We use institutional records to identify the preparation TTLs for 3,355 projects, PMs for 3,154 projects and CDs at time of approval for 4,342 projects. The identity of supervision TTLs is already captured in routine reporting systems. The identity of preparation TTLs, PMs, and CDs are scraped from internal records – this process is described in more detail in Annex 2. From HR records we also observe the TTL’s age and education, previous Bank experience, and location (headquarters or a field office). Our measure of staff contribution is a ‘value-added’ measure defined as the difference between the predicted performance of the individual’s previous projects, based on national and fixed project factors, and the realized performance.<sup>8</sup> While we have collected data on projects evaluated by the World Bank’s IEG since the late 1990s up through the end of Fiscal Year 2017 (i.e. June 2017), we focus the analysis on two sub-samples. The main data set concentrates on 2,845 projects approved between Fiscal Years 1995 and 2009.<sup>9</sup> This date range is strategically chosen for two reasons: (a) by starting with FY1995 projects, the missingness of novel project input measures is minimized as missing values are more prevalent in projects that were approved before 1995, and (b) by limiting the start date to before 2009 minimizes data censoring, as many projects approved after 2009 have yet to close and be rated.

A second contribution of this paper is to test the influence of various aspects of project design and implementation on project outcomes. We do so in part with a stratified random sample of 120 projects from the full data set. This smaller data set, which we term the qualitative data set for convenience, supplements the existing measures with qualitative assessments of project characteristics, extracted from primary documents such as Project Appraisal Documents (PADs), Implementation Completion and Results Reports (ICRs) and IEG ICR Reviews (ICRRs). The qualitative data variables include an expert panel assessment of the quality of Project Development Objectives (PDOs) and results frameworks. They also include data on restructurings and a set of contingent indicators such as the number of PDO changes and closing date extensions for those projects that underwent restructuring – i.e. factors that are countable but not counted in existing administrative data. We complement this with a review of the ICRRs from outlier projects – defined either as those with highly satisfactory outcome ratings in low capacity environments (‘positive outliers’) or highly unsatisfactory ratings in high-capacity environments (‘negative outliers’).

The project success ratings themselves fall on a six-point scale ranging from 1 (highly unsatisfactory) to 6 (highly satisfactory). For a binary designation of satisfactory project, attaining a cut-off of 4 or more is required (72% of projects in our data were rated as ‘satisfactory’). There is a tendency for IEG to downgrade ratings assigned by project management and, in the case of PPARs, its own reviewers. Of the 1,968 projects for which we have final ICR and ICRR ratings, 628 were downgraded from the ICR and 54 upgraded. Of the 563 projects for which we have PPARs, 159 were downgraded and 54 upgraded. As most downgrades are from satisfactory to moderately satisfactory and from moderately satisfactory to

> 8 This measure is distinct from the staff quality measure in Denizer et al. (2013), who adopt the average outcome of all other observed projects of the TTL as their quality measure. Our quality measure is somewhat more closely related to the one in Limodio (2021), although our measure utilizes additional project-level information in the value-added calculation, and is discussed further in the next section.

> 9 This set of projects excludes 29 regional (multi-country) projects due to the inferential challenges of work with operations in multiple countries. Annex 2 Table A2.1 lists the total number of projects by fiscal year along with the number of projects included in the main analytic data set.

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