GDP per capita at approval stood at 5,625 USD in PPP terms, and the average annual economic growth rate over the life of the project was 3.8%.

In the qualitative data set, selected as a stratified random sample from the larger groups of projects, the mean project characteristics are virtually the same as the full set even though the rate of project success is only 50%. This is because projects were selected to ensure relatively equal numbers of low- and highrated projects.

The analytic approach used to identify significant predictors of project performance is a straightforward regression framework given by the following, i 0 i i i Where _Y_ is the rating of project _i_ YY approved in year ii = γγ + γγXXXX ii _t_ + γγand country ZZZZ ii + ffEE _c_ , + εε _X_ is a vector of considered predictors ii Where _Y_ is the rating of project _i t c X_ which will vary with the particular analysis, _Z_ is a vector of standard project covariates included in all specifications, and fE is a vector of evaluator fixed effects to control for possible rater bias. The standard set of project covariates ( _Z_ ) includes world region, sector of project, whether the loan is a standard (IBRD) or a concessionary (IDA) loan extended to low-income countries, the original intended size of the project (in 2015 constant dollars), the original intended project length, the country’s CPIA score in the year of approval, and the change in the CPIA score over the project’s lifetime.

The main outcome, the IEG rating, is an ordinal six-point scale and the main analytic results (Tables 4 to 9) are simply estimated from OLS with standard errors clustered at the country level. The results are robust to alternative specification choices such as an ordered probit, a probit with a dichotomized success/failure outcome, whether the observations are weighted by the constant dollar value of the intended loan volume, as well as the missingness of key explanatory variables (most notably the ‘value added’ of the Practice Manager which has a high rate of missingness). Annex 4 presents all alternative specifications for each of the subsequent analytic tables discussed in the next section. The annex also presents all main results stratified by IDA or IBRD project status.

# **4. What do we find?**

We begin with an investigation of the relative importance of project or country characteristics in explaining the observed variation in project ratings. Several measures related to the decomposition of variance in the IEG ratings score are presented in Table 3, namely the mean sum of squares, the F-statistic, and the adjusted R-square. The project characteristics investigated include country and fiscal year indicators, project sector, and various accountings of key project staff identity including the TTL at preparation responsible for project design, the supervisory TTL at the mid-point of the project, the supervisory TTL at the close of the project, both the practice manager and the country director at the start of the project, and the staff responsible for project evaluation after close.

All characteristics investigated have some predictive power for ultimate project success, with the exception of project sector, as determined by the F-statistic. The country of operation itself is relatively influential, with an adjusted R-square of 0.07. Interacting country with fiscal year pushes the R-square to .09. In contrast, when controlling for the identity of key project staff such as the TTL at preparation or the supervisory TTLs at midpoint or endpoint, the adjusted R-squares rise to 0.13-0.14. The most variation is 11