Abstract
Does World Bank aid to countries damaged by civil conflict meet its stated goals of speeding economic recovery and reducing the risk of conflict recidivism? We contend that the Bank’s success depends on its ability to bolster and signal the credibility of politicians’ commitments to peaceful politics and tailor its programs to the post-conflict environment. In the first systematic evaluation of World Bank post-conflict assistance, we estimate selection-corrected event history models of the effect of Bank programs on recovery and recurrence using an original dataset of all World Bank programs in post-conflict environments. Among key results, we find that the Bank tends to select aid recipients according to their pre-existing probability of conflict recurrence and that, once we control for this non-random selection, the Bank has no systematic effect on either conflict recurrence or economic recovery.
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Notes
The World Bank, of course, is not the only international organization devoting its energies to post-conflict situations. For a detailed account of the role the United Nations is playing in these societies, see Doyle and Sambanis (2006).
We use the word “anarchy” in the sense it is used in international relations, implying the dearth of central authority that individuals can appeal to for assistance (Lake 2007).
Keefer (2007) emphasizes that, even in the pre-conflict period, political leaders can spark violent insurgency when they under-supply public goods while over-supplying private and club goods, all due to a weak commitment to pursuing peaceful politics.
In the case of the IMF, Vreeland (2003) forwards a persuasive case that politicians advancing difficult reforms will leverage IMF assistance more for its policy conditions than the financial assistance itself.
The comparison may be quite apt, as the depth of reforms needed in the post-conflict period mirrors that of a structural adjustment program.
Stone (2002) makes a similar argument about IMF programs.
The implications of this point are worth considering more fully. The Bank has incentives that pull it in different directions. On the one hand, it wishes to achieve “peace” results, while, on the other, it seeks to impose economic reforms. To the extent that economic reform involves austerity that may undermine peace prospects, this raises the threshold for cases that can be safely selected. This point is more easily understood by conceptualizing three types of states that the Bank might aid: high-risk states that are likely to relapse into conflict no matter what is done; medium-risk states that might succeed if given aid but might fail if austerity measures are imposed; and low-risk states that are likely to succeed even with the Bank’s preferred reforms enacted. In this case, the Bank’s cross-cutting incentives might lead it to choose only low-risk states for its projects. We thank an anonymous reviewer for suggesting this point.
Available in the on-line Appendix.
A potential critique of this approach is that it ignores the “opportunity costs” of conflict. That is, in the absence of conflict, if the country had maintained its normal growth rate, its GDP per capita would have increased as well, which suggests that some counter-factual level should be the threshold for recovery. While we recognize this critique’s validity, we believe our definition’s advantages outweigh its potential disadvantages. Most importantly, our approach does not require us to speculate about the country’s counter-factual growth rate, which is particularly important because high levels of growth-rate volatility in the developing world make speculations about future growth paths tenuous at best (Pritchett 2000).
Flores and Nooruddin (2006) offer a fuller discussion of these coding choices.
See Box-Steffensmeier and Jones (2004) for an excellent treatment of event history models in political science.
The baseline hazard graph is available in the on-line Appendix.
We also estimated Cox non-parametric equations for each of the models presented in this text. The log-normal distribution generates the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) scores. And our results hold when we use the Cox model instead; see on-line Appendix.
In models not reported here but available in the on-line Appendix, we used the total commitment amount in millions of US$ and also its log as alternative measures. We prefer the per capita measure as it mirrors conventional practice in studies of international aid. Another possibility would be to use the actual amount disbursed from that committed. We prefer the commitment amount on theoretical grounds since it comes closer to approximating the signaling role played by a World Bank program. The commitment amount also is more comparable across programs since some more recent programs are still in progress and therefore have had fewer funds disbursed.
The PRIO/Uppsala data set includes a variable for whether the conflict had its roots in a territorial or secessionist issue or if it was for the control of the center. We code the variable ‘1’ if it had a territorial basis and ‘0’ otherwise.
Seminal work by Organski and Kugler (1977) on the ‘Phoenix effect’ after world wars leads us to suspect that longer wars might be easier to recover from since economies begin their recovery process earlier.
We control for all bilateral official development assistance (ODA) flowing into the country. The data are drawn from the OECD and are measured on a per capita basis. Due to earlier research by Collier and Hoeffler (1998) and Flores and Nooruddin (2006) that shows that the efficacy of aid is conditional on its timing, we define three time categories (1–3 years post-conflict, 4–6 years post-conflict, and 7 or more years post-conflict) and interact our time-varying aid per capita variable with these categories. The result is three variables (aid in the first time category, aid in the second, etc.), each of which is equal to bilateral aid in that year only if that year falls in that time category. This allows us to see if aid has a different effect depending on when it’s given.
This is measured as the difference between the country’s GDP per capita in the year the conflict episode ends and the year the conflict began.
We utilize a new data set on termination types assembled by PRIO/Uppsala, which codes different ways in which conflict episodes can end. We include dichotomous indicators for ‘outright military victories and formal peace agreements, with a reference category of informal cease-fires or cessations of violence without any explicit termination.
We include the counter for number of previous recoveries as a crude first cut for possible stratification (Beck et al. 1998).
There is some evidence of a non-linear effect but the inflection point is around 22 years, which is much longer than virtually all of the recovery episodes in our data set.
Quinn et al. (2007) report a similar finding.
Winship and Morgan (1999) offers an accessible and comprehensive review of techniques used to estimate causal effects from observational data.
That is, neither instrument affects the speed of recovery directly; rather their effect is through factors like World Bank aid.
Dreher et al. (2008) find that members of the UN Security Council receive more World Bank projects, even after accounting for economic and political factors, as well as regional and country effects. In a similar spirit, Dreher and Jensen (2007) find that affinity to the USA reduces the number of conditions in IMF structural adjustment loans; Kuziemko and Werker (2006) find that the USA uses its control of UNICEF to provide strategically important countries additional aid.
Alliance data are obtained through EUGene, a data management tool for creating data sets for use in international relations created by Scott Bennett and Allan Stam, and available on-line at www.eugenesoftware.org.
The French alliance indicator is a much stronger predictor of World Bank aid than is the UN-voting-based S-score. When we use each of these variables separately as an instrument, our results do not change. See on-line Appendix.
We perform 2000 replications of the model. To preserve space, we report only the Bias-corrected and accelerated confidence (BC a ) intervals, but the results do not change if we use either the percentile or the bias-corrected (without acceleration) confidence intervals. For more on bootstrapping and the different confidence intervals, see Efron and Tibshirani (1986, 1993), Mooney (1996), and Mooney and Duval (1993). We thank Chris Achen and Jan Box-Steffensmeier for answering questions about this strategy.
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Acknowledgements
For their comments and/or assistance, we thank: Christopher Achen, Daniel Blake, Carew Boulding, Janet Box-Steffensmeier, Clark Gibson, Erica Gould, Susan Hyde, Luke Keele, David Lake, Merriam Mashatt, Eddy Malesky, Porter McConnell, Gary Milante, Nita Rudra, Meg Shannon, Heidi Sherman, Joel Simmons, and two anonymous reviewers for this journal. We are grateful to Brooke Keebaugh and Yoon-ah Oh for their research assistance. Earlier versions of this paper benefited greatly from audience feedback at the International Studies Association meetings, Chicago, IL, March 2007; the Midwest Political Science Association meetings, Chicago, IL, April 2007; the UCSD Workshop on Building Peace in Fragile States, April 2007; the US Institute of Peace, Washington, DC, May 2007; the DC Area Workshop on Contentious Politics, hosted at the University of Maryland, May 2007; and the 2007 ECPR General Meeting in Pisa, Italy, September 2007. All errors are our own.
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Flores, T.E., Nooruddin, I. Financing the peace: Evaluating World Bank post-conflict assistance programs. Rev Int Organ 4, 1–27 (2009). https://doi.org/10.1007/s11558-008-9039-0
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DOI: https://doi.org/10.1007/s11558-008-9039-0