quality for the period under consideration using the same procedure described above. For instance, we estimate the model over 2000-2009 to build the index for 2009, use data for 2001-2010 to build the index for 2010, and so on.<sup>13</sup> Note thus that the 2009 index should be interpreted as reflecting average investment quality over the period 2000-2009 and the 2010 index should be interpreted as measuring average investment quality over 2001-2010. There is thus an overlap between the values for 2009 and 2010, and the index behaves like a moving average.

There are two reasons why we use this approach. First, all institutional variables tend to move slowly, and investors interested in assessing sovereign risk will focus on recent past performance. In this sense, the moving average index that we use in our analysis is an appropriate measure of how investors are likely to assess investment quality in each country. Second, year-by-year estimates are unstable because our database only includes an average of 4.5 completed projects for each country-year (the average drops to 3.6 if we exclude China and India) and there are countryyears with just one project or no projects at all. In fact, in building our PIQ-D index we only consider country-periods which have at least eight projects.<sup>14</sup> Our results are robust to using an alternative time-variant measure built by following the steps described above but by using 5-year windows (PIQ-Q). Given that the PIQ-Q index is based on a relatively small number of projects (on average, less than 20 for each 5-year period-country), we only use it to test whether our results are robust to using non-overlapping estimation periods.

In a battery of robustness checks, we also experiment with a measure of investment quality that controls for project manager characteristics. We do this by using two approaches: (i) by including a TTL dummy variable and (ii) by controlling for TTL characteristics, including years of experience and educational attainment.<sup>15</sup> While controlling for TTL characteristics leads to a substantial loss of observations, we find that the baseline PIQ index is highly correlated with the version built using regressions with TTL fixed effects or characteristics (see Annex Table A1 for estimation results and Annex Figure A1 for the correlation between the two indexes).

One possible caveat with our index is that it focuses on World Bank-financed investment projects. There are two potential issues here.

First, it is likely that World Bank monitoring improves the performance of public investment. However, this should not be an issue because we build our index by holding constant World Bank inputs and focus on a residual element which is associated with country-specific project performance. As long as the influence of World Bank monitoring is uncorrelated with countryspecific variables which affect public investment performance, our index remains a valid measure of country-specific public investment quality.

> 13 Note that we only retain country-years with at least 8 observations of project outcomes in a given period for robustness.

> 14 This seems the optimal threshold given the tradeoff between the number of country-years for which the index can be estimated, and the stability of the index based on a sensitivity analysis with alternative thresholds. 15 Project data on TTLs are obtained from the WB Operations Portal and from Limodio (2021) and Bulman et al. (2015). In all cases, we remove identifying information and numerically code TTL names.

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