evaluations dataset with additional project characteristics from the World Bank’s Projects and Operations portal data. The resulting dataset includes 4,671 projects covering 146 countries over 2000-2021.

Previous work that used World Bank project data to examine the determinants of project outcomes, includes Ashton et al. (2021), Bulman et al. (2017), Chauvet et al. (2010), Denizer et al. (2013), and Dreher et al. (2013). We build on their work to quantify cross-country heterogeneity in project outcomes while controlling for project-level characteristics. Our aim in this exercise is not to estimate the drivers of project outcomes. Instead, we control for project-specific, sector-specific, and World Bank intervention-induced factors to obtain a residual term which captures countrylevel factors that are determinants of project outcomes. Formally, we start by estimating the following model: where PSi,s,c,t is the numeric project outcome score (ranging from 1 to 6) for project i in sector s, country c, and year t; Xi,c,s,t is a matrix of project-specific characteristics; yc,t is real per capita GDP, gc,t is real GDP growth in country c, year t (we include these variables to control for the — level of economic development and the business cycle effects the results are robust to estimating the model without yc,t and gc,t), μs and τt are sector and year fixed effects, and εi,s,c,t is the error term.

The matrix of project-specific characteristics includes _Project length, Project volume_ , _Agreement type_ , _Quality at entry_ , and _Quality of supervision._<sup>_11_</sup> _Project length_ measures the actual duration of the project and ranges between 1 and 20 years, with an average value of 7.6 years. _Project volume_ is a categorical variable aimed at measuring the total cost of each project. It includes five categories ranging from less than 10 million USD to over 100 million USD (in the regression, we include a dummy for each of these five categories).

_Quality at entry_ and _Quality of supervision_ control for the influence of project-specific monitoring and supervision by the World Bank. The first variable is a numeric six-point indicator ranging from 1 = “Highly unsatisfactory” to 6 = “Highly satisfactory” that reflects the World Bank staff's assessment of the likelihood that the operation would achieve planned development outcomes and align with the Bank's fiduciary responsibilities. The second variable is also a six-point score and reflects the extent to which World Bank staff proactively identified and addressed threats to achieving development outcomes and fulfilling the Bank's fiduciary role. Thus, _Quality at entry_ infrastructure, de facto many of them also envision a mix of spending both on gross fixed capital formation and current expenditures such as technical assistance, reforms, etc. (not necessarily directly related to fixed investment). Therefore, as one of the robustness exercises, we also examine the index based only on IPF projects of the Infrastructure WB Practice Group. However, this significantly limits the project sample (956 projects). Since the version of the index based only on this infrastructure category and our baseline PIQ index that is based on all IPF projects are highly correlated, we proceed with the more robust baseline PIQ index.

> 11 We also conduct robustness checks where we also control for project manager (task team leader) effects by including dummy variables or, alternatively, continuous variables measuring work experience and educational attainment of project managers.

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