomenon known as the "vanguard effect," which is analogous to the institutional effect observed in multilateral aid but specifically targets bilateral investors It is anticipated that the coefficient α1 will be positive in both foreign aid scenarios, although the impact level is expected to differ systematically among them.
Multilateral aid plays a significant role in influencing government performance, as discussed by Rodrik (1995) The marginal impact of foreign aid can be modeled as a linear function of institutional quality, represented as α δ 1 + institution The incorporation of political and institutional factors in assessing the effect of foreign aid on foreign direct investment (FDI) was first introduced by Harms and Lutz (2006).
Foreign Direct Investment (FDI) influences foreign aid through both direct and indirect mechanisms The direct mechanism effectively accounts for the short-term inflows of bilateral aid.
Increased foreign direct investment (FDI) in a host country often motivates the home country's government to provide aid, although such support is not mandatory, as most nations welcome FDI The correlation between economic activities and diplomatic relationships suggests that FDI can be a significant factor in determining bilateral aid Conversely, multilateral donors typically do not consider FDI when allocating aid Thus, in the short term, FDI inflows may positively influence bilateral aid while remaining neutral in the context of multilateral assistance.
Foreign Direct Investment (FDI) can significantly influence both bilateral and multilateral aid over time through an indirect mechanism By boosting GDP growth and GDP per capita, FDI can ultimately reduce a host country's dependence on concessional loans and donor grants Consequently, if FDI inflows are effectively converted into national wealth, the long-term impact would be reflected in negative β1 coefficients for FDI inflows in the equations governing bilateral and multilateral aid.
The relationship between Foreign Direct Investment (FDI) and foreign aid can be influenced by the quality of institutions within a country Research indicates that various institutional factors, including levels of democracy and corruption, significantly impact GDP growth and GDP per capita By incorporating these institutional elements into the analysis of FDI inflows, we can better assess how effectively an economy utilizes FDI and reduces its dependence on foreign aid.
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.
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
, it it it i t i t i t i t it infdigdp aidgdp insf infdigdp gdpgrow utilcom tradeopen u α α ϕ λ λ λ
, it it it i t i t i t i t it it aidgdp infdigdp insa aidgdp lngdppc lngdppc debtgdp lnpop u β β ϕ λ λ λ λ
The model incorporates net inflows of foreign direct investment (FDI) and official development assistance (ODA) as percentages of GDP, represented by infdigdp and aidgdp, respectively A key distinction from previous research by Asiedu et al (2009) is the inclusion of the first lag of aid inflows, aidgdp i t, − 1, which acknowledges the gradual disbursement of foreign aid aligned with project completion This, along with the first lag of FDI inflows, infdigdp i t, − 1, establishes a balanced system of two equations, leading to the designation of the model as a dual-approach dynamics-balanced model.
The macro and demographic determinants discussed are primarily derived from Asiedu et al (2009), with some modifications In the Foreign Direct Investment (FDI) equation, economic infrastructure is represented by the total density of communication utilities (utilcom), which includes telephone lines.
Several studies, including those by Asiedu et al (2009), Busse and Hefeker (2007), Jensen (2003), and Gastanaga, Nugent, and Pashamova (1998), utilize the first lag of Foreign Direct Investment (FDI) as an explanatory variable The rise in current FDI inflows can enhance the usage of communication systems; therefore, we lag this variable to prevent interaction with both the dependent variable and its first lag in the equation Additionally, trade openness, defined as total exports and imports over GDP, is lagged by two periods due to FDI's direct impact on this variable within the same year As foreign investors engage in activities like greenfield investments or mergers and acquisitions, they are likely to export machinery, equipment, and expertise while importing final products from the host country Moreover, the consumption by expatriates contributes to the in-border exports of the destination country.
Foreign Direct Investment (FDI) inflows can significantly influence a country's GDP growth within the same year; thus, GDP growth is lagged by two periods to mitigate any reverse effects from FDI Additionally, various institutional factors that may impact FDI inflows, including regulatory quality, control of corruption, government efficiency, rule of law, and political stability, will be analyzed individually in the regression analysis.
To analyze the relationship between foreign aid and foreign direct investment (FDI) in relation to current GDP per capita, we lag the variable lngdppc We anticipate a negative correlation between a country's income level and the amount of aid it receives, with the diminishing rate of this relationship not remaining constant, prompting the inclusion of a quadratic component for lngdppc Additionally, we lag the macro variable debt over GDP (debtgdp) to accurately reflect the impact of foreign aid on current sovereign debt, as historical debt levels are more relevant for determining future aid disbursements.
Foreign aid is influenced by key political and institutional factors, including democracy, corruption control, and government efficiency Additionally, population size plays a significant role, as smaller countries tend to be more homogeneous, making them more likely to attract foreign support Thus, with similar institutional conditions, countries with lower populations can benefit more from foreign aid inflows.
In comparison with the trade openness used in the FDI equation, a smaller population represents a larger social openness at the country level 5
VARIABLE mean sd N mean sd mean sd
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
Freedom 4.136 1.848 4,014 4.838 1.690 3.686 1.805 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
Note: Full sample (144 countries) includes low-income (54 countries) and middle-income (90 countries) subsamples
This study focuses on low- and middle-income countries, which have predominantly received foreign aid The classification of these countries is based on their Gross National Income (GNI) per capita as recorded by the World Bank in 2013 Macro data utilized in the research is primarily sourced from the World Development Indicators (World Bank, 2014) and the World Economic Outlook (IMF, 2014), while institutional data is derived from the Worldwide Governance Indicators (World Bank).
The study focuses on key governance indicators such as regulatory quality, control of corruption, government efficiency, rule of law, and political stability, analyzing data from 1996 to 2012 To enhance the analysis, the research incorporates the democracy score from Freedom House, providing a broader perspective on the political landscape during this period.
5 Dudley and Montmarquette (1976) discuss in detail the relationship between small country and foreign aid
The World Bank's 2013 income classifications categorize countries into three groups: low income, with a maximum of $1,035; middle income, ranging from $1,036 to $12,615; and high income, with a minimum of $12,616 These classifications are based on subjective variables, the details of which are summarized in Table 1, with further information on the variables and data sources provided in Table A1 of Appendix A.
RESULTS
Independent Marginal Effects between Institutions, Foreign Aid, and FDI
Table 2 presents the regression results derived from the DADB model, with Panel A detailing the base regression for various types of foreign aid: aggregate (AA), bilateral (BA), and multilateral (MA) The analysis focuses on three key variables: FDI inflows, foreign aid, and institutional quality In evaluating FDI, five institutional measures—rule of law, regulatory quality, control of corruption, government efficiency, and political stability—are assessed individually, revealing a positive correlation with FDI inflows Panel A of Table 3 highlights the coefficients and significance levels, indicating that, aside from government performance, all institutional measures significantly enhance FDI attraction, with significance levels reaching at least 5 percent Notably, regulatory quality in the private sector emerges as the most influential factor for multinational enterprises (MNEs), where a one-point increase in this measure correlates with a 0.66 percentage-point rise in FDI inflows, even when accounting for other persistent factors.
To analyze the impact of government policies on foreign direct investment (FDI), we utilize principal component analysis (PCA) to create a composite institutional index, referred to as INSF This index reflects the positive influence of various institutional measures on FDI and will be applied in subsequent regression analyses The rationale for this composite approach lies not only in reducing complexity but also in the observation that these measures often correlate; typically, a well-functioning government exhibits strong performance in areas such as rule of law, regulatory quality, and corruption control, while minimizing political instability and conflict.
Bilateral aid positively influences foreign direct investment (FDI), with a 1% increase in bilateral aid linked to a 0.18% rise in FDI, consistent with Rodrik's findings In contrast, multilateral aid has a more significant impact, with a coefficient of 0.39, as it attracts foreign investors from various countries, unlike bilateral aid, which is limited to donor nations An analysis of low-income and middle-income countries reveals that foreign aid's effect on FDI is more pronounced in low-income countries, where it is over twice that of middle-income nations, despite the absence of a significant institutional impact This discrepancy may arise from a perceived institutional threshold that foreign investors recognize, where lower-quality institutions in low-income countries deter investment considerations Additionally, the obligations imposed by international organizations on low-income countries enhance foreign investor confidence, as these nations often rely on foreign aid, which is significantly higher than that received by middle-income countries.
In the FDI equation, other control variables show statistical significance and align with expected outcomes A 1% increase in GDP growth or trade openness can lead to net FDI inflows rising by 0.06% or 0.01% of GDP, respectively, after two years Additionally, an increase of 10 units per 100 people in the total number of telephone lines, mobile subscribers, and internet users may result in private capital inflows growing by 0.1% of GDP within the subsequent two years.
The insignificant coefficients of FDI inflows in the aid equations presented in Table 2 indicate that foreign direct investment does not significantly influence foreign aid levels This suggests that countries with higher FDI do not necessarily receive more or less bilateral or multilateral aid While FDI may encourage additional support from certain donors, the overall amounts of bilateral aid received by countries remain unaffected by their total FDI inflows This finding aligns with Alesina and Dollar's (2000) assertion that there is no established relationship between FDI and foreign aid.
The democracy index, which assesses political rights and civil liberties, significantly influences the allocation of both bilateral and multilateral foreign aid, indicating that countries with greater freedoms receive more assistance Additionally, factors such as control of corruption and political stability also affect aid distributions from donors Notably, while corruption is shown to decrease foreign aid, this finding contrasts with previous research by Alesina and Weder (2002) Moreover, the impact of political stability on aid is notably stronger than that of corruption To further analyze governance's role in foreign aid, we developed a composite institutional index, INSA, which evaluates various governance indicators The effects of foreign direct investment (FDI) and institutional quality on foreign aid are detailed in the latter sections of Table 3, specifically for low-income and middle-income countries.
Other control variables in foreign aid distribution equations significantly influence outcomes, with the squared income level indicating a negative marginal effect that diminishes over time Furthermore, income levels are more impactful in multilateral aid equations compared to bilateral ones, highlighting multilateral organizations' commitment to poverty reduction Lastly, countries with higher debt or smaller populations tend to be more open and inclined to seek assistance from foreign entities and international organizations.
Reliability and Robustness Checks
In this section, we assess the reliability and robustness of the specified model The reliability is evaluated by omitting the lagging of control variables in both equations To further ensure robustness, we conduct three additional experiments: first, we remove a control variable from the model; second, we introduce several irrelevant variables; and third, we apply different regression methods, including POLS, difference GMM (Arellano & Bond, 1991), and system GMM (Blundell & Bond, 1998), to each equation separately.
Panel C of Table 2 presents findings without lagging the control variables, revealing that the non-lag specification shows a diminished effect of GDP growth at 0.05, compared to 0.06 in the lagged version This reduction is attributed to the feedback loop of the dependent variable GDP growth is a critical factor for multinational enterprises (MNEs) when selecting investment locations, as higher GDP growth indicates greater potential for absorbing investments and generating profits Consequently, when foreign investors enter a high-growth country, they enhance the local production of goods and services, creating a reciprocal relationship where GDP growth is positively influenced by foreign direct investment (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,
∆ >x and the following marginal effect on the dependent variable yis β real Then, we have
/ real y x β = ∆ ∆ Next, the change ∆yof yenlarges the self-change of xan additional amount feed 0.
∆x ≥ Hence, the total change of xis equal to ∆x nominal = ∆ + ∆x x feed The coefficient of x which we obtain in the multivariate regression of y is nominal real feed y y x x x β = ∆ ≤ ∆ = β
The nominal rate of bias is defined as nominal 1. real h β
In this analysis, we find that with a nominal β of 0.05 and a real β of 0.06, the calculated h is approximately 0.83 This indicates that when considering the reverse causality of foreign direct investment (FDI) inflows, the marginal effect of GDP growth is downward-biased, representing only 83 percent of its actual value.
In contrast to GDP growth, the trade openness coefficient in the non-adjusted specification is significantly larger and more impactful than in the adjusted specification This discrepancy arises from the interdependent nature of the variables involved, where the relationship can be expressed as y=βx Due to the componential relationship, this can be reformulated as y=bx, leading to a combined regression of y=(β+b x) Consequently, if the component relationship is positive (b>0), the coefficient of the endogenous variable x increases, resulting in heightened significance levels in the regression, regardless of whether the components are positive or negative For instance, with β =0.01 and β+ =b 0.03, a similar component relationship is observed between debt amounts and aid inflows in the non-adjusted specification, highlighting that foreign aid is a component of sovereign debt for the same year.
A model's reliability is enhanced when its regression coefficients remain stable despite variations in other variable proxies In this regard, the two-lag specification outperforms the non-lag version, as the coefficient for the lag of the FDI variable consistently holds at 0.56, while the non-lag counterpart fluctuates between 0.54 and 0.55 Additionally, the non-lag specification is less robust, as evidenced by the insignificance of the INSF index coefficient when utilcom is excluded from the model, highlighting the superior stability of the two-lag approach.
In our robustness checks, we removed certain control variables from the model, specifically utilcom in the FDI equation and lnpop in the aid equations, with results detailed in Table 2, Panels B, G, and H The removal of utilcom led to a significant increase in the impacts of other control variables—GDP growth, trade openness, and lagged FDI inflows—while the coefficients for our primary variables, the INSF index and foreign aid, became downward biased, with the INSF index exhibiting instability Despite these changes, the significance of their effects on FDI inflows persisted, and multilateral aid's impact remained double that of bilateral aid The omission of utilcom did not notably alter the three aid equations, whereas the absence of lnpop highlighted the influence of corruption control and political stability on foreign aid allocation.
In our analysis, we incorporated several irrelevant or weakly relevant variables into the base specification of the DADB model Specifically, we added the money stock M2 to the FDI equation and included the inflation rate in the aid equations, along with the young- and old-dependency ratios of the working-age population The results, presented in Panels E and F, indicate that while domestic financial development, inflation, and younger dependents show no relevance, the older dependency ratio exhibits some indirect significance This ratio can reflect human capital, living conditions, and future debt repayment capabilities On one hand, favorable human capital and living conditions may attract foreign investment; on the other hand, the older population's limited work capacity and debt repayment ability could raise concerns for bilateral donors In our regression analysis, the coefficients for the old-dependency ratio in the FDI and bilateral aid equations were found to be 0.09 and -0.06, respectively, with weak significance levels.
In column (14), the coefficient of freedom in the bilateral aid equation decreases from -0.15 to -0.08, indicating a reduction in significance compared to the base regression in column (2), suggesting that the role of freedom in bilateral aid disbursements may be plausible Similar trends are observed with the INSF index and communication utilities in the FDI equation (Panel F, column 16) However, in other cases within Panels E and F, the coefficients and their significance levels remain largely 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, 1996-
VARIABLE AA BA MA AA BA MA
Note: 3SLS estimation, t-statistics in parentheses, *** p F value of 0.000 and a maximum F statistic of 72.04, indicating a strong model fit The analysis includes 1537 observations across 130 groups, with an average of 11.82 observations per group and a minimum of 1 The time variable is set to year, and the number of instruments utilized in the study is 123.
(Robust, but weakened by many instruments.)
Hansen test of overid restrictions: chi2(116) = 123.11 Prob > chi2 = 0.308 (Not robust, but not weakened by many instruments.)
Sargan test of overid restrictions: chi2(116) = 603.07 Prob > chi2 = 0.000 Arellano-Bond test for AR(2) in first differences: z = -0.02 Pr > z = 0.988Arellano-Bond test for AR(1) in first differences: z = -2.61 Pr > z = 0.009
Table B 9: Regression Result of Table 4, column (9)
Table B 10: Regression Result of Table 5, column (7)
_cons -.5506081 5862545 -0.94 0.349 -1.710527 6093107 lag2tradeopen 0141414 0061888 2.29 0.024 0018968 0263861 lag2utilcom 0115317 0047162 2.45 0.016 0022005 0208628 lag2gdpgrow 0428812 0425304 1.01 0.315 -.0412664 1270287 laginfdigdp 5117775 1054431 4.85 0.000 3031558 7203992 aidgdp 4905417 1542035 3.18 0.002 1854464 7956371 govindex 2007069 0931901 2.15 0.033 0163279 3850858 infdigdp Coef Std Err t P>|t| [95% Conf Interval] Robust
The dynamic panel-data estimation using one-step system GMM yielded significant results, with a Prob > F value of 0.000 and a maximum value of 14 The model, represented by F(6, 129) = 113.49, indicates an average of 11.82 across 123 instruments The analysis included 1,537 observations distributed across 130 groups, with the minimum number of observations per group being 1, and the time variable measured in years.
_cons 48.13831 14.65431 3.28 0.001 19.13562 77.141 lnpop -.8128585 2472221 -3.29 0.001 -1.302142 -.3235753 lag2debtgdp 0241604 0108354 2.23 0.028 0027158 0456049 c.lag2lngdppc 2642384 2642451 1.00 0.319 -.2587355 7872123 c.lag2lngdppc# lag2lngdppc -6.5823 3.934489 -1.67 0.097 -14.36914 1.204543 lagaidgdp 4831472 0709565 6.81 0.000 3427154 623579 insaindex 7525089 2544338 2.96 0.004 2489528 1.256065 infdigdp -.000011 0512869 -0.00 1.000 -.1015142 1014923 aidgdp Coef Std Err t P>|t| [95% Conf Interval] Robust
The dynamic panel-data estimation using one-step system GMM yielded a Prob > F value of 0.000, indicating a statistically significant model The analysis involved 124 instruments across 126 groups, with a total of 1,437 observations The F-statistic was calculated as F(7, 125) = 139.89, and the average value was found to be 11.40, while the maximum value reached 14 The time variable considered in the study was the year, and the minimum number of observations per group was 1.
(Robust, but weakened by many instruments.)
Hansen test of overid restrictions: chi2(116) = 121.99 Prob > chi2 = 0.333 (Not robust, but not weakened by many instruments.)
Sargan test of overid restrictions: chi2(116) = 607.87 Prob > chi2 = 0.000 Arellano-Bond test for AR(2) in first differences: z = 1.47 Pr > z = 0.142Arellano-Bond test for AR(1) in first differences: z = -1.76 Pr > z = 0.078
Table B 11: Regression Result of Table 5, column (8)
_cons 16.27724 8.414567 1.93 0.055 -.3762308 32.93071 lnpop -.5351091 1549214 -3.45 0.001 -.8417178 -.2285005 lag2debtgdp 0124058 0076282 1.63 0.106 -.0026913 0275028 c.lag2lngdppc -.0482114 1577698 -0.31 0.760 -.3604574 2640346 c.lag2lngdppc# lag2lngdppc -.5954528 2.30223 -0.26 0.796 -5.151852 3.960946 lagaidgdp 5936378 1215039 4.89 0.000 3531664 8341092 insaindex 3885303 1540346 2.52 0.013 0836767 6933839 infdigdp -.0070849 0366113 -0.19 0.847 -.0795433 0653735 aidgdp Coef Std Err t P>|t| [95% Conf Interval] Robust
The dynamic panel-data estimation using one-step system GMM yielded significant results, with a Prob > F value of 0.000 and a maximum value of 14 The F-statistic was calculated as F(7, 125) = 143.57, with an average value of 11.40 The analysis included a total of 124 instruments and 1 observation per group, spanning 126 groups and a total of 1437 observations over the time variable of year, specifically categorized by the group variable imfid.
(Robust, but weakened by many instruments.)
Hansen test of overid restrictions: chi2(116) = 122.01 Prob > chi2 = 0.333 (Not robust, but not weakened by many instruments.)
Sargan test of overid restrictions: chi2(116) = 536.89 Prob > chi2 = 0.000 Arellano-Bond test for AR(2) in first differences: z = 1.30 Pr > z = 0.195Arellano-Bond test for AR(1) in first differences: z = -1.99 Pr > z = 0.047
Table B 12: Regression Result of Table 5, column (9)
_cons 33.15753 6.570723 5.05 0.000 20.15326 46.16181 lnpop -.1848659 0542548 -3.41 0.001 -.2922428 -.077489 lag2debtgdp 0124225 0038698 3.21 0.002 0047636 0200814 c.lag2lngdppc 3900247 110271 3.54 0.001 1717848 6082647 c.lag2lngdppc# lag2lngdppc -6.965905 1.695313 -4.11 0.000 -10.32114 -3.610671 lagaidgdp 37808 0422015 8.96 0.000 2945579 461602 insaindex 3236754 1098354 2.95 0.004 1062975 5410532 infdigdp -.0146111 0085929 -1.70 0.092 -.0316175 0023953 aidgdp Coef Std Err t P>|t| [95% Conf Interval] Robust
The dynamic panel-data estimation using one-step system GMM yielded significant results, with a Prob > F value of 0.000 and an F-statistic of 160.63 (F(7, 125)) The analysis included 124 instruments and 1,437 observations across 126 groups, with a minimum of one observation per group The average value was found to be 11.40, while the maximum reached 14, with the time variable specified as the year and the group variable identified as imfid.
(Robust, but weakened by many instruments.)
Hansen test of overid restrictions: chi2(116) = 120.08 Prob > chi2 = 0.379 (Not robust, but not weakened by many instruments.)
Sargan test of overid restrictions: chi2(116) = 579.65 Prob > chi2 = 0.000 Arellano-Bond test for AR(2) in first differences: z = -0.30 Pr > z = 0.765Arellano-Bond test for AR(1) in first differences: z = -1.45 Pr > z = 0.147