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The Effects of U.S. Foreign Assistance on Democracy Building, 1990–2003

Published online by Cambridge University Press:  13 June 2011

Steven E. Finkel ,
Aníbal Pérez-Liñán  and
Mitchell A. Seligson
Steven E. Finkel
Affiliation:
University of Pittsburgh, [email protected]
Aníbal Pérez-Liñán
Affiliation:
University of Pittsburgh, [email protected]
Mitchell A. Seligson
Affiliation:
Vanderbilt University, [email protected]
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Abstract

Democracy promotion has been an explicit doctrine of U.S. foreign policy since the end of the cold war. Between 1990 and 2003 resources for democracy programs increased by over 500 percent. Has this policy worked? Prior research has been inconclusive, relying either on case studies or on quantitative efforts that have not distinguished overall foreign assistance from democracy promotion. The authors answer this question using a new data set that includes program information for 165 countries for the years 1990–2003. The analysis distinguishes between direct and indirect causal mechanisms and employs a variety of statistical models that allow the authors to control for the unique democratization trend in each country when assessing causal effects, as well as for the potential endogeneity of U.S. democracy assistance. The analysis shows that democracy assistance does indeed have a significant impact.

Type
Research Article
Information
World Politics , Volume 59 , Issue 3 , April 2007 , pp. 404 - 439
Copyright
Copyright © Trustees of Princeton University 2007

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References

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22 Particularly controversial has been whether military interventions aimed to promote democracy can be successful. For qualified optimism, see Meernik, James, “United States Military Intervention and the Promotion of Democracy,” Journal of Peace Research 33 (November 1996)CrossRefGoogle Scholar; and Peceny, Mark, Democracy at the Point ofBayonets (University Park: Pennsylvania State University Press, 1999)Google Scholar. For a critical perspective, see Bueno de Mesquita, Bruce and Downs, George W., “Intervention and Democracy,” Intervention and Democracy, 60 (Summer 2006)Google Scholar; and Fukuyama (fn. 9).

23 USAID (fn. 8), 28. This document provides a thorough overview of recent programs, cooperative agreements, and “indefinite quantity contracts” (IQC) in the four sectors areas of the USAID portfolio.

24 Nelson, Joan M., Aid, Influence, and Foreign Policy (New York: Macmillan, 1968)Google Scholar; Walters, Robert S., American and Soviet Aid; A Comparative Analysis (Pittsburgh, Pa.: University of Pittsburgh Press, 1970)Google Scholar.

25 Carothers, Thomas, “The End of the Transition Paradigm,” in Carothers, Thomas, ed., Critical Mission: Essays on Democracy Promotion (Washington, D.C.: Carnegie Endowment for International Peace, 2004)Google Scholar.

26 On the ensuing debate around the “right” sequence, see Carothers, Thomas, “How Democracies Emerge: The ‘Sequencing’ Fallacy,” How Democracies Emerge: The 'Sequencing' Fallacy, 18 (January 2007)Google Scholar.

27 For summaries of those projects, see http://www.usaid.gov/our_work/democracy_and_ governance (accessed June 2007).

28 Karl, Terry Lynn, The Paradox ofPlenty: Oil Booms and Petro-States (Berkeley: University of California Press, 1997)Google Scholar; Knack (fn. 2).

29 Knack (fn. 2), 259. The article provides no citation for the statement, and we have not found evidence for such an explicit policy in USAID documents. Rather, the agency's self-described intent is to “target democracy dollars to maximize impact,” with “each country's unique history and political evolution definfing] opportunities and obstacles in the transition to democracy”; USAID—Office of Democracy and Governance, Strategic Assessment, http://www.usaid.gov/our_work/democracy_and_ governance/technical_areas/dg_office/assess.html (accessed February 2007).

30 Countries were included in the analysis if they met any of the following criteria: (1) they were recipients of USAID funds during 1990–2003; (2) they were classified by the World Bank as low or middle income; (3) they had an average Freedom House score equal to or greater than 3 (using the original untransformed scale) over the period 1972–2003; or (4) they were newly independent countries (that is, created after 1990). In total, 165 countries met at least one of the criteria.

31 The database was compiled by John Richter and Andrew Green at USAID. We are indebted to Andrew Green for his advice on how to aggregatefiguresfor different subsectors.

32 In previous research, two other methods of standardization have been tested: aid as percentage of GDP and aid per capita. See Knack (fn. 2); Paxton and Morishima (fn. 13); Burnside, Craig and Dollar, David, “Aid, Policies, and Growth,” Aid, Policies, and Growth, 90 (September 2000)Google Scholar. The former method assumes that aid would have larger effects in smaller economies, the latter that aid would have larger effects in countries with smaller populations. We explore each of these possibilities in the analyses that follow.

33 Our series on democracy assistance are based on the information presented in uSAID's Congressional BudgetJustification, which reports actual appropriations for the previous year. However, the Green-Richter database improves on the CBJ series in at least three ways: (1) to the extent that it was possible, the USAID team added to the country totals all funding from regional programs or centralized mechanisms that was determined to have been allocated for activities in the specific countries; (2) the team classified the allocation of funding at the subsectoral (for example, Rule of Law) and the sub-subsectoral (for example, Human Rights) levels; and (3) they occasionally adjusted the administrative classification of funds when the nature of activities was better reflected by a different label (for example, a civic education activity financed through the Rule of Law budget may have been reclassified as funding for Civil Society). We computed two-year means for all series because appropriations in one year are sometimes obligated and/or expended the following year.

34 The original Freedom House indices (measuring Political Rights and Civil Liberties) range from 1 to 7, with 7 being the least democratic outcome. Following the conventional procedure, we added the two scores, subtracted one point so that the scale would range between 1 and 13, and inverted the scores so that highest values correspond to the most democratic cases. The Polity index is based on several ordinal scales reflecting the competitiveness and openness of executive recruitment, the competitiveness and regulation of political participation, and the constraints on the chief executive; Marshall, Monty G. and Jaggers, Keith, Polity IV Project: Dataset Users' Manual (College Park: University of Maryland, 2002)Google Scholar.

35 For details on the items included in the factor analysis, see Appendix 2. The sources for the items were Freedom House, Freedom in the World Country Ratings, 1972 through 2004, www.freedomhouse.org (accessed May 2005); Tatu Vanhanen, Measures ofDemocracy 1810–2002, http://www.fsd.uta.fi/ english/data/catalogue/FSD1289/ (accessed June 2004); David L. Cingranelli and David L. Richards, am Human Rights Project, www.humanrightsdata.com (accessed June 2004); Monty G. Marshall, Keith Jaggers, and Ted Robert Gurr, Polity IV Project: Political Regime Characteristics and Transitions, 1800–2003, http://www.cidcm.umd.edu/inscr/polity/ (accessed April 2005).

36 Cingranelli and Richards (fn. 35); Mark Gibney, Political Terror Scale, www.unca.edu/ politicalscience/faculty-staff/gibney.html (accessed July 2004).

37 Minorities at Risk Project, MAR Data, www.cidcm.umd.edu/inscr/mar/ (accessed July 2004); Green, Andrew T., Trends in Post-Communist Civil Societies: Nonprofits, Unions, and Information Legislation, 1991–2001 (Washington, D.C.: USAID/DCHA, Office of Democracy and Governance, 2004)Google Scholar; Cingranelli and Richards (fn. 35).

38 We employed the interval and ordinal measures of press freedom created by Freedom House, plus the indicators created by Minorities at Risk (fn. 37), and Cingranelli and Richards (fn. 35). See Freedom House, Freedom of the Press 2004: Survey Methodology, www.freedomhouse.org (accessed May 2004).

39 Daniel Kaufmann, Aart Kraay, and Massimo Mastruzzi, Governance Matters IV: Governance In-dicators for 1996–2004, http://www.worldbank.org/wbi/governance/pubs/govmatters4.html (accessed June 2005).

40 Raudenbush, Stephen W. and Bryk, Anthony S., Hierarchical Linear Models: Applications and Data Analysis Methods (Thousand Oaks, Calif.: Sage Publications, 2002)Google Scholar; Singer, Judith D. and Willett, John B., Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence (Oxford: Oxford University Press, 2003)CrossRefGoogle Scholar.

41 Quadratic and cubic terms were tested as well, with their effects being insignificant.

42 We estimated the models using HLM 6.0 and SPSS 13.0. In the latter's MIXED module, we specified the error term structure to be (ARHI) in order to model both the heteroskedastic and autocor-related nature of the disturbances. The Polity IV model, though, attained the best fit without the heteroskedasticity option (an autocorrelation-only specification).

43 The World Bank, World Development Indicators On-Line, http://devdata.worldbank.org/dataon-line/ (accessed February 2005).

44 The formula employed to compute the spatial lags is

where X is the diffusion measure, FH is the Freedom House score, d is the distance between capitals, t indicates the year, i is the country in question, J is the set of all other countries, and jЄJ.

45 OECD, CRS Database (International Development Statistics CD-ROM: OECD, 2005)Google Scholar. Projects were coded as democracy related when the OECD activity database indicated code PDDG>0 or when the five-digit CRS purpose code was between 15020 and 15065. (The two criteria proved to be highly consistent.) Figures are in constant 1995 dollars.

46 Arthur Banks, Cross-National Time-Series Data (CNTS), www.databanks.sitehosting.net/index.htm (accessed July 2004).

47 Political Instability Task Force, Internal Wars and Failures of Governance, 1955–2005, http://glo-balpolicy.gmu.edu/pitf/pitfpset.htm (accessed June 2006). Earlier models also included inflation, unemployment, exports, urbanization, and British colonial experience as predictors. These variables were statistically irrelevant in every model estimated and so we dropped them.

48 Measured as the average of the Annett and the Fearon indices of fractionalization, both ranging between 0 (ethnic homogeneity) and 1 (extreme fractionalization). See Annett, Anthony, “Social Fractionalization, Political Instability and the Size of Government,” IMF Staff Papers 48, no. 3 (2001)Google Scholar; Fearon, James D., “Ethnic and Cultural Diversity by Country,” Ethnic and Cultural Diversity by Country, 8 (June 2003)Google Scholar; Fearon, James D. and Laitin, David D., “Ethnicity, Insurgency and Civil War,” Ethnicity, Insurgency and Civil War, 97 (February 2003)Google Scholar.

49 Measured as the share of income received by the top 20 percent of the population; World Bank (fn. 43).

50 Several sources contained missing data. Listwise deletion resulted in a poor solution because it reduced the geographic coverage of the analysis. Whenever possible, we used alternative sources of information to estimate missing data (for example, Penn World Tables to impute World Bank figures). In other cases, we used an expectation-maximization (EM) imputation procedure. See Allison, Paul D., Missing Data (Thousand Oaks, Calif: Sage Quantitative Applications in the Social Sciences, no. 136, 2001)Google Scholar; McLachlan, Geoffrey J. and Krishnan, Thriyambakam, The EM Algorithm and Extensions (New York: Wiley, 1997)Google Scholar; King, Gary, Honaker, James, Joseph, Anne, and Scheve, Kenneth, “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation,” Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation, 95 (March 2001)Google Scholar. Imputation models are available upon request.

51 The OECD data on U.S. democracy assistance, for example, has a correlation with the Green-Richter DG variable of only.62, suggesting that the OECD measure has a considerable amount of measurement error. We therefore make no substantive claim that only U.S. democracy assistance matters in promoting democratic outcomes in recipient countries.

52 The “average” country in this context means countries with mean levels of all covariates, including foreign assistance, time-varying covariates, and country-level predictors.

53 Controlling for all variables predicting the growth trajectory intercept and slope, there is still significant variation in those parameters in the overall sample of countries. This is shown in the statistically significant “random intercept” and “random slope” estimates at the bottom of the table. Including these random effects allows us to estimate the impact of DG aid and other time-varying factors, over and above each country's specific democratic trajectory.

54 An noted above, the hierarchical growth specification controls for selection biases related to the country's overall democratization trend and the predictable elements of that trend; the endogeneity model here goes further to control for the possibility USAID allocates funding based on the expected level of democracy in the immediate term.

55 Paxton and Morishima (fn. 13).

56 The fixed-effects model cannot estimate the impact of observed country-level stable attributes, as they are perfectly correlated with the unique component of the country intercept. Hence all fixed country-level variables, observed and unobserved, are controlled simultaneously but cannot be disentangled by this procedure.

57 This model was estimated using Stata 9.0's XTREGAR module, which includes first-order auto-correlated disturbances.

58 Beck, Nathaniel and Katz, Jonathan N., “Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon,” Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon, 55 (Spring 2001)Google Scholar.

59 The New York Times data were retrieved from the Lexis-Nexis database using the search protocol “[Country name] w/s secretary of state.”

60 Among the top ten recipients of State Department mentions are Iraq in 2003 (345 mentions, with a Freedom House score of 3), Israel in 1991 (169 mentions, with a FH score of 11), the Soviet Union in 1990 (100 mentions, with a FH score of 6), China in 1994 (86 mentions, a FH score of 1), and North Korea in 2003 (84 mentions and a FH score of 1). State Department mentions correlate at.22 with DG funding, at -.13 with FH scores, and at -.08 with Polity scores.

61 Lewbel, Arthur, “Constructing Instruments for Regressions with Measurement Error When No Additional Data Are Available, with an Application to Patents and R&D,” Constructing Instruments for Regressions with Measurement Error When No Additional Data Are Available, with an Application to Patents and R&D, 65 (September 1997)Google Scholar. For a recent application of this procedure, see Rudra, Nita, “Globalization and the Strengthening of Democracy in the Developing World,” Globalization and the Strengthening of Democracy in the Developing World, 49 (October 2005)Google Scholar.

62 A fixed-effects instrumental variables version of model 3.2 with identical instruments shows a statistically significant DG effect of.048.

63 Arellano, Manuel and Bond, Stephen, “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, 58 (April 1991)Google Scholar. For a good discussion of the Arellano-Bond and other GMM estimators in panel models, see Wawro, Gregory, “Estimating Dynamic Panel Models in Political Science,” Estimating Dynamic Panel Models in Political Science, 10 (Winter 2002)Google Scholar.

64 For ease of presentation, we do not show the estimated effects for each and every variable that was included in these models but focus instead on the effects of our primary variables of interest, DG obligations. The full results from these models are available on request.

65 The interpretation of cumulative effects of AID obligations is also reflected in the results of the lagged endogenous variable model of Table 3.3, whereby sustained AID obligations over time would have both short-term impacts on Freedom House scores of.018 per million dollars in any given year and a cumulative long-term or equilibrium effect of.054 (.018/(1-.669), where.669 is the coefficient for the lagged dependent variable). For good expositions on long-term effects in dynamic time series or panel models, see Suzanna De Boef and Luke Keele, “Revisiting Dynamic Specification” (Paper presented at the Society of Political Methodology, Florida State University, 2005); Kaplan, David, “Modeling Sustained Educational Change with Panel Data: The Case for Dynamic Multiplier Analysis,” Journal of Educational and Behavioral Statistics 27 (Summer 2002)CrossRefGoogle Scholar.

66 Logarithmic models are often used to test nonlinearities of this sort, but we chose the quadratic specification because of the large number of zero values for DG and other assistance variables (nearly half of the sample).

67 That is, a country with average DG expenditures as a proportion of DGP (.0005) is predicted to change.018 units on the Freedom House Scale, compared with a predicted change of.052 units for a country with average raw DG expenditures ($2.3 million).

68 Full tables with the effects of all independent variables are available upon request.

69 Brinks and Coppedge (fn. 5); Gleditsch (fn. 21); Pevehouse (fn. 5); Starr (fn. 21); Whitehead (fn.21).

70 See Bueno de Mesquita and Downs (fn. 22); Fukuyama (fn. 9).