Financing the peace: Evaluating World Bank post-conflict assistance programs

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.

This is a preview of subscription content, log in to check access.

Fig. 1

Notes

  1. 1.

    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).

  2. 2.

    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).

  3. 3.

    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.

  4. 4.

    Certainly, this concern is not unique to post-conflict government; a burgeoning literature in political economy provides evidence of the credibility problems faced by peaceful developing countries seeking to attract foreign direct investment (Jensen 2006; Henisz 2002).

  5. 5.

    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.

  6. 6.

    Our discussion of the Bank’s incentives are based primarily on Kapur et al. (1997), which provides a comprehensive history of the World Bank’s first half-century, and on Nielson et al. (2006), which offers a more recent analysis of organizational imperatives and reform movements at the World Bank.

  7. 7.

    The comparison may be quite apt, as the depth of reforms needed in the post-conflict period mirrors that of a structural adjustment program.

  8. 8.

    See Babson (2006), Boyce (2004, 2007), Forman and Salomons (1999), as well as the Bank’s own assessments (World Bank 2006b, c).

  9. 9.

    Stone (2002) makes a similar argument about IMF programs.

  10. 10.

    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.

  11. 11.

    Available in the on-line Appendix.

  12. 12.

    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).

  13. 13.

    Flores and Nooruddin (2006) offer a fuller discussion of these coding choices.

  14. 14.

    See Box-Steffensmeier and Jones (2004) for an excellent treatment of event history models in political science.

  15. 15.

    The baseline hazard graph is available in the on-line Appendix.

  16. 16.

    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.

  17. 17.

    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.

  18. 18.

    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.

  19. 19.

    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.

  20. 20.

    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.

  21. 21.

    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.

  22. 22.

    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.

  23. 23.

    A plausible alternative hypothesis is that countries with better property rights are more likely to recover quickly from conflict. We use the measure of contract-intensive money described by Clague et al. (1996, 1999) as a proxy for strength of property rights.

  24. 24.

    We include the counter for number of previous recoveries as a crude first cut for possible stratification (Beck et al. 1998).

  25. 25.

    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.

  26. 26.

    Quinn et al. (2007) report a similar finding.

  27. 27.

    Achen (1986) provides an excellent introduction to problems of non-random assignment. See Nooruddin (2002), Vreeland (2003), and Nooruddin and Simmons (2006) for treatments of selection bias in studies of the effectiveness of economic sanctions and IMF programs.

  28. 28.

    Winship and Morgan (1999) offers an accessible and comprehensive review of techniques used to estimate causal effects from observational data.

  29. 29.

    That is, neither instrument affects the speed of recovery directly; rather their effect is through factors like World Bank aid.

  30. 30.

    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.

  31. 31.

    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.

  32. 32.

    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.

  33. 33.

    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.

References

  1. Achen, C. (1986). Statistical analysis of quasi-experiments. Berkeley, CA: University of California Press.

    Google Scholar 

  2. Aizenman, J., & Marion, N. (1999). Volatility and investment: Interpreting evidence from developing countries. Economica, 66, 157–179.

    Article  Google Scholar 

  3. Babson, B. O. (2006). Visualizing a North Korean “Bold Switchover”: International financial institutions and economic development in the DPRK. Asia Policy, 2, 11–24, July.

    Google Scholar 

  4. Ball, N., & Halevy, T. (1996). Making peace work: The role of the international development community. Washington, DC: Overseas Development Council.

    Google Scholar 

  5. Bartels, L. M. (1991). Instrumental and ‘quasi-instrumental’ variables. American Journal of Political Science, 35, 777–800.

    Article  Google Scholar 

  6. Beck, N., Katz, J. N., & Tucker, R. (1998). Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science, 42(4), 1260–1288.

    Article  Google Scholar 

  7. Boehmke, F. J., Morey, D. S., & Shannon, M. (2006). Selection bias and continuous-time duration models: Consequences and a proposed solution. American Journal of Political Science, 50(1), 192–207.

    Article  Google Scholar 

  8. Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90(430), 443–450.

    Article  Google Scholar 

  9. Box-Steffensmeier, J., & Jones, B. (2004). Event history analysis. Cambridge University Press.

  10. Boyce, J. K. (2004). The international financial institutions: Postconflict reconstruction and peacebuilding capacities. Working paper, University of Massachusetts, Amherst.

  11. Boyce, J. K. (2007). Public finance, aid, and post-conflict recovery. Working paper 2007–09, University of Massachusetts, Amherst.

  12. Clague, C., Keefer, P., Knack, S., & Olson, M. (1996). Property and contract rights in autocracies and democracies. Journal of Economic Growth, 1(2), 243–276.

    Article  Google Scholar 

  13. Clague, C., Keefer, P., Knack, S., & Olson, M. (1999). Contract-intensive money: Contract enforcement, property rights, and economic performance. Journal of Economic Growth, 4(2), 185–211.

    Article  Google Scholar 

  14. Collier, P., & Hoeffler, A. (1998). On economic causes of civil war. Oxford Economic Papers, 50, 563–573.

    Article  Google Scholar 

  15. Collier, P., Elliott, V. L., Hegre, H., Hoeffler, A., Reynal-Querol, M., & Sambanis, N. (2003). Breaking the conflict trap: Civil war and development policy. Washington, DC: World Bank and Oxford University Press.

    Google Scholar 

  16. Dollar, D., & Svensson, J. (2000). What explains the success or failure of structural adjustment programmes? The Economic Journal, 110, 894–917.

    Article  Google Scholar 

  17. Doyle, M., & Sambanis, N. (2006). Making war and building peace: United Nations peace operations. Princeton, NJ: Princeton University Press.

    Google Scholar 

  18. Dreher, A., & Jensen, N. M. (2007). Independent actor or agent? An empirical analysis of the impact of US interests on IMF conditions. Journal of Law and Economics, 50(1), 105–124.

    Article  Google Scholar 

  19. Dreher, A., Sturm, J.-E., & Vreeland, J. R. (2008). Development aid and international politics: Does membership on the UN security council influence world Bank decisions? Journal of Development Economics (forthcoming).

  20. Efron, B., & Tibshirani, R. J. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science, 1, 54–77.

    Article  Google Scholar 

  21. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. London: Chapman and Hall.

    Google Scholar 

  22. Flores, T. E., & Nooruddin, I. (2006). Democracy under the gun: Understanding post-conflict economic recovery. Manuscript, University of Michigan and Ohio State University.

  23. Forman, S., & Salomons, D. (1999). Meeting essential needs in post-conflict recovery. Working paper, New York University Center on International Cooperation.

  24. Fortna, V. P. (2004). Peace time: Cease-fire agreements and the durability of peace. Princeton, NJ: Princeton University Press.

    Google Scholar 

  25. Gartzke, E. (1998). Kant we all get along?: Opportunity, willingness, and the origins of the democratic peace. American Journal of Political Science, 42(1), 1–27.

    Article  Google Scholar 

  26. Gartzke, E. (2000). Preferences and the democratic peace. International Studies Quarterly, 44(2), 191–210.

    Article  Google Scholar 

  27. Gartzke, E., & Jo, D.-J. (2002). United Nations general assembly voting, 1946–1996. Version 3.0. http://www.columbia.edu/~eg589/datasets.

  28. Greene, W. H. (2005). Econometric analysis, (5th ed.). Upper Saddle River, N.J.: Prentice Hall.

    Google Scholar 

  29. Heckman, J. J. (1997). Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources, 32(3), 441–462.

    Article  Google Scholar 

  30. Henisz, W. J. (2002). Politics and international investment. Cheltenham, UK: Edward Elgar.

    Google Scholar 

  31. Jensen, N. M. (2006). Nation-states and the multinational corporation: A political economy of foreign direct investment. Princeton, NJ: Princeton University Press.

    Google Scholar 

  32. Kapur, D., Lewis, J. P., & Webb, R. (1997). The World Bank: Its first half century. Washington, DC: Brookings Institution Press.

    Google Scholar 

  33. Keefer, P. (2007). Insurgency and credible commitment in autocracies and democracies. World Bank policy research working paper 4185, April.

  34. Kievelitz, U., Schaef, T., Leonhardt, M., Hahn, H., & Vorwerk, S. (2004). Practical guide to multilateral needs assessments in post-conflict situations. New York, NY, and Washington, DC: United Nations and World Bank.

    Google Scholar 

  35. Kreimer, A., Eriksson, J., Muscat, R., Arnold, M., & Scott, C. (1998). The World Bank’s experience with post-conflict reconstruction. Washington, DC: World Bank, Operations Evaluation Department.

    Google Scholar 

  36. Kuziemko, I., & Werker, E. D. (2006). How much is a seat on the security council worth? Foreign aid and bribery at the United Nations. Journal of Political Economy, 114(5), 905–930.

    Article  Google Scholar 

  37. Lake, D. A. (2007). Escape from the state of nature: Authority and hierarchy in world politics. International Security, 32(1), 47–79.

    Article  Google Scholar 

  38. Lefrancois, F. (2004). World Bank, IMF, and Armed conflicts and World Bank, IMF: Helping peace or creating conditions for war. Accessed at http://www.brettonwoodsproject.org on February 17, 2007.

  39. Licklider, R. (Ed.) (1993). Stopping the killing: How civil wars end. New York, London: New York University Press.

    Google Scholar 

  40. Licklider, R. (1995). The consequences of negotiated settlements in civil wars, 1945–1993. American Political Science Review, 89(3), 681–690.

    Article  Google Scholar 

  41. Mendelson-Forman, J., & Mashatt, M. (2007). Employment generation and economic development in stabilization and reconstruction operations. United States Institute of Peace, Stabilization and Reconstruction Series No. 6 (March). Available at http://www.usip.org/pubs/specialreports/srs/srs6.pdf.

  42. Mooney, C. Z. (1996). Bootstrap statistical inference: Examples and evaluations for political science. American Journal of Political Science, 40(2), 570–602.

    Article  Google Scholar 

  43. Mooney, C. Z., & Duval, R. D. (1993). Bootstrapping: A nonparametric approach to statistical inference. Sage Paper 07-095. Newbury Park, Calif.: Sage Publications.

    Google Scholar 

  44. Nielson, D., Tierney, M., & Weaver, C. (2006). Bridging the rationalist-constructivist divide: Re-engineering the culture of the world Bank. Journal of International Relations and Development, 9(2), 107–139.

    Article  Google Scholar 

  45. Nooruddin, I. (2002). Modeling selection bias in studies of sanctions efficacy. International Interactions, 28(1), 57–74.

    Article  Google Scholar 

  46. Nooruddin, I., & Simmons, J. W. (2006). The politics of hard choices: IMF programs and government spending. International Organization, 60(4), 1001–1033.

    Article  Google Scholar 

  47. Organski, A. F. K., & Kugler, J. (1977). The costs of major wars: The phoenix factor. American Political Science Review, 71(4), 1347–1366.

    Article  Google Scholar 

  48. Paris, R. (2004). At war’s end: Building peace after civil conflict. New York: Cambridge University Press.

    Google Scholar 

  49. Posen, B. (1993). The security dilemma in ethnic conflict. In Brown, M. (Ed.), Ethnic conflict and international security, (pp. 103–124). Princeton, NJ: Princeton University Press.

    Google Scholar 

  50. Pritchett, L. (2000). Understanding patterns of economic growth: Searching for hills among plateaus, mountains, and plains. World Bank Economic Review, 14(2), 221–250.

    Google Scholar 

  51. Quinn, J. J., & Simon, D. J. (2006). Plus ça change,...: The allocation of French ODA to Africa during and after the cold war. International Interactions, 32, 295–318.

    Article  Google Scholar 

  52. Quinn, J. M., Mason, T. D., & Gurses, M. (2007). Sustaining the peace: Determinants of civil war recurrence. International Interactions, 33, 167–193.

    Article  Google Scholar 

  53. Roberts-Schweitzer, E., Greaney, V., & Duer, K. (2006). Promoting social cohesion through education. Washington, DC: World Bank.

    Google Scholar 

  54. Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557–586.

    Article  Google Scholar 

  55. Stone, R. W. (2002). Lending credibility: The international monetary fund and the post-communist transition. Princeton, NJ: Princeton University Press.

    Google Scholar 

  56. Svensson, J. (2003). Why conditional aid doesn’t work and what can be done about it? Journal of Development Economics, 70, 381–402.

    Article  Google Scholar 

  57. Thyne, C. L. (2006). ABC’s, 123’s, and the golden rule: The pacifying effect of education on civil war, 1980-1999. International Studies Quarterly, 50, 733–754.

    Article  Google Scholar 

  58. Vreeland, J. R. (2003). The IMF and economic development. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  59. Walter, B. F. (1997). The critical barrier to civil war settlement. International Organization, 51(3), 335–364.

    Article  Google Scholar 

  60. Walter, B. F. (1999). Designing transitions from civil war: Demobilization, democratization, and commitments to peace. International Security, 24(1), 127–155.

    Article  Google Scholar 

  61. Walter, B. F. (2002). Committing to peace: The successful settlement of civil wars. Princeton, N.J.: Princeton University Press.

    Google Scholar 

  62. Walter, B. F. (2004). Does conflict beget conflict? Explaining recurring civil war. Journal of Peace Research, 41(3), 371–388.

    Article  Google Scholar 

  63. Weiss, M. A. (2004). World Bank post-conflict aid: Oversight issues for congress. Washington, DC: Congressional Research Service, Library of Congress.

    Google Scholar 

  64. Winship, C., & Morgan, S. L. (1999). The effects of causal effects from observational data. Annual Review of Sociology, 25, 659–706.

    Article  Google Scholar 

  65. World Bank (2003). Post-conflict fund: Annual report—fiscal year 2003. Washington, DC: World Bank.

    Google Scholar 

  66. World Bank (2004a). Post-conflict fund: Annual report—fiscal year 2004. Washington, DC: World Bank.

    Google Scholar 

  67. World Bank (2004b). Evaluation of the post-conflict fund, 2002. Washington, DC: World Bank, Independent Evaluation Group.

    Google Scholar 

  68. World Bank (2005a). Post-conflict fund: Annual report—fiscal year 2005. Washington, DC: World Bank.

    Google Scholar 

  69. World Bank (2005b). Reshaping the future: Education and postconflict reconstruction. Washington, DC: World Bank.

    Google Scholar 

  70. World Bank (2006a). World development indicators, 2006. CD-ROM.

  71. World Bank (2006b). Post-conflict fund and licus trust fund: Annual report—fiscal year 2006. Washington, DC: World Bank.

    Google Scholar 

  72. World Bank (2006c). Engaging with fragile states: An IEG review of World Bank support to low-income countries under stress. Washington, DC: World Bank Independent Evaluation Group.

    Google Scholar 

  73. World Bank (2006d). Information statement: International bank for reconstruction and development. Washington, DC: World Bank. Available at http://www.worldbank.org/debtsecurities/.

    Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Irfan Nooruddin.

Electronic Supplementary Material

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

Keywords

  • World Bank
  • Post-conflict
  • Economic recovery
  • Conflict recurrence

JEL Codes

  • O19
  • O22
  • P48