Introduction A Brief Scholarly Review Since the end of the Cold War, many scholars (Meernik, Krueger, and Poe 1998, Lancaster 2007, and Heckelman and Knack 2008, et al.) have recognized that foreign assistance has taken on a much greater emphasis for promoting economic development, as well as democracy. The stated goals and ideals of foreign aid are widely promoted, but they often may not reflect reality. The humanitarian research stresses the amount, types, and recipients of foreign aid. The OECD and others have collected much data showing the totals, distribution, and categories of foreign aid. Few studies, however, have attempted to measure the real-world results and effectiveness of foreign aid over the decades, as well as the statistical relationship between ODA and specific humanitarian/national interests; general totals and targets yes, but not the outcomes and socio-economic details. After trillions of ODA over the last several decades, it behooves us to assess the current recipients’ key socio-economic data and, then, determine whether or not humanitarian or national interests have prevailed. Baldwin (1966 and 1985), Hook (1995), and Lancaster (2007), et al. have argued that foreign aid can be driven by national interests, including security and power politics. David Baldwin’s Research Data and Methods The main research question is whether primarily humanitarian or national interests determine ODA. The dependent variable is Net ODA. The independent variables are socio-economic and military factors that may influence the total amount of ODA and each one in itself proffers a hypothesis. The fourteen independent variables are GNI (GDP) per capita, population size, GNI (GDP) total, literacy, education, life expectancy, infant mortality, and urbanization levels, HIV/AIDS rate, total arable land, total exports, total imports, external debt amount, and military expenditures as a percentage of GDP. Each of these variables is defined and given by DAC and the CIA (see Appendix for specific descriptions). This research project carries out OLS regression analysis to determine the strength of the relationship between Net ODA and each of the independent variables. It, then, uses multiple regression to test a number of grouped variables and all the variables together to determine if the overall analysis and results change according to the different combinations, let alone have any significance between each other (F significance value). Hypotheses and Findings
All fourteen independent variables together in a multiple regression model produce surprising changes in the overall results of the simple regression models. Six IVs (GNI per capita, population, literacy, education, life expectancy, and external debt) go from significant to not significant (though the external debt variable was very close to being significant). The six IVs that were not significant in the simple regression models (GNI, urbanization, HIV/AIDS, arable land, exports, and imports) remained insignificant in the multiple regression model. The infant mortality and military expenditures variables were the only two variables that were significant in both the simple and multiple regression models. What is notable is that the percentage of military expenditures per GDP variable overtakes the infant mortality variable as the predominant IV in this multiple regression model. The military expenditures variable is significant, with a 95% Confidence Interval, a T value of 2.99, and a P value of 0.004, which is an improvement over the simple regression model, while the infant mortality variable is reduced but is still significant. The overall model’s Adjusted R-squared is 0.1717 (R-squared is 0.2925), which is a significant increase over the simple regression figures, meaning that the fourteen independent variables together explain approximately 17% (29% with R-squared) of the DV variance in the model, which is reasonably significant when considering the overall picture and the fact that just two of the fourteen variables were significant in themselves. Conclusion In the end, the military expenditures variable was a surprise from an ODA perspective, in both the simple and multiple regression models. It is common for bilateral military assistance to be given, but for ODA to appear to be influenced heavily by military expenditures is a significant finding. The regression analysis on exports and imports with net ODA was another interesting finding that warrants further investigation, especially in terms of energy security. Finally, the external debt variable is an important finding. Donors may be sending ODA to recipients in order to have them pay back their debts to donors and donor financial institutions, let alone prevent the recipients from defaulting on donor loans. This significant relationship between ODA and the recipient’s external debt demands further research. If ODA is being determined significantly by a recipient’s debt to foreign creditors, then it may be the donor’s special financial interests and powers that are determining a large part of ODA, especially the amount and recipient. This would contradict the whole humanitarian grounds of foreign aid and suggest that donor financial interests and institutions are playing a very influential role in the disbursement of ODA, which would be another significant finding. Moreover, the dismal and most recent socio-economic statistics of many recipients after decades of foreign aid indicate that there may be other non-humanitarian factors involved. All in all, these findings suggest that national interests play a significant role in ODA and that there are ripe opportunities for further research in the foreign assistance area. Bibliography Baldwin, David A. (1966), ———. (1985), Central Intelligence Agency, CIA Heckelman, Jac C. and Stephen Knack ( 2008), “Foreign Aid and Market-Liberalizing Reform.”, Hook, Steven W. (1995), Lancaster, Carol (2007), Foreign Lumsdaine, David Halloran (1993), Meernik, James, Eric L. Krueger, and Steven C. Poe (1998), “Testing Models of U.S. Foreign Policy: Foreign Aid during and after the Cold War.” Organization for Economic Cooperation and Development, OECD and Development Assistance Committee Reports and Data, http://www.oecd.org (accessed February 16, 2010). Ruttan, Vernon W. (1996), Schraeder, Peter J., Steven W. Hook, and Bruce Taylor (1998) “Clarifying the Foreign Aid Puzzle: A Comparison of American, Japanese, French, and Swedish Aid Flows.” Wood, Robert E. (1986), From Please note that not all data is represented in this version of the paper. If additional details are desired, please contact the author directly. 151 ODA Recipients (2008)
Definitions/Measurements of Variables Using the OECD/DAC and the CIA All currency in U.S. Dollars Net ODA=In Millions. Total Official Development Assistance to a Recipient in 2008 (most Infant Mortality as the Most Significant Independent Variable
Multiple Regressions
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