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The Review of International Organizations

Erschienen in:

05.09.2016

Good countries or good projects? Comparing macro and micro correlates of World Bank and Asian Development Bank project performance

verfasst von: David Bulman, Walter Kolkma, Aart Kraay

Erschienen in: The Review of International Organizations | Ausgabe 3/2017

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Abstract

This paper examines the micro and macro correlates of aid project outcomes in a sample of 3797 World Bank projects and 1322 Asian Development Bank projects. We find that project outcomes vary much more within countries than between countries: only 10–25 % of the variation in project outcomes is between countries, and the rest is within countries. Among country-level macro variables, GDP growth and the policy environment are significantly positively correlated with project outcomes. Among project-level micro variables, shorter project duration and the presence of additional financing are significantly correlated with better project outcomes. In addition, the track record of the project manager in delivering successful projects is highly significantly correlated with project outcomes. We find few significant differences between the two institutions in the relationship between these variables and project outcomes.

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For related work on this issue in a very different context of aid projects in different communities in Pakistan, see Khwaja (2009).

World Bank projects can be assigned to multiple sectors, with an indication of the fraction of the project’s value in each sector. We follow DKK in assigning each project to its largest sector. In the ADB data we have information on only one sector per project, which is reflected in the table above.

The lack of PCR ratings prior to 1995 explains the high share of projects (48 %) in our sample that receive detailed Project Performance Evaluation Reports (PPERs). From 1995 onwards, 292 out of 980 ADB projects have PPERs, for a rate of 30 % which is comparable to the WB.

Average project success rates vary across sectors, and the sectoral composition of projects differs slightly between the ADB and the WB. However, these differences are sufficiently small that they do not account for much of the difference in overall project success rates between the two institutions. Assuming ADB sector success rates and applying the WB Asia sector distribution results in a success rate of 64.0 % compared to the ADB baseline of 64.5 %. Similarly, assuming the WB sector success rates and applying ADB sector distribution results in a success rate of 77.3 % compared to a WB Asia baseline of 78.2 %. The different treatment of sustainability issues in evaluations in the World Bank and ADB in the 2000s may also explain part of the difference. Sustainability sub-ratings are generally somewhat lower than overall ratings in the ADB.

We cut off the analysis in 2007 due to the more limited number of projects to analyze from 2008 onwards, which artificially drives the R-squareds higher.

Summary statistics on all variables used in the empirical analysis can be found in Appendix Table 1.

We obtain broadly similar results if we use a linear probability model (as in DKK), or if we estimate an ordered probit model separately for the WB and the ADB, so as to use the information in the 1–6 scale of the WB and the 1–4 scale of the ADB ratings.

As a further robustness check, we also estimated our specification for a sample that excluded ADB projects evaluated prior to 1995, in order to match the timing of the WB project sample. However, the results are very similar in this smaller sample, and so are not reported to conserve space.

Note that since every variable in the regression, including the unreported sector and period dummies, enters both directly and as an interaction with a dummy for ADB projects, this means that our specification is equivalent to estimating separate regressions for the two institutions. We estimate the pooled regression with interactions only to facilitate testing the null hypothesis of equal slopes across the two institutions.

The ADB produces a similar set of assessments, but only for poorer client countries eligible for concessional lending from the Asian Development Fund. In contrast, the WB produces its CPIA assessments for all clients, but discloses them publicly only for concessional borrowers. For concessional borrowers in Asia, the WB and ADB CPIA ratings are quite highly correlated.

It is important to emphasize that we are looking at planned project length, not actual project length. Complex projects may get restructured or cut off and then rated unsuccessful. As components get canceled, the actual project length may be quite short.

A potential concern here is that some of the difference between disbursements and commitments could reflect exchange rate fluctuations on loans denominated in currencies other than US dollars, that then are converted back to US dollars for recording purposes. To guard against this possibility, we also repeated our results, but dropping all log differences between disbursements and commitments that were less than 25 % in absolute value, so that the remaining “large” differences are more likely to be due to deliberate choices to scale down or scale up the project. Doing so has little effect on our results.

Interestingly, investment lending projects tend to receive lower ratings in Columns (1) and (2) which do not include project-level controls -- indicating that investment lending projects tend to have other characteristics such as longer planned length and longer implementation delays that are associated with worse performance. Once these attributes are held fixed, investment lending projects do better on average. Note that this sign reversal holds true only for WB projects. In Columns (1) and (2) the ADB interaction enters significantly positively, and with an estimated coefficient that is larger in absolute value than the direct effect. This means that, even without controlling for other project characteristics, investment lending projects do not receive lower ratings on average in the ADB.

Note that the figures in this sentence are not the inverses of the figures in the previous sentence. This is because the average across projects of years per project manager is not the same as the average across projects of project managers per year. Note also that our figure of average tenure of 1.9 years for the ADB, derived from 10 years of interim progress report data from 2000 onwards, is similar to 1.7 years found by an analysis of staff tenure data of 375 PVRs (IED 2015). This suggests that the limited availablility of pre-2000 data in our sample does not seriously affect our estimate of average turnover.

This difference should not be over-interpreted as a sign of greater candor on the part of WB project managers, as it simply is an unconditional probability. Controlling for the same set of project characteristics included in the regressions in Table 2, we do not find that the rate of unsatisfactory self-assessments in the first half of project life is significantly different between the two institutions.

Optimism bias is a well-known feature of project management (Flyvbjerg 2006; Siemiatycki 2010; Kolkma 1999).

All World Bank projects in our sample have first half ratings. Including the warning ratings reduces the ADB sample significantly while including the project manager track record variable reduces the sample size for both institutions. The regression results reported below focus only on a sample with observations for both variables. Running separate regressions for each increases the number of observations but does not significantly alter the results.

A potential concern here is that, despite the presence of country-level control variables in the regression, some project managers tend to work in “difficult” countries, and this in turn lowers the measured track record of the project manager. This however turns out not to be a factor -- the project manager track record variable remains strongly significant even when country fixed effects are included in the regressions.

It is worth noting that including the first half interim project outcome ratings helps to control for some of the omitted variable bias problems that clouded the interpretation of other project-level correlates discussed above. This is because this indicator captures information observable to the project manager at the time about likely deficiencies in the project that are hard to measure ex post. The fact that coefficients on most of the other project-level variables retain their size and significance is therefore encouraging that these potential omitted-variable biases may not be too severe.

Specifically, if \( {w}_i^j \) is the percent of the duration of project j for which i was the manager and PR _j is the rating for project j, then our default measure of the track record of project manager i is: \( {R}_i=\sum_j{w}_i^j*{PR}_j/\sum_j{w}_i^j \) and the track record of the project J management team is: \( {R}^J=\sum_i{w}_i^J*{R}_i \). Alternative #1 excludes from R _i all projects for which i was the last manager. Alternative #2 excludes the last project manager from the calculation of R ^J.

While such turnover can be beneficial for the project in question, it may not be beneficial for the entire portfolio of projects the ADB or the WB are implementing. This aggregate effect will depend on both (a) what happens to the other projects the incoming project manager is also working on -- it could be that the additional claims of the new project on the project manager’s attention come at the expense of the other projects in his or her portfolio, and (b) what happens to the outgoing project manager, who may instead be put in charge of other projects.

Chauvet, L., Collier, P. and Fuster, A. (2015). Supervision and project performance: A principal-agent approach. Working Papers DT/2015/04, DIAL (Developpement, Institutions et Mondialisation)

Denizer, C., Kaufmann, D., and Kraay, A. (2013). Good Countries or Good Projects? Macro and Micro Correlates of World Bank Project Outcomes. Journal of Development Economics, 105, 288–302

Dollar, D. and Levin, V. (2005). Sowing and reaping: institutional quality and project outcomes in developing countries, Policy Research Working Paper Series 3524, The World Bank.

Dollar, D., & Svensson, J. (2000). What Explains the Success and Failure of Structural Adjustment Programs? The Economic Journal, 110, 894–917.CrossRef

Flyvbjerg, B. (2006). From Nobel Prize to Project Management: Getting Risks Right. Project Management Journal, 37(3), 5–15.

Gelb, A. (2010). How can donors create incentives for results and flexibility for fragile states? A Proposal for IDA. Center for Global Development, Working Paper No. 227.

Geli, P., Aart, K. and Hoveida, N. (2014). Predicting Active World Bank Project Outcomes. World Bank Policy Research Working Paper No. 7001.

Guillaumont, P. and Laajaj, R. (2006). When Instability Increases the Effectiveness of Aid Projects. World Bank Policy Research Working Paper No. 4034.

IED Independent Evaluation Department (2015). 2015 annual evaluation review. Manila: Asian Development Bank.

Isham, J., & Kaufmann, D. (1999). The Forgotten Rationale For Policy Reform: The Productivity of Investment Projects. The Quarterly Journal of Economics, 114(1), 149–184.MIT PressCrossRef

Isham, J., Kaufmann, D. and Pritchett, L. (1997). Civil Liberties, Democracy, and the Performance of Government Projects, World Bank Economic Review, 11(2), 219–242.

Khwaja, A. I. (2009). Can Good Projects Succeed in Bad Communities?. Journal of Public Economics, 93, 899–916.

Kilby, C. (2000). Supervision and Performance: The Case of World Bank Projects. Journal of Development Economics, 62, 233–259.

Kilby, C. (2013). The Political Economy of Project Preparation: An Empirical Analysis of World Bank Projects. Journal of Development Economics., 105, 211–225.

Kilby, C. (2015). The Impact of World Bank Preparation on Project Outcomes. Journal of Development Economics, 115, 111–123.

Kolkma, W. (1999). Work in Progress: The Hidden Dimensions of Monitoring and Planning in Pakistan. West Lafayette: Purdue University Press.

Siemiatycki, M. (2010). Managing Optimism Biases in the Delivery of Large-Infrastructure Projects: A Corporate Performance Benchmarking Approach. European Journal of Transport and Infrastructure Research, 10(1), 30–41.

Smets, L., Knack, S., & Molenaers, N. (2013). Political Ideology, Quality at Entry and the Success of Economic Reform Programs. The Review of International Organizations, 8, 447–476.CrossRef

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Titel: Good countries or good projects? Comparing macro and micro correlates of World Bank and Asian Development Bank project performance
verfasst von: David Bulman
Walter Kolkma
Aart Kraay
Publikationsdatum: 05.09.2016
Verlag: Springer US
Erschienen in: The Review of International Organizations / Ausgabe 3/2017
Print ISSN: 1559-7431
Elektronische ISSN: 1559-744X
DOI: https://doi.org/10.1007/s11558-016-9256-x

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