This paper does not find evidence that a country’s administrative capacity at the time of a project’s approval predicts PSM project performance. While this may seem surprising, it is likely explained by two factors. First, it may simply mean that TTLs realistically adjust the ambitiousness of their projects’ objectives to the level of administrative ability they find at baseline. If this is the case, PSM projects in low-capacity environments would be equally likely to perform well as PSM projects in high-capacity environments. Second, the most reliable comparative data source on administrative capacity—the CPIA scores—is only available for a small share of the projects in the sample, making it more difficult to find statistically significant correlations.

A second key finding relates to the Bank’s approach to managing risk in PSM operations. The review finds that subjective risk ratings provided by TTLs and the World Bank’s overall portfolio performance in a given country are the most telling early risk indicators for PSM projects. By contrast, the set of 13 standardized risk flags utilized in ISRs does little to predict PSM project performance. This suggests that encouraging an open dialogue about risk between TTLs and management should be core to the Bank’s risk management strategy going forward.

Finally, the paper finds that performance differences between different PSM reform content areas and the average Bank project are to a significant extent driven by the particular country contexts to which PSM projects are targeted. When comparing PSM projects with non-PSM projects in the same country and approved at a similar time, performance differences between PSM projects and non-PSM projects largely disappear. Performance differences between “low-risk” PSM reform content areas (such as taxes) and “high-risk” areas (PFM and civil service reform) shrink and become statistically insignificant, when controlling for country context. This suggests that a significant part of the “risk” of PSM projects is driven by location, rather than being inherent to what they attempt to achieve.

It is important to note that key factors that theoretically matter to PSM project performance remain omitted in this paper, due to data constraints—such as the immediate institutional context of the implementing client MDA. Such omitted variables pose a significant risk of bias, calling for caution in causally interpreting this review’s findings. Overall, controlling for context and project covariates that are observable during the first half of project implementation improves the percentage of correctly predicted IEG outcome ratings for PSM projects from 71 to 77 percent, or reduces proportional prediction error by 21 percent. Adjusted and pseudo-R<sup>2</sup> suggest that these covariates can explain about 19 percent of IEG outcome ratings variation. That is not a negligible share, but clearly highlights that key predictors remain unobserved—predictors that account for the remaining 81 percent of variation.

Clearly, this finding to some extent reflects the inherent limits of quantitative cross-country regression research on public administration reform; many factors relevant to PSM reform success will always 40