Political Corruption and Its Link with Foreign Policy in South Asia ()
1. Introduction: Characterizing General Features of Political
Corruption
Political corruption is commonly defined by political scientists as the abuse of public power for illegitimate private benefit. According to Dr. Vito Tanzi, internationally renowned economist and former director of the Fiscal Affairs Department at the International Monetary Fund, forms of corruption include bribery, extortion, fraud, rent-seeking, nepotism, insider trading, and theft. Corruption can involve politicians, government bureaucrats, or any other public officials (Tanzi, 1998).
In the field of political science, numerous scholarly works, such as (Lane, 2017), have posited that the modern definition of corruption may not fully encompass its far-reaching consequences. For example, bribery, often cited as the most essential property of corruption, is often defined as a sum of money or other inducement offered for an illegal purpose. However, even such definition can be rather vague, since issues like favoritism, embezzlements, tax evasion, and money laundering are all highly up for interpretation on a case-by-case basis for whether they conform to this explication. In both the private and public sectors, the view of what constitutes a corrupt practice often boils down to legal condemnation and judgment (Lane, 2017).
Given the inherent complexity of corruption, we characterize the phenomenon with a pertinent denotation. In essence, corruption simply undermines honest governance. It is an age-old issue that has persisted in various forms of government, dating centuries back to ancient civilizations. When analyzing the contemporary geopolitical landscape, it is evident that corruption still runs rampant. The purpose of this literature review is to explore the existing scholarly conversation surrounding political corruption and delineate the apparent gap in the literature regarding the link between corruption and foreign policy.
2. Literature Review
2.1. Highlighting Prevalence of Political Corruption
Political corruption exists as a highly pervasive issue, transcending geographical boundaries. Comprehending its prevalence is key in assessing both the domestic impacts of corruption on countries, as well as its broader geopolitical influence. Amongst the most prominent organizations dedicated to researching and counteracting corruption worldwide is Transparency International. As described by Samuel Kimeu, Executive Director of Transparency International in Kenya, this international association has worked since 1993 as the leader in the global anti-corruption movement (Kimeu, 2014). To better quantify the phenomenon of corruption and its prevalence, Transparency International developed the Corruption Perceptions Index (CPI), which ranks 180 countries by perceived levels of corruption. This index serves as the gold standard in the political science field for researchers seeking to gauge corruption trends. Despite persistent efforts by a multitude of anti-corruption movements, a 2022 report by Transparency International showed that corruption still thrives across the globe. Furthermore, corruption remains the most prevalent in developing regions, particularly South Asia, the Middle East/North Africa, and Sub-Saharan Africa.
2.2. Causes of Political Corruption
Considering the rampancy of corruption across the globe, we must first assess its causes. The existing literature posits that the existence of political corruption in a country cannot be attributed to one prime cause; rather, a multitude of factors have been found as possible sources of higher corruption levels. In a cross-national analysis of multiple variables that have been hypothesized to have links with corruption, Professor of Political Science Daniel Triesman from the University of California, Los Angeles revealed that a country having a colonial background was strongly associated with higher overall corruption levels (Treisman, 2000). This may serve as a rationale for why developing regions like South Asia still struggle with corrupt governance. The study also pointed to political stability as a key determinant, revealing that historically greater political instability corresponded with greater corruption levels. An analysis by researchers from The Bank of Finland Institute for Emerging Economies reveals that the lack of democratization is an essential factor linked with corruption, as governments that displayed more characteristics of democracy were shown to have lower levels of corruption (Goel & Nelson, 2010). Increased economic prosperity, which is often a byproduct of democracy, had an analogous effect on democratization, with the results revealing that higher GDP per capita and lower poverty levels also correlated with less corruption. (Treisman, 2000) also highlighted that countries with more effective legal systems were found to demonstrate lower corruption levels. This is because a stronger rule of law creates an environment in which corrupt activities are easier detected and punished. Nations in which public officials had, on average, higher relative salaries were found to exhibit lower corruption levels as well, indicating that better public wages may incentivize corrupt activity as well taking into account that South Asia and other economically developing regions often struggle with democratization and exhibit more political instability, weaker rule of law, and have lower salaries for public officials, researchers can shed light on several factors that may serve as inherent causes of political corruption. The remainder of this literature review will shift to a discussion of the repercussions corruption holds for nations.
2.3. Research on Domestic Impacts of Political Corruption
Before further analysis of this phenomenon from an international perspective, it is essential to consider the internal pressures that corruption exerts on nations. The existing collection of literature on corruption centers around national-level impacts. Economics Professor Arvind Jain from Concordia University performed an extensive analysis of the domestic consequences of corruption (Jain, 2001). His research, along with an extensive body of other foundational scholarly work, highlights the tremendous disadvantages political corruption poses for countries in the social, economic, and domestic spheres.
2.3.1. Economic Implications
Research has demonstrated the severe detrimental economic effects political corruption has, namely hindered economic growth, lower private sector productivity, and natural resource abuse. Firstly, Jain describes that higher corruption levels are negatively related to macroeconomic growth measures, most importantly GDP. This assertion is confirmed by a study conducted by German economists Axel Dreher and Thomas Herzfeld, who determined that a one-point rise in the CPI correlates with a reduction in GDP growth by 0.13% and in GDP per capita by approximately 425 US$ (Dreher & Herzfeld, 2015). Jain explains that this occurs because corruption in an economy diminishes free-market competition. This causes large corporations in these highly corrupt countries to display worse performance, inhibiting private sector growth (Jain, 2001). Furthermore, countries disposed with an abundant amount of natural resources, especially in developing regions, experience an even greater magnitude of economic growth from corruption. Economics Professors Sambit Bhattacharyya and Roland Hodler justify that this is because natural resource wealth gives corrupt, rent-seeking officials more opportunities to exploit profit from resources for their private gain (Bhattacharyya & Hodler, 2010). They highlight how developing nations abundant in resources like oil and gold, especially those governed by non-democratic regimes, are likelier to see corrupt power abuses. Such foundational literature has consistently reiterated the fact that the presence of corruption serves as a hindrance to the economic development of a country.
2.3.2. Social Implications
Not only has corruption been studied to negatively impact economies, but it also poses significant issues for everyday citizens. The foundational work by Professor Jain describes how corrupt governance leads to the misallocation of public resources. This occurs as government tax revenue is inefficiently spent or exploited. This causes key societal needs for people, like healthcare and education, to be neglected, leading to societal inequality (Jain, 2001). Research by Professor Selçuk Akçay from Afyon Kocatepe University adds to the findings by Jain, detailing that corruption further reduces human development by restricting fundamental economic and political freedoms and lowering overall standards of living (Akçay, 2006). Professors Mohammad Aman Ullah and Eatzaz Ahmad from the Pakistan Institute of Development Economics build on this research, reporting how corruption distorts income distribution by increasing income inequality. Their findings provide that a one standard deviation worsening on the CPI increases the Gini coefficient by 1.3% (Ullah & Ahmad, 2016). The researchers explain that corrupt activity disproportionately favors the wealthy and well-connected members of society at the expense of the middle and lower socioeconomic classes. Therefore, the consensus amongst the literature here creates the notion that corruption acutely degrades overall quality of life.
2.3.3. Political Implications
Existing scholarly work has also explored how corruption poses problems for governments. The research by Professor Jain asserts that corruption contributes to government instability, giving rise to more political conflicts. Corruption also undermines the core principles of democracy (Jain, 2001). For this reason, as confirmed by research by Hanne Fjelde and Havard Hegre, researchers from the Peace and Conflict Research at Uppsala University, higher levels of corruption uphold more autocratic regimes rather than democratic governments. Conversely, autocracies also allow for a greater amount of corruption to persist than democracies (Fjelde & Hegre, 2014). In research on corruption and economic growth in the countries part of the Economic and Monetary Community of Central Africa, (Ondo, 2017) highlights how bureaucratic corruption emerges in nations with excessive, strict regulation in order to ameliorate the burdensome effects of the harsh regulatory environment on economic efficiency. By utilizing a “grease in the wheel approach,” these less democratic nations allow corruption to exist as a means to allow economic growth in spite of cumbersome governance. Acknowledging this, several political experts have suggested democratization as a policy recommendation to curb corruption levels. However, based on research by Susan Rose-Ackerman, Professor of Law and Political Science at Yale University, this has not been easy, as efforts to introduce elements of democracy into authoritarian regimes to curb political corruption have been historically unsuccessful (Rose-Ackerman, 1999). As a consequence, high levels of corruption persist as a source of political tension for both democratic and non-democratic countries.
2.4. Lack of Research on International Impacts of Political
Corruption
Although political corruption has been heavily scrutinized at a national level by the aforementioned literature, potential international implications of corruption lack the same groundwork. Global economists have claimed that the overarching issue of corruption has not been investigated internationally to the extent that the rapid globalization of business has necessitated (Bahoo et al., 2020). The scholarly work on the transnational implications of corruption is described as “scattered” by (Bahoo et al., 2020), as it does not fully encapsulate the multidisciplinary impact of corruption on the legal/political, commercial, or diplomatic perspectives of nations. Instead, existing literature is mostly speculative regarding how global affairs of countries are impacted by corruption. For example, reputed global economist Kimberly Ann Elliott from the Center for Global Development performed an extensive analysis of the complexities of corruption from an international perspective. She speculates that corruption likely plays a significant role in global affairs like international trade and statesmanship. Elliott echoes claim by Transparency International, affirming that corruption still runs rampant within the developing world. Her perspective also provides further insight on assertions made by Professor Rose-Ackerman, outlining that international anti-corruption movements launched by organizations like the International Chamber of Commerce, the Organization for Economic Cooperation and Development, and the World Bank have largely failed to address the global scope of corruption (Elliott, 1997). Hence, Elliott proclaims that the phenomenon of corruption unequivocally warrants further research through an international lens.
2.5. Delineating Gap in Literature: Link between Political
Corruption and Foreign Policy in South Asia
Specifically, foreign policy is an aspect of international affairs that is completely unexplored in relation to political corruption. According to Professor of International Relations Steve Smith from the University of Exeter, foreign policy is defined as the strategies and actions employed by a state in its international affairs. It is a multifaceted field encompassing the economic, diplomatic, and militaristic objectives of states in their global relationships (Smith, 1986). Given that political corruption has not been thoroughly analyzed through an international perspective, gaining insight into a potential connection between corruption and the foreign policies of nations would be instrumental to furthering the understanding of modern-day geopolitics.
Furthermore, the gap in the research can be defined more specifically, as there is a lack of substantial research on the international effects of corruption in the developing world. South Asia, amongst all developing regions, commands particular interest. This is because, according to Professor Mushtaq Khan, the head of the Anti-Corruption Evidence Research Consortium, not only is South Asia a region in which corruption is highly prevalent in governance, but it is also by far the fastest economically developing region in the world (Khan, 2006). Considering findings made by other foundational literature, such as (Jain, 2001) and (Dreher & Herzfeld, 2015), that assert that corruption inhibits economic growth, it is imperative that research is done on South Asia specifically to determine how the economic development potential of this region is being inhibited by the presence of corruption. Additionally, South Asia is a region with a historical legacy of British colonialism, following which highly corrupt governance has persisted. The region is home to a rapidly growing population of over 2 billion residing in newly independent, sovereign states, with two South Asian countries even having nuclear weapons (Siddiqui, 2019). Given these reasons, South Asia holds tremendous geopolitical importance in the contemporary world. The dynamics of this critical region as they pertain to corruption and the foreign policies of its countries certainly warrant further investigation.
2.6. Summary of Literature Review and Gap
Political corruption runs rampant across the world. Typically, analysis of this phenomenon centers at the national level. Research has proven the detrimental effects of corruption domestically in terms of inhibiting economic growth, degrading societal development, and causing political instability. However, there is a lack of scholarly work assessing political corruption from an international lens, particularly in the field of foreign policy. The literature that does exist on potential worldwide implications of corruption is highly speculative in nature, demanding further investigation on corruption from a global affairs perspective. South Asia, specifically, is a region that holds tremendous geopolitical importance when analyzing corruption, as it is a rapidly developing region that has experienced highly pervasive corruption in its governance. Therefore, the gap within the scholarly conversation is the relationship between political corruption and the foreign policies of South Asian nations. Addressing this gap will enable a richer comprehension of the far-reaching ramifications of political corruption in the global context. This analysis will enable South Asian nations to more effectively formulate foreign policy to best counteract potential hindrances caused by political corruption and meet their foreign policy objectives. Also, more international cooperation will be fostered in efforts to curb widespread political corruption.
3. Methodology
3.1. Study Design
This study addressed the research question: to what extent is political corruption related to the foreign policies of South Asian countries? The goal was to determine the extent to which corruption is associated with hindrances to geopolitical relationships formed by nations in South Asia. To formulate a more nuanced answer to the question at hand, a three-part hypothesis was constructed. The researcher hypothesized that political corruption in South Asian nations hinders global economic cooperation, impedes effective foreign diplomacy, and increases militaristic instability.
A quantitative multivariate regression analysis study was conducted to test this hypothesis. In simple regression analysis, the investigator seeks to ascertain the correlation between one independent variable and another dependent variable. Multivariate regression is a more complex form of regression analysis, being a technique measuring the degree to which one independent variable is linearly linked to multiple dependent variables (Alexopoulos, 2010). This was accomplished by running a regression of the independent variable against each of the dependent variables separately. Once the multivariate regression was applied to the datasets, linear regression equations were generated to predict the behavior of each of the dependent variables based on the independent variable.
3.2. Alignment of Research Methodology
The rationale for the selection of multivariate regression analysis in this study was because the researcher intended to assess the impact of political corruption, one independent variable, on foreign policy, which, as later delineated, was represented by a multitude of dependent variables. This implementation of a multivariate regression methodology draws on similar methodologies utilized in much of the existing scholarly literature in the field seeking to analyze trends related to corruption. For instance, the foundational source by Professor Arvind Jain employed regression analysis of the independent variable of corruption levels against quantitative economic, social, and political measures in a country (Jain, 2001). Jain effectively applied regressions to highlight the widespread consequences corruption poses domestically. Similarly, Professor Axel Dreher and Thomas Herzfeld utilized multivariate regression of corruption levels against macroeconomic measures in their study to successfully gauge the negative influence of corruption on the economic growth of a country (Dreher & Herzfeld, 2015). Foundational authors Hanne Fjelde and Havard Hegre also used a multivariate regression model of analysis, comparing corruption levels with variables measuring democracy and autocracy to reveal the connection between corruption and political instability (Fjelde & Hegre, 2014). Hence, scholarly work on the impacts of corruption mainly draws upon multivariate regression analysis.
3.3. Regression Model
Results from the linear regression were represented by Equation (1).
(1)
β0: intercept;
β1: slope of regression line (β1 > 0 indicates positive association; β1 < 0 indicates negative association);
Xi: independent variable;
Yi: dependent variable.
After the linear regressions were completed, the p-value was recorded. P-value indicates the statistical significance of relationships between the independent and dependent variables. If p < 0.05, a significant relationship exists between variables, indicating the independent variable explains variations in the dependent variable. This provides sufficient evidence to reject the null hypothesis of there being no relationship between the variables. However, if the p > 0.05, then a statistically significant relationship cannot be determined, indicating that the null hypothesis is not ruled out and that observed variations in the variables may be due to chance.
Then, the r-value, or the correlation coefficient, was recorded. R-value ranges from −1 to 1 and indicates the strength of the linear relationship between variables. A value close to 1 indicates a strong positive correlation, and a value close to −1 indicates a strong negative correlation, while a value of 0 indicates no correlation (Asuero et al., 2006).
3.4. Procedure
Given that South Asia was the focal region of analysis, quantitative data was derived from the following 6 countries in this region: India, Pakistan, Bangladesh, Afghanistan, Nepal, Sri Lanka. It is necessary to acknowledge that although Bhutan and the Maldives are considered part of South Asia, they were excluded from the sample. This is because these countries have very low populations compared to other South Asian nations and have tourism-based economies. These inherent differences with the rest of the region caused less data to be available regarding corruption and foreign policy measures, causing these countries to be excluded.
Data on the independent variable of political corruption levels for South Asia was gathered first. The Corruption Perceptions Index (CPI) report published by Transparency International was utilized to source this data (see Appendix A). CPI corruption levels for a country range from 0 (highly corrupt) to 100 (not corrupt). It is important to recognize that a higher CPI level signifies less perceived corruption. The utilization of the CPI index for corruption data is a standard in political science research, as this index is the most widely used and credible measure of corruption. A major advantage of the CPI is that it enables the quantification of the rather theoretical issue of corruption.
The dependent variable was foreign policy in South Asian nations. Referring back to its definition, Professor Steve Smith refers to foreign policy as the strategies and actions employed by a state in its international affairs. He defines it as a complex, multifaceted field encompassing economic, diplomatic, and militaristic objectives of states (Smith, 1986). Hence, it is evident that foreign policy cannot be quantified by just one dependent variable, given its broad, comprehensive nature. To work around this inherent complexity, the researcher broke foreign policy down into the 3 distinct spheres outlined by Professor Smith: economic, diplomatic, and militaristic dimensions. 3 dependent variables were designated into each dimension, resulting in a total of 9 variables quantifying multidimensional foreign policy, as displayed in Table 1. This allowed the researcher to ensure that all aspects of foreign policy were adequately studied in relation to corruption.
Table 1. Categorization of dependent variables: multidimensional foreign policy.
Dimension |
Economic |
Diplomatic |
Militaristic |
Dependent Variables |
International Trade |
International Organization |
Armed Conflict |
Foreign Investment |
Diplomatic Visits |
Global Militarisation |
Foreign Aid |
Diplomatic Influence |
Geopolitical Security |
Table 2. Datasets utilized for economic variable.
Economic Dimension of Foreign Policy |
Dependent Variables |
International Trade |
Foreign Investment |
Foreign Aid |
Measures Recorded |
Imports
(% of GDP) |
Exports
(% of GDP) |
Trade Balance
(% of GDP) |
Inward FDI
(% of GDP) |
Outward FDI
(% of GDP) |
Net Aid Received (% of GNI) |
Dataset & Database |
World Integrated Trade Solution by World Bank (see Appendix B) |
FDI Flows and Stock by UNCTADSTAT (see Appendix C) |
Net Official Development Assistance and Official Aid Received by World Bank (see Appendix D) |
For each of the 6 South Asian countries, data for political corruption, as well as the 9 dependent variables measuring foreign policy, was recorded for the time period 2017-2022 in Google Sheets. The datasets utilized to source this data are outlined in Tables 2-4.
Table 3. Datasets utilized for diplomatic variables.
Diplomatic Dimension of Foreign Policy |
Dependent Variables |
International Organization |
Diplomatic Visits |
Diplomatic
Influence |
Measures Recorded |
# of International Organizations |
# of Outgoing Diplomatic Visits |
# of Receiving Diplomatic Visits |
Diplomatic
Influence Score |
Dataset & Database |
International Organization Participation Report by The World Factbook - CIA (see Appendix E) |
Diplomatic Exchange by Correlates of War (see Appendix F) |
Asia Power Index by Lowy Institute (see Appendix G) |
Table 4. Datasets utilized for militaristic variables.
Militaristic Dimension of Foreign Policy |
Dependent Variables |
Armed Conflict |
Global Militarisation |
Geopolitical Security |
Measures Recorded |
# of Armed Conflicts |
Global Militarisation Index Score |
Security Score |
Dataset & Database |
Armed Conflict
1946-2023 by Uppsala
Conflict Data Program (see Appendix H) |
Global Militarisation Index by Bonn International
Centre for Conflict Studies (see Appendix I) |
Asia Power Index by Lowy Institute (see Appendix J) |
After data was collected, the multivariate regression analysis began. Separate linear regressions were performed for political corruption and each of the 9 dependent variables. Values for the resulting regression equations, along with p-values and r-values, were recorded. This enabled the researcher to analyze trends on which dimensions of foreign policy were most affected by varying levels of political corruption in South Asia. An intricate examination of distinct components of foreign policy allowed a nuanced conclusion to be formed on the international implications of political corruption.
4. Results
Quantitative data on the independent variable of political corruption values for each South Asian nation from 2017-2022 are displayed in Table 5. A key trend that emerged is that throughout all years studied, India consistently remained the least corrupt South Asian nation, marked by the highest CPI score. Afghanistan consistently remained the most corrupt country, having the lowest CPI score. The order from least corrupt to most corrupt for South Asia was as follows: India, Sri Lanka, Nepal, Pakistan, Bangladesh, and Afghanistan.
Table 5. Political corruption levels in South Asian countries from 2017-2022.
Country |
Political Corruption (CPI) |
Country |
Political Corruption (CPI) |
2022 |
|
2021 |
|
India |
40 |
India |
40 |
Pakistan |
27 |
Pakistan |
28 |
Bangladesh |
25 |
Bangladesh |
26 |
Afghanistan |
24 |
Afghanistan |
16 |
Nepal |
34 |
Nepal |
33 |
Sri Lanka |
36 |
Sri Lanka |
37 |
2020 |
|
2019 |
|
India |
40 |
India |
41 |
Pakistan |
31 |
Pakistan |
32 |
Bangladesh |
26 |
Bangladesh |
26 |
Afghanistan |
19 |
Afghanistan |
16 |
Nepal |
33 |
Nepal |
34 |
Sri Lanka |
38 |
Sri Lanka |
38 |
2018 |
|
2017 |
|
India |
41 |
India |
40 |
Pakistan |
33 |
Pakistan |
32 |
Bangladesh |
26 |
Bangladesh |
28 |
Afghanistan |
16 |
Afghanistan |
15 |
Nepal |
31 |
Nepal |
31 |
Sri Lanka |
38 |
Sri Lanka |
38 |
Quantitative data collected on the dependent variables measuring the economic, diplomatic, and militaristic dimensions of foreign policy in South Asia from 2017-2022 are displayed in Tables J1-J3, respectively (see Appendix J). The results of the linear regression analysis of political corruption levels against the economic, diplomatic, and militaristic dimensions of foreign policy are displayed below in Tables 6-8, respectively.
Table 6. Regression analysis results for economic dimension of foreign policy.
Economic Variable |
International Trade |
Foreign Investment |
Foreign Aid |
Measure |
Imports
(% of GDP) |
Exports
(% of GDP) |
Trade Balance
(% of GDP) |
Inward FDI
(% of GDP) |
Outward FDI (% of GDP) |
Net Aid Received (% of GNI)* |
r |
−0.3780 |
0.6277 |
0.5503 |
0.6810 |
0.3021 |
−0.8146 |
p < 0.05 |
0.023, Yes |
<0.0001, Yes |
0.0005, Yes |
0.0001, Yes |
0.0734, No |
<0.0001, Yes |
β1 |
−0.5109 |
0.4602 |
0.9332 |
0.04944 |
0.006668 |
−0.8719 |
β0 |
43.67 |
−1.453 |
−44.04 |
−0.7109 |
−0.08541 |
31.77 |
Note. *For data on Net Aid Received (% of GNI), a noteworthy limitation skewed the data: conflict surrounding Taliban takeover of Afghanistan in 2021.
Table 7. Regression analysis results for diplomatic dimension of foreign policy.
Dependent Variables |
International
Organization |
Diplomatic Visits |
Diplomatic
Influence |
Measure |
# of International Organizations |
Outgoing
Diplomatic Visits |
Receiving
Diplomatic Visits |
Diplomatic
Influence Score |
r |
0.6829 |
0.5301 |
0.5620 |
0.6645 |
p < 0.05 |
<0.0001, Yes |
0.0077, Yes |
0.0043, Yes |
<0.0001, Yes |
β1 |
0.8748 |
2.15 |
2.996 |
1.622 |
β0 |
36.05 |
−1.018 |
−30.24 |
−20.65 |
Table 8. Regression analysis results for militaristic dimension of foreign policy.
Dependent Variables |
Armed Conflicts |
Global Militarisation |
Geopolitical Security |
Measure |
# of Armed Conflicts |
Global Militarisation
Index Score |
Security Score |
r |
−0.2736 |
0.1880 |
0.6598 |
p < 0.05 |
0.1064, No |
0.3197, No |
<0.0001, Yes |
β1 |
−0.04756 |
0.6111 |
1.919 |
β0 |
5.159 |
85.86 |
−29.04 |
Results of the regression analysis provided that political corruption had statistically significant relationships (p < 0.05) with all economic measures of foreign policy besides Outward FDI. Before reporting the individual trends for each variable, it is critical to reiterate that a higher CPI value denotes less corruption in a country.
Assessing the first economic variable of International Trade, a weak negative correlation (r = −0.3780) was found between lower political corruption level and Imports, while a moderate positive correlation (r = 0.6277) was found between lower political corruption level and Exports. Trade Balance, a simple calculation of Imports minus Exports, also displayed a moderate positive correlation (r = 0.5503) with lower corruption.
For the second economic variable, Foreign Direct Investment, lower political corruption had a moderate positive correlation (r = 0.6810) with Inward FDI, but no significant relationship was determined between corruption and Outward FDI.
In terms of the third economic variable, Foreign Aid, lower political corruption exhibited a strong negative correlation (−0.8146) with Net Aid Received.
Political corruption had statistically significant relationships with all three diplomatic variables outlining foreign policy. Moderate positive correlations were determined between lower political corruption level and # of International Organizations (r = 0.6829), both Outgoing and Receiving Diplomatic Visits (r = 0.5301 and r = 0.5620), and Diplomatic Influence Score (r = 0.6645).
Regression results revealed that political corruption did not have statistically significant relationships with two of the three militaristic variables of foreign policy, namely Armed Conflicts and Global Militarisation.
Corruption was seen, though, to have a significant relationship with Geopolitical Security, as there was a moderate positive correlation (r = 0.6598) between lower political corruption and Security Score.
5. Discussion & Conclusion
The goal of this study was to determine how political corruption was linked to the multidimensional phenomenon of foreign policy in South Asian countries. Prior to conducting the regression analysis, the researcher expected in the form of a three-part hypothesis that increased levels of political corruption would be associated with disadvantages in the economic relationships, diplomatic conduct, and military stability of South Asian nations. From the results of this investigation, it is safe to deduce that increased political corruption was correlated with impediments in the economic and diplomatic dimensions of foreign policy, validating the first two suppositions of the initial hypothesis. However, in large, military affairs of the studied nations showed no significant relationships with political corruption, disproving the third component of the original three-part hypothesis.
5.1. Economic Dimension of Foreign Policy
Analyzing the results of the regression analysis, the economic dimension of foreign policy exhibited a notable link with corruption.
Firstly, the regression results indicated that the international trade variable was correlated with political corruption. The trends revealed that the more corrupt the nation, the greater it would import as a percentage of its GDP, while the less corrupt the nation, the greater it would export as a percentage of its GDP. Thus, less corrupt countries were able to exhibit more favorable trade balances. These trends are best explained by the fact that higher corruption leads to large inefficiencies in domestic production and resource allocation, compelling more corrupt countries to resort to foreign imports to meet demand (Jain, 2001). Also, as Professor Jain further elucidates in his foundational research, barriers to trade exist in corrupt regimes in the form of unfavorable tariffs, bribery and rent-seeking behavior, and regulatory business restrictions. Thus, countries with lower corruption were able to be more competitive in regards to foreign trade, justifying why less corruption leads to greater exports and more favorable trade balances. By enhancing their trading relationships on a global stage, these less corrupt nations are able to gain greater leverage in regards to their foreign policy goals and in turn foster more positive economic partnerships with firms in more developed countries. This can serve as a huge leap for a developing country, allowing it to employ its reputation of a non-corrupt business environment to build reliability and enhance its strategic positioning in the international economic landscape.
Analyzing Foreign Direct Investment, it was determined that less corruption in a country was linked to more FDI inflow; outward FDI results were not significant. This aligns with the understanding that less corrupt nations are often attractive investment locations for international firms. As Professor Elliott describes in her analysis, when a country is perceived as having low corruption, investors are more confident in the rule of law, fair competition, transparent business operations, and growth potential, making them more likely to invest (Elliott, 1997). Also, taking into consideration that South Asia is still a developing region, the firms in studied countries do not generally seek to put outward investment into foreign nations; rather, companies in these nations are more concerned with receiving investment from more developed regions to facilitate their own growth. It is likely for this reason that outward FDI did not produce statistically significant results.
In terms of Foreign Aid, the results indicated that more corruption was correlated with a country receiving greater net aid as a % of GNI. This may seem counterintuitive at first, due to the logical assumption that foreign aid donors are more likely to provide aid to nations with less corruption to ensure that their aid is not misused. However, upon further analysis, it becomes clear that this anomaly occurred because of one key limiting factor during the time period of this study: conflict surrounding the Taliban takeover of Afghanistan in 2021. Since the brutal overthrow of the Afghan government by the Taliban regime, billions of US$ of foreign humanitarian aid has been poured into Afghanistan (Bizhan, 2023). Acknowledging from the CPI trends that Afghanistan is also by far the most corrupt South Asian nation, this influx of foreign aid skewed the regression results, making it appear as if higher corruption actually influenced the ability of a country to receive more foreign aid. This should be noted as an inherent limitation of this regression analysis.
5.2. Diplomatic Dimension of Foreign Policy
Moving on, an examination of the regression analysis proves that political corruption also had a direct link with the diplomatic dimension of foreign policy.
Findings displayed that less corruption was associated with greater International Organization membership. Confirming initial speculation in the research by Professor Elliott, less corrupt regimes may be more willing to advance national interests by pursuing international cooperation (Elliott, 1997). Thus, in efforts to expand diplomatic networks, these countries may join more international organizations. This trend may also be attributed to the point that nations with lower corruption are seen as more trustworthy global partners, therefore giving these countries more opportunities to be a part of international alliances.
Also, it was seen that less corrupt nations had more Outgoing and Receiving Diplomatic Visits. Because lower corruption gives countries a more honorable reputation on the international stage, they are more likely to build diplomatic partnerships. Such nations may seek to pursue the formation of stronger strategic alliances, which involves the frequent sending and receiving of diplomats as well, serving as rationale for the observed trend.
Furthermore, results revealed that less corruption in South Asian countries was correlated with greater Diplomatic Influence. As the foundational work by Professor Jain revealed, less corrupt governments are stronger in terms of domestic economic development and political affairs (Jain, 2001). Hence, they are able to more effectively empower their nations to emerge as regional or global leaders. For instance, the study conducted by Professor Khan highlights how, through rapid internal development, India, the least corrupt nation in the region, cemented itself as a global superpower in the past decade (Khan, 2006). This exemplifies how less corrupt countries were able to attain greater diplomatic outreach and influence.
5.3. Militaristic Dimension of Foreign Policy
As established by the regression analysis, political corruption had a statistically insignificant relationship with both Armed Conflicts and Global Militarisation. This is presumably because these militaristic variables are more dependent on specific national threats like territorial disputes, internal and external wars, terrorism, or geopolitical rivalries. For South Asia, such violence is often attributed to the legacy of British colonialism. Modern day conflicts in the region include India-Pakistan border disputes, Pakistan-Afghanistan border disputes, Taliban rule, independence movements within various countries, and several other geopolitical disagreements. Hence, political corruption levels are likely not a significant determinant of military affairs in South Asia.
Yet, it must be pointed out that for the Geopolitical Security variable, less corrupt nations had higher security scores. It is plausible that this is because countries with lower corruption are better able to establish themselves as formidable powers by more efficiently investing in security infrastructure and forming military alliances. The assertion by Professor Jain on less corruption being more conducive to internal stability may also help explain why less corrupt countries were seen as more geopolitically secure (Jain, 2001).
Nevertheless, because two of the three militaristic variables measuring foreign policy produced insignificant results in the regression analysis, it can be safely determined that political corruption did not have nearly as close of a link with the militaristic dimension of foreign policy as it did with the economic and diplomatic dimensions.
5.4. Final Conclusion
After a nuanced analysis of the phenomenon of political corruption in relation to multidimensional foreign policy, the researcher concluded that political corruption in South Asian nations is linked with significant international economic hindrance and impediments to foreign diplomacy, but has no relationship with international military conduct.
6. Limitations
Despite the coherent analysis employed, there were some notable limitations to this study. Firstly, corruption levels in South Asian nations were based on the Corruption Perceptions Index (CPI). CPI data only signifies perceived corruption, not actual level of corruption. Although there is nothing intrinsically wrong with analyzing corruption based on its perception, researchers must acknowledge that because corruption is a criminal activity, numerous cases of corruption within governments often go unreported. As described by researcher Jonathan Rose from Queen’s University, quantifying the complex issue of corruption with the CPI is an imperfect research practice (Rose & Heywood, 2013). Additionally, the CPI utilizes a highly theoretical definition of corruption that focuses on only “the abuse of entrusted power for private gain” and is ill-suited for measuring the true impacts of dishonest governance (Lane, 2017). However, since the need to quantify perceived corruption levels persists as a standard throughout the political science field, the majority of the past literature assessing trends in corruption levels draws on CPI. This reiterates that although this index is not perfect, the CPI is still the most credible tool available to measure corruption.
Also, it is important to note that this study only aggregated data from 2017-2022. Hence, historical trends in the regional link between corruption and foreign policy were not analyzed. Because many South Asian countries have seen significant declines in corruption since their independence from British colonial rule, the examination of historical trends in corruption and foreign policy could potentially be noteworthy. However, this study instead centered on the contemporary trends in the detrimental influence of corruption on foreign policy, leaving out the historical perspective.
Additionally, it is also key to consider that this study only consisted of a sample size of 6 nations over the course of a 6-year period. Given the fact that there is a very limited number of nations in South Asia, coupled with the fact that most corruption and foreign policy data only exists for a brief, recent period, the lower sample size of data may have contributed to some inaccuracy. Still, the majority of the regressions yielded statistically significant results.
Furthermore, as discussed earlier, several conflicts in South Asia during the time period of analysis skewed foreign policy data. One of the most prominent examples was the Taliban takeover of Afghanistan, which caused billions of dollars of humanitarian aid to be sent into a corrupt nation, skewing regression data for the Foreign Aid variable. Similarly, the numerous regional border skirmishes and internal turmoil that occurred from 2017-2022 could also have potentially affected the validity of data used in the regression analysis.
In the end, it is imperative to recognize that this was a linear regression analysis study, meaning that causation between the independent variable and dependent variables cannot be found. Rather, the results only indicate correlational relationships between political corruption and measures of foreign policy. Therefore, this analysis does not prove that political corruption in South Asian countries directly restrains cooperative economic and diplomatic conduct while not impacting militaristic operations; it simply highlights the association that exists based on observed trends in the link between political corruption and foreign policy.
7. Implications
Nonetheless, this study still makes significant contributions to the field of political science. The findings of higher political corruption levels being associated with negative trends economic relationships and international diplomacy bring up several government policy recommendations for South Asian nations. For one, these regimes must internally crack down on bribery and rent-seeking behaviors associated with economic and diplomatic operations. This urges the possible need for stricter punishments for corrupt officials, anti-corruption incentives, and tighter legislation on corruption. The recommendation of a need for a more strict policy against political corruption is a recurring theme amongst foundational literature, aligning with suggestions for anti-corruption legislation made in the study by Professor Elliott (Elliott, 1997).
Also, it is imperative that the damaging trends associated with corruption must be brought under international security. When greater attention is drawn to the notion that political corruption is an issue that is destructive to economic and diplomatic relationships, more international cooperation will be fostered to address the issues posed by corruption. Joint global action to address corruption, in addition to domestic regulation within South Asian countries, is likely to be an effective solution. This is because, since corruption is an issue that has now been proven to transcend national borders, international cooperation is likely the best course of action to counteract this problem. Organizations like the United Nations Convention Against Corruption and Transparency International could take collaborative action in assisting individual countries in curbing corrupt governance.
8. Directions for Future Research
From this research, several instructions emerge for further study. A cross-regional analysis on the international effects of political corruption would be highly useful. In such a study, corruption trends in different regions would be assessed against one another in order to gauge whether or not the relationships with foreign policy emerging in South Asia are applicable to other areas of the globe, facilitating a deeper international understanding of this matter.
Also, future research should consider accounting for covariates. In this study, most correlations found between political corruption and the economic and diplomatic dimensions of foreign policy were moderate in strength. Adjusting the regression analysis to account for covariates that may also have an impact on multidimensional foreign policy, such as democracy levels or cultural ideologies, may allow research to discover stronger correlations between corruption and foreign policy.
Last but not least, future research is urged to explore different alliances as an aspect of foreign policy. Rather than categorizing foreign policy by its economic, diplomatic, and militaristic dimensions, researchers could employ an approach of analyzing how corruption impacts choices of nations to be more Western-aligned, anti-Western, or non-aligned. This would develop a further understanding of corruption in relation to an alliance-based perspective on foreign policy.
Appendix
Appendix A. Corruption Perceptions Index (CPI) by Transparency
International
Transparency International. (2022). 2022 Corruption Perceptions Index. Transparency.org. https://www.transparency.org/cpi/2022
The Corruption Perceptions Index (CPI) is an index published by Transparency International, a global organization dedicated to studying and counteracting corruption. This index ranks 180 countries and territories across the globe based on perceived levels of corruption. CPI scores range from 0 - 100, with a higher score indicating a greater perceived level of corruption within a country. This index serves as the gold standard in the political science field as the most widely-utilized measure for gauging trends in political corruption. CPI data is available from 1995-2023.
Appendix B. World Integrated Trade Solution (WITS) by World
Bank
World Bank. (2023, November 2). World Integrated Trade Solution (WITS). World Integrated Trade Solution (WITS) | Data on Export, Import, Tariff, NTM. https://wits.worldbank.org/
The World Integrated Trade Solution (WITS) is a dataset published by the World Bank that provides data on international economic relationships and trade. It tracks several key aspects of global economic integration, including import and export volumes, trade balances, international business exchanges, foreign direct investment, trade tariffs, and economic agreements. Data is displayed for over 180 countries from 1997-2021.
Appendix C. FDI Flows and Stock by UNCTADSTAT
United Nations. (2023, September 22). Foreign Direct Investment: Inward and Outward Flows and Stock, Annual. UNCTADSTAT Data Centre. https://unctadstat.unctad.org/datacentre/dataviewer/US.FdiFlowsStock
The FDI Flows and Stock is a dataset published by the United Nations Conference on Trade and Development Statistics (UNCTADSTAT). It breaks down economies in all countries of the world by region, level of development, and different economic organizations. Inward and outward foreign direct investment stock and flow data is displayed from 1990-2022.
Appendix D. Net Official Development Assistance and Official Aid
Received by World Bank
World Bank. (2022). NET Official Development Assistance and Official Aid Received. World Bank Open Data. https://data.worldbank.org/indicator/DT.ODA.ALLD.CD
The Net Official Development Assistance and Official Aid Received is a dataset published by the World Bank. It provides data on the amount of foreign aid and net development assistance received by over 170 countries across the world during the time period 1960-2022.
Appendix E. International Organization Participation by
The World Factbook, CIA
Central Intelligence Agency. (2023). International Organization Participation. The World Factbook. https://www.cia.gov/the-world-factbook/field/international-organization-participation/
The International Organization Participation dataset is published by the Central Intelligence Agency (CIA). It lists a total of 238 recognized countries and territories across the globe. All of the international organizations and formal alliances that each subject country is a member in or participates in to some degree are listed. Notes are made on whether some nations are simply observing states, candidates for membership, or are no longer participating.
Appendix F. Diplomatic Exchange by Correlates of War
Diplomatic Exchange. (2016). Correlates of War Datasets. Correlates of War. https://correlatesofwar.org/data-sets/
The Correlates of War (COW) Datasets provide an overview of historical global trends in political affairs. They include a quantitative breakdown of instances of geopolitical cooperation and diplomacy dating back to the 1940s. For each country, each instance of either diplomats being sent or received is recorded, along with the destination for diplomats and the year they were sent and received. This forms an extensive list that marks all instances of diplomatic interaction between nations.
Appendix G. Asia Power Index by Lowy Institute
Lowy Institute. (2023). Lowy Institute Asia Power Index 2023 Edition. Lowy Institute Asia Power Index. https://power.lowyinstitute.org/
The Lowy Institute Asia Power Index is a dataset published by Lowy Institute providing geopolitical information from 2018-2023 on 26 countries and territories in the Asia-Pacific region, including South Asian countries. It provides rankings of these nations based on indicators such as power, level of democracy, military strength, government expenditure, diplomatic relations, strength of allies, organization membership, and other key factors assessing the dynamics of countries in that region.
Appendix H. Armed Conflict 1946-2023 by Uppsala Conflict Data
Program
Davies, S., Pettersson T., & Öberg M. (2023). Organized Violence 1989-2022 and the Return of Conflicts Between States? Journal of Peace Research, 60(4). https://ucdp.uu.se/downloads/index.html#armedconflict
The Armed Conflict 1946-2023 dataset is published by the Uppsala Conflict Data Program. It includes all instances of armed conflict from the time period 1946-2022 in which at least one party was the government of a state. The nature of the armed conflict, as well as the year in which it took place, is recorded, formulating an extensive list encompassing all global armed conflicts in modern history.
Appendix I. Global Militarisation Index by Bonn International
Centre for Conflict Studies
Bonn International Centre for Conflict Studies. (2022). Global Militarization Index. GMI Map. https://gmi.bicc.de/
The Global Militarisation Index (GMI) is published by Bonn International Centre for Conflict Studies. It depicts worldwide militarisation based on factors like military and paramilitary forces in a country, heavy weapons available to countries armed forces, military expenditure, domestic medical care, and health expenditure. GMI scores range from 0 to 400, with a higher score demonstrating increased militarisation for a country. Data is displayed from 2000-2022.
Appendix J. Tables Displaying Data on Dependent Variables
Measuring Economic, Diplomatic, Militaristic
Dimensions of Foreign Policy
Table J1. Economic variables measuring foreign policies of South Asian countries from 2017-2022.
Country |
Imports
(% of GDP) |
Exports
(% of GDP) |
Trade Balance (% of GDP) |
Inward FDI (% of GDP) |
Outward FDI (% of GDP) |
Net Aid Received (% of GNI) |
2022 |
|
|
|
|
|
|
India |
26.4 |
22.8 |
−4.9 |
1.42 |
0.42 |
- |
Pakistan |
22.5 |
10.5 |
−13.4 |
0.41 |
0.41 |
- |
Bangladesh |
20.9 |
12.9 |
−10.2 |
0.8 |
0.01 |
- |
Afghanistan |
58.75 |
5.97 |
−44.45 |
0.0005 |
0 |
- |
Nepal |
42.6 |
6.8 |
−37.51 |
0.17 |
0 |
- |
Sri Lanka |
25 |
21.5 |
−8.09 |
1.25 |
0.02 |
- |
2021 |
|
|
|
|
|
|
India |
24.2 |
21.5 |
−2.7 |
1.4 |
0.54 |
0.10 |
Pakistan |
18 |
9.1 |
−8.9 |
0.63 |
0.07 |
0.80 |
Bangladesh |
17.1 |
10.7 |
−6.4 |
0.7 |
0.02 |
1.20 |
Afghanistan |
37.1 |
14.3 |
−22.8 |
0.14 |
0.21 |
32.40 |
Nepal |
37.9 |
5.1 |
−32.8 |
0.54 |
0 |
4.20 |
Sri Lanka |
24.3 |
16.9 |
−7.4 |
0.69 |
0.02 |
0.20 |
2020 |
|
|
|
|
|
|
India |
19.1 |
18.7 |
−0.4 |
2.4 |
0.42 |
0.10 |
Pakistan |
17.4 |
9.3 |
−8.1 |
0.7 |
−0.02 |
0.90 |
Bangladesh |
15.8 |
10.4 |
−5.4 |
0.69 |
0 |
1.40 |
Afghanistan |
36.3 |
10.4 |
−25.9 |
0.06 |
0.18 |
20.90 |
Nepal |
34.1 |
6.8 |
−27.3 |
0.38 |
0 |
5.20 |
Sri Lanka |
21.6 |
15.4 |
−6.2 |
0.54 |
0.02 |
0.30 |
2019 |
|
|
|
|
|
|
India |
21.2 |
18.7 |
−2.5 |
1.77 |
0.46 |
0.10 |
Pakistan |
19.5 |
9.4 |
−10.1 |
0.77 |
−0.03 |
0.60 |
Bangladesh |
18.5 |
13.1 |
−5.4 |
0.82 |
0.01 |
1.20 |
Afghanistan |
45.3 |
10.8 |
−34.5 |
0.12 |
0.14 |
21.70 |
Nepal |
41.5 |
7.8 |
−33.7 |
0.54 |
0 |
3.90 |
Sri Lanka |
27.6 |
21.8 |
−5.8 |
0.89 |
0.09 |
0.20 |
2018 |
|
|
|
|
|
|
India |
23.7 |
19.9 |
−3.8 |
1.53 |
0.41 |
0.10 |
Pakistan |
19 |
8.6 |
−10.4 |
0.54 |
−0.01 |
0.40 |
Bangladesh |
19.8 |
12.7 |
−7.1 |
1.14 |
0.01 |
0.90 |
Afghanistan |
40 |
4.8 |
−35.2 |
0.65 |
0.21 |
20.70 |
Nepal |
40.6 |
7.8 |
−32.8 |
0.21 |
0 |
4.40 |
Sri Lanka |
28.4 |
21.4 |
−7 |
1.83 |
0.08 |
−0.30 |
2017 |
|
|
|
|
|
|
India |
22 |
18.8 |
−3.2 |
1.52 |
0.42 |
0.10 |
Pakistan |
17.3 |
8.2 |
−9.1 |
0.74 |
0.02 |
0.70 |
Bangladesh |
17.2 |
12.8 |
−4.4 |
0.74 |
0.05 |
1.20 |
Afghanistan |
41.2 |
4.4 |
−36.8 |
0.27 |
0.06 |
20.10 |
Nepal |
36.8 |
7.8 |
−29 |
0.67 |
0 |
4.30 |
Sri Lanka |
26.9 |
20.2 |
−6.7 |
1.57 |
0.08 |
0.30 |
Note. Measures indicated with “-” had no data available for that given year. Thus, data from these years was omitted in the regression analysis calculations.
Table J2. Diplomatic variables measuring foreign policies of South Asian countries from 2017-2022.
Country |
# of International Organizations |
Outgoing Diplomatic Visits |
Receiving Diplomatic Visits |
Diplomatic Influence |
2022 |
|
|
|
|
India |
80 |
- |
- |
65.8 |
Pakistan |
70 |
- |
- |
29.3 |
Bangladesh |
62 |
- |
- |
30.1 |
Afghanistan |
50* |
- |
- |
14.5 |
Nepal |
55 |
- |
- |
15.5 |
Sri Lanka |
61 |
- |
- |
21.1 |
2021 |
|
|
|
|
India |
80 |
- |
- |
63.5 |
Pakistan |
70 |
- |
- |
33.8 |
Bangladesh |
62 |
- |
- |
31.5 |
Afghanistan |
50* |
- |
- |
11.3 |
Nepal |
55 |
- |
- |
18 |
Sri Lanka |
61 |
- |
- |
27.5 |
2020 |
|
|
|
|
India |
80 |
132 |
150 |
65.9 |
Pakistan |
70 |
86 |
79 |
36 |
Bangladesh |
62 |
57 |
46 |
27.4 |
Afghanistan |
50 |
44 |
35 |
10.3 |
Nepal |
55 |
31 |
26 |
15 |
Sri Lanka |
61 |
49 |
40 |
25.1 |
2019 |
|
|
|
|
India |
80 |
127 |
149 |
68.5 |
Pakistan |
70 |
86 |
79 |
32.2 |
Bangladesh |
62 |
56 |
46 |
26.4 |
Afghanistan |
50 |
44 |
34 |
6.4 |
Nepal |
55 |
31 |
26 |
12.8 |
Sri Lanka |
61 |
50 |
41 |
26.6 |
2018 |
|
|
|
|
India |
80 |
127 |
150 |
68.3 |
Pakistan |
70 |
86 |
79 |
30.7 |
Bangladesh |
62 |
56 |
45 |
25.5 |
Afghanistan |
50 |
43 |
34 |
6.2 |
Nepal |
55 |
31 |
26 |
12.3 |
Sri Lanka |
61 |
50 |
41 |
23.1 |
2017 |
|
|
|
|
India |
80 |
122 |
149 |
- |
Pakistan |
70 |
85 |
79 |
- |
Bangladesh |
62 |
56 |
45 |
- |
Afghanistan |
50 |
43 |
34 |
- |
Nepal |
55 |
31 |
26 |
- |
Sri Lanka |
61 |
50 |
41 |
- |
Note. *Afghanistan remained a member of 50 international organizations in 2021-2022, but representatives from the Taliban regime did not participate. Measures indicated with “-” had no data available for that given year. Thus, data from these years was omitted in the regression analysis calculations.
Table J3. Militaristic variables measuring foreign policies of South Asian countries from 2017-2022.
Country |
Armed Conflicts |
Global Militarization Index |
Geopolitical Security |
2022 |
|
|
|
India |
2 |
- |
74.7 |
Pakistan |
5 |
- |
11.7 |
Bangladesh |
4 |
- |
35.4 |
Afghanistan |
3 |
- |
11.5 |
Nepal |
3 |
- |
24.3 |
Sri Lanka |
3 |
- |
26.6 |
2021 |
|
|
|
India |
2 |
100 |
74.6 |
Pakistan |
6 |
134 |
11.4 |
Bangladesh |
3 |
68 |
35.5 |
Afghanistan |
4 |
86 |
8.1 |
Nepal |
3 |
75 |
24.5 |
Sri Lanka |
2 |
130 |
26.8 |
2020 |
|
|
|
India |
4 |
105 |
74.1 |
Pakistan |
6 |
137 |
10.8 |
Bangladesh |
3 |
70 |
35.6 |
Afghanistan |
4 |
116 |
7.5 |
Nepal |
2 |
78 |
24.6 |
Sri Lanka |
2 |
129 |
27 |
2019 |
|
|
|
India |
3 |
102 |
74.4 |
Pakistan |
7 |
142 |
9.9 |
Bangladesh |
3 |
70 |
36.2 |
Afghanistan |
4 |
115 |
7.2 |
Nepal |
3 |
78 |
25 |
Sri Lanka |
3 |
131 |
27.3 |
2018 |
|
|
|
India |
4 |
100 |
74.1 |
Pakistan |
7 |
139 |
10 |
Bangladesh |
3 |
67 |
36.3 |
Afghanistan |
4 |
113 |
6.3 |
Nepal |
3 |
82 |
25.1 |
Sri Lanka |
3 |
133 |
27.4 |
2017 |
|
|
|
India |
4 |
96 |
- |
Pakistan |
6 |
137 |
- |
Bangladesh |
4 |
66 |
- |
Afghanistan |
5 |
114 |
- |
Nepal |
3 |
89 |
- |
Sri Lanka |
3 |
138 |
- |
Note. Measures indicated with “-” had no data available for that given year. Thus, data from these years was omitted in the regression analysis calculations.