INTRODUCTION
Practical Motivation and Research Problems
Foreign aid plays a crucial role in attracting private capital inflows, which are essential for fostering growth, technology advancement, and job creation in host countries However, studies indicate that there is no strong correlation between foreign aid and foreign direct investment (FDI) (Alesina & Dollar, 2000; Harms & Lutz, 2006).
Harms and Lutz (2006) emphasize the importance of political and institutional characteristics in understanding the relationship between these factors and private capital inflows The quality of a host country's institutions serves as a significant determinant of foreign direct investment (FDI) Numerous studies have demonstrated that poor institutional quality negatively impacts FDI inflows Interestingly, some nations with high levels of corruption and weak political institutions continue to attract substantial FDI, as noted by Habib and Leon (2002).
Estimating the influence 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 endogeneity using lagged variables, they primarily offer technical solutions without addressing the underlying problems Asiedu, Jin, and Nandwa (2009) suggest a simultaneous equations model to tackle these issues, positing that foreign aid and FDI influence each other concurrently However, this dual approach has flaws, such as the absence of institutional determinants in the aid equation and the contradictory interpretation of the positive coefficient of FDI, which implies that while foreign aid may reduce FDI, FDI could simultaneously increase foreign aid.
Research Objectives
This article explores the impact of foreign aid in mitigating the negative effects of expropriation risk on foreign direct investment (FDI) We analyze both low-income and middle-income countries, expanding on previous research that focused solely on low-income nations Unlike Harms and Lutz (2006), we employ a distinct model with varied variable proxies and examine a more recent timeframe to evaluate how foreign aid influences FDI.
This article explores the intricate relationship between foreign aid and foreign direct investment (FDI) by analyzing data within a specified framework It aims to determine if multilateral aid can effectively increase FDI, providing empirical evidence on the influence of institutions in this context The research reexamines the hypothesis that stronger institutions attract more FDI, supported by existing literature Additionally, it evaluates the significance of various institutional measures on FDI and investigates whether factors such as democracy, control of corruption, and political stability enhance a country's ability to secure foreign aid.
Structure
Chapter 2 reviews a prominent trade theory that elucidates the investment decisions of foreign investors, focusing on studies that identify institutions and foreign aid as key factors influencing private capital inflows Chapter 3 outlines the dual-approach framework and regression model, detailing the variables and data sources used Empirical findings and their interpretations are presented in Chapter 4, while Chapter 5 summarizes the results to inform policy and further research.
LITERATURE REVIEW
This article begins by examining the OLI theory related to foreign investors' investment decisions It then discusses various studies that consider institutions and foreign aid as distinct factors influencing foreign direct investment (FDI) Following this, the research that integrates political and institutional elements into the relationship between foreign aid and FDI is explored Finally, the article summarizes the institutional factors that could impact foreign aid.
The OLI paradigm, developed by Dunning (1988, 1998, 2001), serves as a comprehensive framework for understanding foreign investment activities It comprises three key components: Ownership (O), Location (L), and Internalization (I) The O component highlights the comparative advantages of firms, focusing on their products and capabilities for international expansion The L component addresses location advantages, including access to human and natural resources, production conditions, research opportunities, and market size in the host country Lastly, the I component examines internalization advantages, emphasizing strategies to reduce transaction costs when firms decide between importing intermediate products or integrating foreign suppliers into their production processes This framework aids firms in making informed decisions about their investment destinations.
Political and institutional factors in the host country are key location advantages within the OLI framework Research by Habib and Leon (2002) and Dunning and Lundan (2008) highlights how these factors impact foreign investors Specifically, Habib and Leon (2002) identify a negative correlation between corruption levels in host countries and foreign direct investment (FDI) inflows, suggesting that foreign investors perceive corruption as a violation of ethical standards and a source of additional costs Furthermore, in countries like the United States, paying bribes is illegal, further deterring investors from engaging in corrupt practices (Hines, 1995).
Busse and Hefeker (2007) analyze the influence of government stability, law and order, conflict absence, ethnic harmony, corruption control, democracy, and bureaucratic efficiency on foreign direct investment (FDI) inflows into developing nations 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 significance of these variables 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 greater 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 significantly influences the investment decisions of foreign enterprises, particularly in low-income and Sub-Saharan African nations.
While research linking poor institutions to foreign direct investment (FDI) is scarce, Egger and Winner (2005) provide empirical evidence supporting a positive correlation between corruption and inward FDI Their study, which encompasses both developed and developing nations from 1995 to 1999, reinforces Leff's (1964) argument that bribery may mitigate uncertainty for foreign investors in low-information environments, thereby protecting their interests amid significant economic and political fluctuations.
The impact of foreign aid on foreign direct investment (FDI) is complex and often contradictory, as it can yield both positive and negative effects Research indicates that foreign aid may enhance FDI through improved infrastructure but can also lead to rent-seeking behaviors Many studies, including those by Bird & Rowlands (1997) and Harms & Lutz (2006), fail to find a significant relationship between aggregate aid and FDI While Selaya and Sunesen (2012) identify a positive influence, the role of bilateral aid in attracting FDI is more consistently supported by evidence from Rodrik (1995) and Kimura & Todo (2010) Conversely, there is a lack of empirical support for the effectiveness of multilateral aid in boosting FDI Notably, Asiedu et al (2009) report negative correlations between various forms of aid and FDI in low-income and Sub-Saharan African countries.
Harms and Lutz (2006) investigate the nuanced relationship between foreign aid and foreign direct investment (FDI) by considering institutional variables and employing various estimation methods across different time periods and country groups Despite their comprehensive approach, they find that, on average, foreign aid does not significantly impact FDI However, they uncover a significant finding: foreign aid positively influences FDI specifically in countries with high regulatory hindrances and low institutional quality.
Foreign aid plays a crucial role in mitigating expropriation risk, which subsequently boosts foreign investment in certain countries As highlighted by Asiedu et al (2009), the interaction between expropriation risk and foreign aid reveals a positive correlation, indicating that heightened expropriation risk can enhance the impact of foreign aid on foreign direct investment (FDI) Despite the initial negative coefficient associated with foreign aid, an increase in expropriation risk may align foreign aid 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, Alesina and Weder (2002) found that corruption levels do not significantly impact the decisions of most donors.
MODEL AND DATA
Dual-Approach Framework
The impact of foreign aid on private capital inflows can be significant, particularly with multilateral aid, which aims to enhance social and economic infrastructures, often referred to as the "infrastructure effect." However, this effect may not manifest immediately and is more likely to occur over the long term For instance, if country A receives more multilateral aid than country B under similar conditions, it does not guarantee that country A will see an immediate improvement in its infrastructure or attract more foreign investors This is because multilateral aid typically does not target specific industries; instead, its information and conditional policy functions may provide a protective layer for foreign investors in recipient countries Consequently, this paper introduces the concept of the "institutional effect" of multilateral aid, which can safeguard foreign investors but does not alter the institutional framework in the short term.
Bilateral aid, while focusing on productive sectors, also significantly contributes to infrastructural development, mirroring the infrastructure impact of multilateral aid Additionally, bilateral aid fosters private capital inflows through a phenomenon known as the "vanguard effect." This effect, akin to the institutional impact of multilateral aid, specifically benefits bilateral investors It is hypothesized that the coefficient α1 remains positive for both types of foreign aid, although the level of impact is anticipated to differ systematically.
Multilateral aid plays a significant role in influencing government performance, as discussed by Rodrik (1995) The marginal effect of foreign aid can be represented as a linear function of institutional quality, expressed as α δ 1 + institution Harms and Lutz (2006) were pioneers in incorporating political and institutional variables into the analysis of how foreign aid impacts foreign direct investment (FDI).
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 incentivizes the home country's government to provide aid, although this support is not obligatory, as nations generally welcome FDI The correlation between economic activities and diplomatic relations allows for FDI to influence bilateral aid decisions Conversely, multilateral donors typically disregard FDI levels in recipient countries Therefore, in the short term, the impact of FDI inflows is likely to be positive in the context of bilateral aid while remaining neutral for multilateral aid.
Foreign Direct Investment (FDI) can indirectly influence changes in both bilateral and multilateral aid over time As FDI contributes to GDP growth and increases GDP per capita, these economic improvements can reduce a host country's reliance on concessional loans and donor grants Consequently, in the long run, the coefficients of FDI inflows in aid equations are expected to become negative, indicating a diminishing need for foreign aid as the nation's wealth grows.
The impact of Foreign Direct Investment (FDI) on foreign aid can be influenced by the quality of institutions, such as levels of democracy and corruption, which significantly affect GDP growth and GDP per capita By incorporating institutional factors into the analysis of FDI inflows, we can assess how effectively an economy utilizes FDI, thereby reducing 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
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, specifically denoting these as infdigdp and aidgdp for country i in year t A key distinction from previous studies, such as that by Asiedu et al (2009), is the inclusion of the first lag of aid inflows (aidgdp i t , − 1), reflecting the gradual disbursement of foreign aid tied to project completion Alongside the first lag of FDI inflows (infdigdp i t , − 1), this adjustment creates 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 utilized in this study are primarily derived from Asiedu et al (2009), with certain modifications In the foreign direct investment (FDI) equation, the economic infrastructure is represented by the total density of communication utilities, 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 may enhance the use of communication systems, necessitating the lagging of this variable to prevent interaction with the dependent variable and its first lag in the equation Additionally, trade openness, measured by total exports and imports relative to GDP, is lagged by two periods due to FDI's immediate impact on this variable As foreign investors engage in activities such as greenfield investments or mergers and acquisitions, they tend to export machinery, equipment, materials, and experts to the host country while importing final products Furthermore, 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 in the same year; therefore, GDP growth is lagged by two periods to account for any reverse effects of FDI Additionally, key institutional factors that may impact FDI inflows include regulatory quality, control of corruption, government efficiency, rule of law, and political stability The effects of these institutional measures will be analyzed individually in the regression analysis.
To analyze the relationship between foreign aid and FDI in relation to GDP per capita, we lag the variable lngdppc to prevent component overlap We anticipate a negative correlation between a country's income level and the aid it receives, with the diminishing rate of aid not remaining constant, prompting the inclusion of a quadratic term for lngdppc Additionally, we lag the macro variable debt over GDP (debtgdp) to account for the impact of past sovereign debt on future aid disbursements This approach recognizes that historical debt levels are more relevant in determining future aid allocations.
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 countries with smaller populations tend to be more homogenous, which increases their likelihood of receiving foreign support When other institutional factors remain constant, a lower population can enhance a country's appeal to the international community for aid.
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, as these nations have predominantly received foreign aid The classification of these countries is based on the World Bank's GNI per capita data from 2013 Key macroeconomic data is 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 analyzes key governance indicators such as regulatory quality, corruption control, government efficiency, rule of law, and political stability, focusing on data from 1996 to 2012 To enhance the analysis, the research incorporates the democracy score from Freedom House, providing a broader perspective on governance during this period.
5 Dudley and Montmarquette (1976) discuss in detail the relationship between small country and foreign aid
In 2013, the World Bank classified countries into three income categories: low income (up to $1,035), middle income ($1,036 to $12,615), and high income (at least $12,616) Table 1 provides a summary of the descriptive statistics for these classifications, while Table A1 in Appendix A offers further details on the variables and their data sources.
RESULTS
Independent Marginal Effects between Institutions, Foreign Aid, and FDI
A one-point increase in sound policy measures correlates with a 0.66 percentage point rise in foreign direct investment (FDI) inflows, even after accounting for the persistence of these inflows and other influencing factors.
To analyze the impact of government policies on foreign direct investment (FDI), we employ principal component analysis (PCA) to create a composite institutional index, referred to as INSF This index encapsulates the positive effects of various institutional measures on FDI, facilitating later regression analyses Additionally, the composition of this index simplifies our workload, as these measures typically correlate; a stable government is characterized by high scores in rule of law, regulatory quality, control of corruption, and government efficiency, with minimal instances of political instability, civil unrest, and external conflicts.
Bilateral aid positively influences foreign direct investment (FDI), with a 1% increase in bilateral aid leading to approximately a 0.18% increase in FDI, consistent with Rodrik (1995) In contrast, multilateral aid has a more significant impact, with a coefficient of 0.39, as it attracts foreign investors from all countries, unlike bilateral aid, which primarily benefits donor countries Analysis of low-income versus middle-income countries reveals that foreign aid's effect on FDI is more than double in low-income nations, although institutional quality does not appear to influence FDI in these countries This discrepancy may be attributed to a perceived institutional threshold; foreign investors may disregard differences in institutional quality below a certain level, as low-income countries generally exhibit poorer institutional quality than their middle-income counterparts Furthermore, the extended vanguard effect of bilateral aid and the institutional impact of multilateral aid play a crucial role in low-income countries, where compliance with international regulations enhances investor confidence, given that these nations receive substantially more foreign aid than middle-income countries.
Statistical analysis of the FDI equation reveals that other control variables are significant and align with expected trends Specifically, a 1% increase in GDP growth can lead to a 0.06% rise in net FDI inflows after two years, while a similar increase in trade openness may result in a 0.01% boost Furthermore, an increase of 10 units per 100 people in telephone lines, mobile subscribers, and internet users could enhance private capital inflows by 0.1% of GDP within the subsequent two years.
The findings in Table 2 reveal that the coefficients for FDI inflows in columns (1), (2), and (3 are insignificant, indicating 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 specific donors to provide additional aid, the overall bilateral aid inflows among recipient countries remain unaffected by the total FDI received These results support the assertion made by Alesina and Dollar (2000) that there is no established relationship between FDI and foreign aid.
The democracy index, reflecting political rights and civil liberties, significantly influences the allocation of foreign aid, with countries exhibiting greater freedom receiving more assistance Additionally, factors such as control of corruption and political stability also affect aid distributions from both bilateral and multilateral donors Notably, the empirical evidence indicates that corruption diminishes foreign aid, which contrasts with previous findings by Alesina and Weder (2002), while the impact of political stability is found to be more substantial To further analyze governance indicators, we developed a composite institutional index, INSA, to determine its effect on foreign aid reception The relationship between foreign direct investment (FDI) and institutional quality on foreign aid is detailed in the latter sections of Table 3 for low-income and middle-income countries.
The analysis reveals that various control variables significantly influence the allocation of foreign aid Notably, the squared income level indicates a negative marginal effect that diminishes over time Furthermore, income levels exhibit greater sensitivity and significance in the context of multilateral aid compared to bilateral aid, highlighting the focus of multilateral organizations on poverty reduction Additionally, countries with higher debt or smaller populations tend to be more receptive to seeking assistance from foreign entities and international organizations.
Reliability and Robustness Checks
This section evaluates the reliability and robustness of the specified model Reliability is assessed by not lagging the control variables in the two equations To ensure robustness, three additional experiments are conducted: first, a control variable is excluded from the model; second, irrelevant variables are introduced; and third, various regression methods, including POLS, difference GMM (Arellano & Bond, 1991), and system GMM (Blundell & Bond, 1998), are applied independently to each equation.
Panel C of Table 2 illustrates the findings when control variables are not lagged, revealing that the lagged specification is more reliable Specifically, the effect of GDP growth in the non-lagged model is diminished to 0.05, compared to 0.06 in the lagged model, primarily due to feedback from the dependent variable GDP growth, a crucial indicator of an economy's production and consumption capabilities, significantly influences multinational enterprises (MNEs) in their investment decisions Countries with higher GDP growth present more opportunities for investment, which can be absorbed and yield profits Consequently, as foreign investors expand into higher-growth nations, they enhance the production of goods and services, creating a reciprocal relationship where GDP growth is positively impacted 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 y is β real Then, we have
/ real y x β = ∆ ∆ Next, the change ∆ y of y enlarges the self-change of x an additional amount feed 0.
∆ x ≥ Hence, the total change of x is 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 ratio 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 about 83 percent of its actual value.
In contrast to GDP growth, the coefficient of trade openness in the non-adjusted specification is significantly larger than in the adjusted specification, indicating a strong relationship between the two variables This occurs when one variable is a component of the other, leading to a combined effect in regression analysis Specifically, if the effect of variable x on y is represented as y = βx, and there is a component relationship such that y = bx, the multivariate regression yields y = (β + b)x, resulting in an increased coefficient for the endogenous variable x when the component relationship is positive Consequently, the significance levels in the regression also rise due to direct components, whether positive or negative For instance, in our analysis, we observe β = 0.01 and β + b = 0.03 A similar component relationship exists between sovereign debt and foreign aid inflows in the non-adjusted specification, as foreign aid is considered a component of sovereign debt within the same fiscal year.
A model is deemed more reliable when its regression coefficients remain stable despite variations in the proxies of other variables In this context, the two-lag specification outperforms the non-lag version, as the coefficient for the lagged 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 INSF index coefficient becoming insignificant when utilcom is excluded from the model, highlighting the superiority of the two-lag approach.
In our robustness checks, we analyze the effects of removing certain control variables from our model, specifically utilcom in the FDI equation and lnpop in the aid equations The findings, detailed in Panels B, G, and H of Table 2, reveal that eliminating utilcom leads to a significant increase in the impacts of GDP growth, trade openness, and lagged FDI inflows in the FDI equation, while the coefficients for the INSF index and foreign aid become downward biased, with the INSF index coefficient showing instability Despite this, the significance of their impacts on FDI inflows persists, and notably, the effect of multilateral aid remains twice that of bilateral aid The omission of utilcom does not substantially alter the three aid equations Conversely, removing lnpop highlights the importance of control of corruption and political stability in attracting more foreign aid.
In our analysis, we incorporated irrelevant or weakly relevant variables into the DADB model's base specification, specifically adding the money stock M2 to the FDI equation and the inflation rate to the aid equations Additionally, we included young- and old-dependency ratios of the working-age population for placebo tests The results, displayed in Panels E and F, indicate that while domestic financial development, inflation, and younger dependents are largely irrelevant, the old-dependency ratio shows some indirect relevance This ratio may reflect factors such as human capital, living conditions, and future debt repayment capabilities Notably, human capital and living conditions can attract foreign investment, whereas a lower ability to work and repay debt among the elderly may raise concerns for bilateral donors The regression analysis reveals coefficients for the old-dependency ratio of 0.09 in the FDI equation and -0.06 in the bilateral aid equation, both exhibiting weak significance levels.
In the analysis presented in column (14), the coefficient of freedom in the bilateral aid equation decreases from -0.15 to -0.08, indicating a reduction in its significance level compared to the base regression in column (2) This suggests that freedom may play a plausible role in bilateral aid disbursements Similar trends are observed with the INSF index and communication utilities in the FDI equation (Panel F, column (16)) However, for other cases in 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 (F(6, 129)) The average value across the dataset is 11.82, with a total of 123 instruments utilized The study comprises 130 groups, totaling 1,537 observations, with a minimum of one observation per group, and employs a dynamic panel-data estimation using one-step system GMM The time variable is defined as 'year,' and the group variable is identified as 'imfid.'
(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 analysis reveals a significant F-statistic of 113.49 with a p-value of less than 0.001, indicating strong statistical significance The dataset comprises 1,537 observations across 130 groups, with an average of 11.82 observations per group and a maximum of 14 The estimation method employed is a one-step system Generalized Method of Moments (GMM) for dynamic panel data, utilizing a time variable defined by the year and a group variable identified as imfid, with a total of 123 instruments.
_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 reveals significant findings, with a Prob > F value of 0.000 and a maximum value of 14 The analysis involved 124 instruments across 126 groups, yielding a total of 1,437 observations, with an average value of 11.40 and a minimum of 1 observation per group The time variable considered in this study is year, and the model is identified by the group variable imfid.
(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, indicating strong statistical relevance The maximum value recorded was 14, while the average stood at 11.40 The analysis included 124 instruments across 126 groups, with a total of 1,437 observations, where the minimum number of observations per group was 1, and the time variable considered was the year.
(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 a one-step system GMM reveals significant findings, with a Prob > F value of 0.000 and a maximum value of 14 The analysis, which includes 124 instruments and 1 observation per group, shows an average of 11.40 across 1437 total observations from 126 groups, with the group variable identified as imfid and the time variable as year The F-statistic is reported as F(7, 125) = 160.63, indicating a robust model.
(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