a well‐developed methodology that is consistently applied for project economic analysis, or “high‐CBA” sectors. Regression results for this model are summarized in Table A.11. Model 2 consists only of high‐ CBA projects which account for over 90 percent of the projects that report an ERR in both the PAD and ICR. The default sector for the sector dummies is the transport sector.

Lj = the average rating for other projects previously led by the same task team leader.

## **III. Does EA matter to the success of a project, after controlling for country, project and team factors?**

Model III regressions are estimated using both logit (when project outcomes are rated as successful/unsuccessful) and OLS (when project outcomes are rated on a scale from 1 to 6). Models III (a) and (b) include two definitions of the quality of economic analysis (Aj=1 if ERR is reported in both the PAD and the ICR for a particular project and 0 otherwise; and Dj= the absolute difference between ERR in the PAD and ICR as defined above): Model (Logit) III a. Pj = f(Xj, Lj, Cj, Tj, Aj) Model (Logit) III b. Pj = f(Xj, Lj, Cj, Tj, Dj) Similarly, Models III (c) and (d) include the above two definitions of the quality of economic analysis, and the same functional form but using the OLS estimation method: Model (OLS) III c. Sj = f(Xj, Lj, Cj, Tj, Aj) Model (OLS) III d. Sj = f(Xj, Lj, Cj, Tj, Dj).

One shortcoming of the above measures is that they assume that a CBA is conducted for all investment projects (or IPFs); or that a CBA is the most appropriate form of economic analysis for all projects. This assumption also does not examine whether a CBA (and the ERR) has been done in a way that is most appropriate for a particular IPF. The indicator therefore does not account for those projects for which estimation of an ERR would not be the most appropriate form of economic analysis. This note attempts to partially adjust for this by distinguishing between the high CBA sectors and the low CBA sector projects (i.e. education, health, governance, etc.), by using dummy variables to categorize low‐CBA sectors as default sectors. The share of low CBA projects has been increasing gradually from 34 percent in the full sample to 45 percent in the sample that includes projects completed in 1995 and thereafter.

# 4. Data Description

Project data used for this study are taken from the Independent Evaluation Group (IEG) World Bank Project Performance Ratings data set, which covers 1975 to the present. The data set contains project variables on IEG evaluated/validated project outcome ratings, year of approval, exit fiscal year, net commitment, sector, and estimated ERRs reported in the PAD and ICR. Other project level variables such as preparation and supervision costs, TTLs’ identification numbers, potential problems, actual problems and restructuring of a project during operations are taken from the World Bank’s management information system (or Business Intelligence –BI‐ database). Data on GDP per capita and inflation are taken from World Development Indicators (WDI) for the respective years and were averaged over the period when each project was in operation. The World Bank’s Country Policy and Institutional Assessment (CPIA) is used as a proxy measure for the extent of governance or institutional development and is also averaged over the years in which the project was in operation.

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