and _Quality of supervision_ jointly convey the evaluated quality of project implementation and monitoring by the World Bank. Importantly, both of these variables capture the services provided by the World Bank to ensure quality at entry of the project and effective implementation through appropriate supervision rather than the public investment management of the implementing country’s authorities. Given that the objective of the PIQ index is to quantify country-specific public investment management quality, we control for these World Bank inputs.

_Agreement type_ reflects the type of legal instrument (signed agreement) associated with the project. It includes the following categories: CARB (Carbon Initiative), GEF (Global Environment Fund), IBRD (International Bank for Reconstruction and Development lending), IDA (International Development Association lending), MONT (Montreal Protocol), RETF (Recipient Executed Trust Fund), and SPF (Special Fund). Most projects in the dataset (86 percent) are IBRD and IDA lending projects. The sector fixed effects control for twelve sectors defined using data from the World Bank Operations Portal and based on the dominant sector of operation in each project.<sup>12</sup> We use Equation 2.1 to build three versions of the public investment quality index: a time-invariant measure (PIQ-F) and two time-variant measures (PIQ-D and PIQ-Q). To build the first measure, we start by estimating Equation 2.1 using all available data. We then recover the regression’s residuals and build the index by computing country averages of the residuals. As mentioned, the idea of the index is to obtain a measure of average country-specific project quality which is orthogonal to project characteristics and World Bank inputs. This procedure is similar to estimating the model with country fixed effects and then using the country fixed effects (which measure country-specific unobserved heterogeneity) as our index of public investment quality. We prefer to use OLS and then average the residuals because this approach is simpler from a computational point of view and reduces potential multicollinearity problems. In any case, fixed effects estimations yield results which are similar to those of the pooled OLS estimates described above (compare columns 1 and 2 of Table 2.1).

Given that our dependent variable is ordinal we also experiment with ordered logit and ordered probit regressions (see columns 5-7 of Table 2.1) and then use the fixed effects or country-level averages of residuals to measure investment quality. Again, the results are similar. However, the non-linear nature of these models and the incidental parameter problem inhibit direct interpretation of the residuals and country fixed effects. Given that our results are robust to different approaches, we use OLS as baseline.

We find that project performance is positively correlated with GDP growth and income per capita (even though income per capita is rarely statistically significant), that larger projects tend to perform better, and so do projects with higher quality at entry and better quality of supervision (see Table 2.1).

To build our baseline time-varying measure of public investment quality (PIQ-D), we estimate Equation 2.1 for rolling 10-year windows over 2000-2021, and then measure average investment

> 12 The sectors are Agriculture; Education; Energy and extractives; Financial sector; Health; Information and Communication Technologies; Industry, trade and services; Public administration; Social protection; Transportation; Water; Other.

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